Add eight RoboLab harness evaluation log snapshots

.gitattributes

@@ -283,3 +283,225 @@ BananaInBowlTask/run_0.hdf5 filter=lfs diff=lfs merge=lfs -text
 2026-09-30_14-45-15_harness/FoodPacking3BoxesTask/run_7.hdf5 filter=lfs diff=lfs merge=lfs -text
 2026-09-30_14-45-15_harness/FoodPacking3BoxesTask/run_8.hdf5 filter=lfs diff=lfs merge=lfs -text
 2026-09-30_14-45-15_harness/FoodPacking3BoxesTask/run_9.hdf5 filter=lfs diff=lfs merge=lfs -text
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+2026-10-01T06:04:49.356338Z ERROR codex_core::tools::router: error=unable to locate image at `/home/myx/codebase/outputs/harness/20261001T054755.827592Z_eval/episode-0001/runtime_5ailebky/workspace/exterior.png`: No such file or directory (os error 2)

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+2026-10-01T06:53:12.647591Z ERROR codex_core::tools::router: error=unable to locate image at `/home/myx/codebase/outputs/harness/20261001T054755.827592Z_eval/episode-0002/runtime_nzwibs51/workspace/wrist_zoom.png`: No such file or directory (os error 2)

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20261001T054755.827592Z_eval/episode-0011/runtime_uy_v15n7/codex/config.toml

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+Apache License
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+http://www.apache.org/licenses/
+
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20261001T054755.827592Z_eval/episode-0011/runtime_uy_v15n7/codex/skills/.system/imagegen/SKILL.md

@@ -0,0 +1,315 @@
+---
+name: "imagegen"
+description: "Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas."
+---
+
+# Image Generation Skill
+
+Generates or edits images for the current project (for example website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, or infographics).
+
+## Top-level modes and rules
+
+This skill has exactly two top-level modes:
+
+- **Default built-in tool mode (preferred):** built-in `image_gen` tool for image generation, editing, and transparent-image requests. Does not require `OPENAI_API_KEY`.
+- **Fallback CLI mode:** `scripts/image_gen.py` CLI. Use when the user explicitly asks for or confirms the CLI/API/model path. Requires `OPENAI_API_KEY`.
+
+Within CLI fallback, the CLI exposes three subcommands:
+
+- `generate`
+- `edit`
+- `generate-batch`
+
+Rules:
+- Use the built-in `image_gen` tool by default for normal image generation and editing requests.
+- Do not switch to CLI fallback for ordinary quality, size, or file-path control.
+- For transparent images, ask built-in `image_gen` for a transparent background and preserve the generated alpha.
+- Never silently switch from built-in `image_gen` or CLI `gpt-image-2` to CLI `gpt-image-1.5`; ask the user first unless they explicitly requested `gpt-image-1.5`.
+- The word `batch` by itself does not mean CLI fallback. If the user asks for many assets or says to batch-generate assets without explicitly asking for CLI/API/model controls, stay on the built-in path and issue one built-in call per requested asset or variant.
+- If the built-in tool fails or is unavailable, tell the user the CLI fallback exists and that it requires `OPENAI_API_KEY`. Proceed only if the user explicitly asks for that fallback.
+- If the user explicitly asks for CLI mode, use the bundled `scripts/image_gen.py` workflow. Do not create one-off SDK runners.
+- Never modify `scripts/image_gen.py`. If something is missing, ask the user before doing anything else.
+
+Built-in save-path policy:
+- In built-in tool mode, Codex saves generated images under `$CODEX_HOME/*` by default.
+- Do not describe or rely on OS temp as the default built-in destination.
+- Do not describe or rely on a destination-path argument (if any) on the built-in `image_gen` tool. If a specific location is needed, generate first and then move or copy the selected output from `$CODEX_HOME/generated_images/...`.
+- Save-path precedence in built-in mode:
+  1. If the user names a destination, move or copy the selected output there.
+  2. If the image is meant for the current project, move or copy the final selected image into the workspace before finishing.
+  3. If the image is only for preview or brainstorming, render it inline; the underlying file can remain at the default `$CODEX_HOME/*` path.
+- Never leave a project-referenced asset only at the default `$CODEX_HOME/*` path.
+- Do not overwrite an existing asset unless the user explicitly asked for replacement; otherwise create a sibling versioned filename such as `hero-v2.png` or `item-icon-edited.png`.
+
+Shared prompt guidance for both modes lives in `references/prompting.md` and `references/sample-prompts.md`.
+
+Fallback-only docs/resources for CLI mode:
+- `references/cli.md`
+- `references/image-api.md`
+- `references/codex-network.md`
+- `scripts/image_gen.py`
+
+## When to use
+- Generate a new image (concept art, product shot, cover, website hero)
+- Generate a new image using one or more reference images for style, composition, or mood
+- Edit an existing image (inpainting, lighting or weather transformations, background replacement, object removal, compositing, transparent background)
+- Produce many assets or variants for one task
+
+## When not to use
+- Extending or matching an existing SVG/vector icon set, logo system, or illustration library inside the repo
+- Creating simple shapes, diagrams, wireframes, or icons that are better produced directly in SVG, HTML/CSS, or canvas
+- Making a small project-local asset edit when the source file already exists in an editable native format
+- Any task where the user clearly wants deterministic code-native output instead of a generated bitmap
+
+## Decision tree
+
+Think about two separate questions:
+
+1. **Intent:** is this a new image or an edit of an existing image?
+2. **Execution strategy:** is this one asset or many assets/variants?
+
+Intent:
+- If the user wants to modify an existing image while preserving parts of it, treat the request as **edit**.
+- If the user provides images only as references for style, composition, mood, or subject guidance, treat the request as **generate**.
+- If the user provides no images, treat the request as **generate**.
+
+Built-in edit semantics:
+- Built-in edit mode is for images already visible in the conversation context, such as attached images or images generated earlier in the thread.
+- If the user wants to edit a local image file with the built-in tool, first load it with built-in `view_image` tool so the image is visible in the conversation context, then proceed with the built-in edit flow.
+- Do not promise arbitrary filesystem-path editing through the built-in tool.
+- If a local file still needs direct file-path control, masks, or other explicit CLI-only parameters, use the explicit CLI fallback only when the user asks for it.
+- For edits, preserve invariants aggressively and save non-destructively by default.
+
+Execution strategy:
+- In the built-in default path, produce many assets or variants by issuing one `image_gen` call per requested asset or variant.
+- In the CLI fallback path, use the CLI `generate-batch` subcommand only when the user explicitly chose CLI mode and needs many prompts/assets.
+- For many distinct assets, do not use `n` as a substitute for separate prompts. `n` is for variants of one prompt; distinct assets need distinct built-in calls or distinct CLI `generate-batch` jobs.
+
+Assume the user wants a new image unless they clearly ask to change an existing one.
+
+## Workflow
+1. Decide the top-level mode: built-in by default, including transparent-output requests; fallback CLI only if explicitly requested or confirmed.
+2. Decide the intent: `generate` or `edit`.
+3. Decide whether the output is preview-only or meant to be consumed by the current project.
+4. Decide the execution strategy: single asset vs repeated built-in calls vs CLI `generate-batch`.
+5. Collect inputs up front: prompt(s), exact text (verbatim), constraints/avoid list, and any input images.
+6. For every input image, label its role explicitly:
+   - reference image
+   - edit target
+   - supporting insert/style/compositing input
+7. If the edit target is only on the local filesystem and you are staying on the built-in path, inspect it with `view_image` first so the image is available in conversation context.
+8. If the user asked for a photo, illustration, sprite, product image, banner, or other explicitly raster-style asset, use `image_gen` rather than substituting SVG/HTML/CSS placeholders. If the request is for an icon, logo, or UI graphic that should match existing repo-native SVG/vector/code assets, prefer editing those directly instead.
+9. Augment the prompt based on specificity:
+   - If the user's prompt is already specific and detailed, normalize it into a clear spec without adding creative requirements.
+   - If the user's prompt is generic, add tasteful augmentation only when it materially improves output quality.
+10. Use the built-in `image_gen` tool by default.
+11. For transparent-output requests, ask built-in `image_gen` for a transparent background and preserve the generated alpha channel.
+12. Inspect outputs and validate: subject, style, composition, text accuracy, and invariants/avoid items.
+13. Iterate with a single targeted change, then re-check.
+14. For preview-only work, render the image inline; the underlying file may remain at the default `$CODEX_HOME/generated_images/...` path.
+15. For project-bound work, move or copy the selected artifact into the workspace and update any consuming code or references. Never leave a project-referenced asset only at the default `$CODEX_HOME/generated_images/...` path.
+16. For batches or multi-asset requests, persist every requested deliverable final in the workspace unless the user explicitly asked to keep outputs preview-only. Discarded variants do not need to be kept unless requested.
+17. If the user explicitly chooses or confirms the CLI fallback, then use the fallback-only docs for model, quality, size, `input_fidelity`, masks, output format, output paths, and network setup.
+18. Always report the final saved path(s) for any workspace-bound asset(s), plus the final prompt or prompt set and whether the built-in tool or fallback CLI mode was used.
+
+## Transparent image requests
+
+Ask built-in `image_gen` for a genuinely transparent background and preserve its alpha.
+
+## Prompt augmentation
+
+Reformat user prompts into a structured, production-oriented spec. Make the user's goal clearer and more actionable, but do not blindly add detail.
+
+Treat this as prompt-shaping guidance, not a closed schema. Use only the lines that help, and add a short extra labeled line when it materially improves clarity.
+
+### Specificity policy
+
+Use the user's prompt specificity to decide how much augmentation is appropriate:
+
+- If the prompt is already specific and detailed, preserve that specificity and only normalize/structure it.
+- If the prompt is generic, you may add tasteful augmentation when it will materially improve the result.
+
+Allowed augmentations:
+- composition or framing hints
+- polish level or intended-use hints
+- practical layout guidance
+- reasonable scene concreteness that supports the stated request
+
+Not allowed augmentations:
+- extra characters or objects that are not implied by the request
+- brand names, slogans, palettes, or narrative beats that are not implied
+- arbitrary side-specific placement unless the surrounding layout supports it
+
+## Use-case taxonomy (exact slugs)
+
+Classify each request into one of these buckets and keep the slug consistent across prompts and references.
+
+Generate:
+- photorealistic-natural — candid/editorial lifestyle scenes with real texture and natural lighting.
+- product-mockup — product/packaging shots, catalog imagery, merch concepts.
+- ui-mockup — app/web interface mockups and wireframes; specify the desired fidelity.
+- infographic-diagram — diagrams/infographics with structured layout and text.
+- scientific-educational — classroom explainers, scientific diagrams, and learning visuals with required labels and accuracy constraints.
+- ads-marketing — campaign concepts and ad creatives with audience, brand position, scene, and exact tagline/copy.
+- productivity-visual — slide, chart, workflow, and data-heavy business visuals.
+- logo-brand — logo/mark exploration, vector-friendly.
+- illustration-story — comics, children’s book art, narrative scenes.
+- stylized-concept — style-driven concept art, 3D/stylized renders.
+- historical-scene — period-accurate/world-knowledge scenes.
+
+Edit:
+- text-localization — translate/replace in-image text, preserve layout.
+- identity-preserve — try-on, person-in-scene; lock face/body/pose.
+- precise-object-edit — remove/replace a specific element (including interior swaps).
+- lighting-weather — time-of-day/season/atmosphere changes only.
+- background-extraction — transparent background / clean cutout. Ask built-in `image_gen` for actual transparency.
+- style-transfer — apply reference style while changing subject/scene.
+- compositing — multi-image insert/merge with matched lighting/perspective.
+- sketch-to-render — drawing/line art to photoreal render.
+
+## Shared prompt schema
+
+Use the following labeled spec as shared prompt scaffolding for both top-level modes:
+
+```text
+Use case: <taxonomy slug>
+Asset type: <where the asset will be used>
+Primary request: <user's main prompt>
+Input images: <Image 1: role; Image 2: role> (optional)
+Scene/backdrop: <environment>
+Subject: <main subject>
+Style/medium: <photo/illustration/3D/etc>
+Composition/framing: <wide/close/top-down; placement>
+Lighting/mood: <lighting + mood>
+Color palette: <palette notes>
+Materials/textures: <surface details>
+Text (verbatim): "<exact text>"
+Constraints: <must keep/must avoid>
+Avoid: <negative constraints>
+```
+
+Notes:
+- `Asset type` and `Input images` are prompt scaffolding, not dedicated CLI flags.
+- `Scene/backdrop` refers to the visual setting. It is not the same as the fallback CLI `background` parameter, which controls output transparency behavior.
+- Fallback-only execution notes such as `Quality:`, `Input fidelity:`, masks, output format, and output paths belong in the CLI path only. Do not treat them as built-in `image_gen` tool arguments.
+
+Augmentation rules:
+- Keep it short.
+- Add only the details needed to improve the prompt materially.
+- For edits, explicitly list invariants (`change only X; keep Y unchanged`).
+- If any critical detail is missing and blocks success, ask a question; otherwise proceed.
+
+## Examples
+
+### Generation example (hero image)
+```text
+Use case: product-mockup
+Asset type: landing page hero
+Primary request: a minimal hero image of a ceramic coffee mug
+Style/medium: clean product photography
+Composition/framing: wide composition with usable negative space for page copy if needed
+Lighting/mood: soft studio lighting
+Constraints: no logos, no text, no watermark
+```
+
+### Edit example (invariants)
+```text
+Use case: precise-object-edit
+Asset type: product photo background replacement
+Primary request: replace only the background with a warm sunset gradient
+Constraints: change only the background; keep the product and its edges unchanged; no text; no watermark
+```
+
+## Prompting best practices
+- Structure prompt as scene/backdrop -> subject -> details -> constraints.
+- Include intended use (ad, UI mock, infographic) to set the mode and polish level.
+- Use camera/composition language for photorealism.
+- Only use SVG/vector stand-ins when the user explicitly asked for vector output or a non-image placeholder.
+- Quote exact text and specify typography + placement.
+- For tricky words, spell them letter-by-letter and require verbatim rendering.
+- For multi-image inputs, reference images by index and describe how they should be used.
+- For edits, repeat invariants every iteration to reduce drift.
+- Iterate with single-change follow-ups.
+- If the prompt is generic, add only the extra detail that will materially help.
+- If the prompt is already detailed, normalize it instead of expanding it.
+- For CLI fallback only, see `references/cli.md` and `references/image-api.md` for model, `quality`, `input_fidelity`, masks, output format, and output-path guidance.
+- For transparent images, ask built-in `image_gen` for actual transparency and preserve its alpha.
+
+More principles shared by both modes: `references/prompting.md`.
+Copy/paste specs shared by both modes: `references/sample-prompts.md`.
+
+## Guidance by asset type
+Asset-type templates (website assets, game assets, wireframes, logo) are consolidated in `references/sample-prompts.md`.
+
+## gpt-image-2 guidance for CLI fallback
+
+The fallback CLI defaults to `gpt-image-2`.
+
+- Use `gpt-image-2` for new CLI/API workflows unless the user confirms a different model.
+- CLI `gpt-image-2` does not support `background=transparent`; ask before using `gpt-image-1.5` unless the user explicitly requested that model.
+- `gpt-image-2` always uses high fidelity for image inputs; do not set `input_fidelity` with this model.
+- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.
+- Use `quality low` for fast drafts, thumbnails, and quick iterations. Use `medium`, `high`, or `auto` for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.
+- Square images are typically fastest to generate. Use `1024x1024` for fast square drafts.
+- If the user asks for 4K-style output, use `3840x2160` for landscape or `2160x3840` for portrait.
+- `gpt-image-2` size may be `auto` or `WIDTHxHEIGHT` if all constraints hold: max edge `<= 3840px`, both edges multiples of `16px`, long-to-short ratio `<= 3:1`, total pixels between `655,360` and `8,294,400`.
+
+Popular `gpt-image-2` sizes:
+- `1024x1024` square
+- `1536x1024` landscape
+- `1024x1536` portrait
+- `2048x2048` 2K square
+- `2048x1152` 2K landscape
+- `3840x2160` 4K landscape
+- `2160x3840` 4K portrait
+- `auto`
+
+## Fallback CLI mode only
+
+### Temp and output conventions
+These conventions apply only to the CLI fallback. They do not describe built-in `image_gen` output behavior.
+- Use `tmp/imagegen/` for intermediate files (for example JSONL batches); delete them when done.
+- Write final artifacts under `output/imagegen/`.
+- Use `--out` or `--out-dir` to control output paths; keep filenames stable and descriptive.
+
+### Dependencies
+Prefer `uv` for dependency management in this repo.
+
+Required Python package:
+```bash
+uv pip install openai
+```
+
+Optional for image inspection and downscaling:
+```bash
+uv pip install pillow
+```
+
+Portability note:
+- If you are using the installed skill outside this repo, install dependencies into that environment with its package manager.
+- In uv-managed environments, `uv pip install ...` remains the preferred path.
+
+### Environment
+- `OPENAI_API_KEY` must be set for live API calls.
+- Do not ask the user for `OPENAI_API_KEY` when using the built-in `image_gen` tool.
+- Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.
+
+If the key is missing, give the user these steps:
+1. Create an API key in the OpenAI platform UI: https://platform.openai.com/api-keys
+2. Set `OPENAI_API_KEY` as an environment variable in their system.
+3. Offer to guide them through setting the environment variable for their OS/shell if needed.
+
+If installation is not possible in this environment, tell the user which dependency is missing and how to install it into their active environment.
+
+### Script-mode notes
+- CLI commands + examples: `references/cli.md`
+- API parameter quick reference: `references/image-api.md`
+- Network approvals / sandbox settings for CLI mode: `references/codex-network.md`
+
+## Reference map
+- `references/prompting.md`: shared prompting principles for both modes.
+- `references/sample-prompts.md`: shared copy/paste prompt recipes for both modes.
+- `references/cli.md`: fallback-only CLI usage via `scripts/image_gen.py`.
+- `references/image-api.md`: fallback-only API/CLI parameter reference.
+- `references/codex-network.md`: fallback-only network/sandbox troubleshooting for CLI mode.
+- `scripts/image_gen.py`: fallback-only CLI implementation. Use only when the user explicitly chooses or confirms CLI mode.

20261001T054755.827592Z_eval/episode-0011/runtime_uy_v15n7/codex/skills/.system/imagegen/agents/openai.yaml

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+interface:
+  display_name: "Image Gen"
+  short_description: "Generate or edit images for websites, games, and more"
+  icon_small: "./assets/imagegen-small.svg"
+  icon_large: "./assets/imagegen.png"
+  default_prompt: "Use $imagegen to make or edit an image for this project."

20261001T054755.827592Z_eval/episode-0011/runtime_uy_v15n7/codex/skills/.system/imagegen/references/cli.md

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+# CLI reference (`scripts/image_gen.py`)
+
+This file is for the fallback CLI mode only. Read it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the `gpt-image-1.5` true-transparency fallback path.
+
+`generate-batch` is a CLI subcommand in this fallback path. It is not a top-level mode of the skill.
+The word `batch` in a user request is not CLI opt-in by itself.
+
+## What this CLI does
+- `generate`: generate a new image from a prompt
+- `edit`: edit one or more existing images
+- `generate-batch`: run many generation jobs from a JSONL file after the user explicitly chooses CLI/API/model controls
+
+Real API calls require **network access** + `OPENAI_API_KEY`. `--dry-run` does not.
+
+## Quick start (works from any repo)
+Set a stable path to the skill CLI (default `CODEX_HOME` is `~/.codex`):
+
+```
+export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
+export IMAGE_GEN="$CODEX_HOME/skills/.system/imagegen/scripts/image_gen.py"
+```
+
+Install dependencies into that environment with its package manager. In uv-managed environments, `uv pip install ...` remains the preferred path.
+
+## Quick start
+
+Dry-run (no API call; no network required; does not require the `openai` package):
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "Test" \
+  --out output/imagegen/test.png \
+  --dry-run
+```
+
+Notes:
+- One-off dry-runs print the API payload and the computed output path(s).
+- Repo-local finals should live under `output/imagegen/`.
+
+Generate (requires `OPENAI_API_KEY` + network):
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A cozy alpine cabin at dawn" \
+  --size 1024x1024 \
+  --out output/imagegen/alpine-cabin.png
+```
+
+Edit:
+
+```bash
+python "$IMAGE_GEN" edit \
+  --image input.png \
+  --prompt "Replace only the background with a warm sunset" \
+  --out output/imagegen/sunset-edit.png
+```
+
+## Guardrails
+- Use the bundled CLI directly (`python "$IMAGE_GEN" ...`) after activating the correct environment.
+- Do **not** create one-off runners (for example `gen_images.py`) unless the user explicitly asks for a custom wrapper.
+- **Never modify** `scripts/image_gen.py`. If something is missing, ask the user before doing anything else.
+- Do not silently downgrade from CLI `gpt-image-2` or built-in `image_gen` to CLI `gpt-image-1.5`; ask first unless the user explicitly requested `gpt-image-1.5`.
+
+## Defaults
+- Model: `gpt-image-2`
+- Supported model family for this CLI: GPT Image models (`gpt-image-*`)
+- Size: `auto`
+- Quality: `medium`
+- Output format: `png`
+- Default one-off output path: `output/imagegen/output.png`
+- Background: unspecified unless `--background` is set
+
+## gpt-image-2 size and model guidance
+
+`gpt-image-2` is the default model for new CLI fallback work.
+
+- Use `--quality low` for fast drafts, thumbnails, and quick iterations.
+- Use `--quality medium`, `--quality high`, or `--quality auto` for final assets, dense text, diagrams, identity-sensitive edits, and high-resolution outputs.
+- Square images are typically fastest. Use `--size 1024x1024` for quick square drafts.
+- If the user asks for 4K-style output, use `--size 3840x2160` for landscape or `--size 2160x3840` for portrait.
+- Do not pass `--input-fidelity` with `gpt-image-2`; this model always uses high fidelity for image inputs.
+- Do not use `--background transparent` with CLI `gpt-image-2`; ask before using `gpt-image-1.5` unless the user explicitly requested that model.
+
+Popular `gpt-image-2` sizes:
+- `1024x1024`
+- `1536x1024`
+- `1024x1536`
+- `2048x2048`
+- `2048x1152`
+- `3840x2160`
+- `2160x3840`
+- `auto`
+
+`gpt-image-2` size constraints:
+- max edge `<= 3840px`
+- both edges multiples of `16px`
+- long edge to short edge ratio `<= 3:1`
+- total pixels between `655,360` and `8,294,400`
+- outputs above `2560x1440` total pixels are experimental
+
+Fast draft:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A product thumbnail of a matte ceramic mug on a stone surface" \
+  --quality low \
+  --size 1024x1024 \
+  --out output/imagegen/mug-draft.png
+```
+
+Final 2K landscape:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A polished landing-page hero image of a matte ceramic mug on a stone surface" \
+  --quality high \
+  --size 2048x1152 \
+  --out output/imagegen/mug-hero.png
+```
+
+4K landscape:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A detailed architectural visualization at golden hour" \
+  --size 3840x2160 \
+  --quality high \
+  --out output/imagegen/architecture-4k.png
+```
+
+True transparent fallback request:
+
+Ask for confirmation before using this command unless the user explicitly requested `gpt-image-1.5`.
+
+```bash
+python "$IMAGE_GEN" generate \
+  --model gpt-image-1.5 \
+  --prompt "A clean product cutout on a transparent background" \
+  --background transparent \
+  --output-format png \
+  --out output/imagegen/product-cutout.png
+```
+
+Explain that CLI `gpt-image-2` does not support `background=transparent`, so transparent CLI output requires the confirmed `gpt-image-1.5` fallback.
+
+## Quality, input fidelity, and masks (CLI fallback only)
+These are explicit CLI controls. They are not built-in `image_gen` tool arguments.
+
+- `--quality` works for `generate`, `edit`, and `generate-batch`: `low|medium|high|auto`
+- `--input-fidelity` is **edit-only** and validated as `low|high`; it is not supported for `gpt-image-2`
+- `--mask` is **edit-only**
+
+Example:
+
+```bash
+python "$IMAGE_GEN" edit \
+  --model gpt-image-1.5 \
+  --image input.png \
+  --prompt "Change only the background" \
+  --quality high \
+  --input-fidelity high \
+  --out output/imagegen/background-edit.png
+```
+
+Mask notes:
+- For multi-image edits, pass repeated `--image` flags. Their order is meaningful, so describe each image by index and role in the prompt.
+- The CLI accepts a single `--mask`.
+- Image and mask must be the same size and format and each under 50MB.
+- Masks must include an alpha channel.
+- If multiple input images are provided, the mask applies to the first image.
+- Masking is prompt-guided; do not promise exact pixel-perfect mask boundaries.
+- Use a PNG mask when possible; the script treats mask handling as best-effort and does not perform full preflight validation beyond file checks/warnings.
+- In the edit prompt, repeat invariants (`change only the background; keep the subject unchanged`) to reduce drift.
+
+## Output handling
+- Use `tmp/imagegen/` for temporary JSONL inputs or scratch files.
+- Use `output/imagegen/` for final outputs.
+- Reruns fail if a target file already exists unless you pass `--force`.
+- `--out-dir` changes one-off naming to `image_1.<ext>`, `image_2.<ext>`, and so on.
+- Downscaled copies use the default suffix `-web` unless you override it.
+
+## Common recipes
+
+Generate with augmentation fields:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A minimal hero image of a ceramic coffee mug" \
+  --use-case "product-mockup" \
+  --style "clean product photography" \
+  --composition "wide product shot with usable negative space for page copy" \
+  --constraints "no logos, no text" \
+  --out output/imagegen/mug-hero.png
+```
+
+Generate + also write a downscaled copy for fast web loading:
+
+```bash
+python "$IMAGE_GEN" generate \
+  --prompt "A cozy alpine cabin at dawn" \
+  --size 1024x1024 \
+  --downscale-max-dim 1024 \
+  --out output/imagegen/alpine-cabin.png
+```
+
+Generate multiple prompts concurrently (async batch):
+
+```bash
+mkdir -p tmp/imagegen output/imagegen/batch
+cat > tmp/imagegen/prompts.jsonl << 'EOF'
+{"prompt":"Cavernous hangar interior with a compact shuttle parked near the center","use_case":"stylized-concept","composition":"wide-angle, low-angle","lighting":"volumetric light rays through drifting fog","constraints":"no logos or trademarks; no watermark","size":"1536x1024"}
+{"prompt":"Gray wolf in profile in a snowy forest","use_case":"photorealistic-natural","composition":"eye-level","constraints":"no logos or trademarks; no watermark","size":"1024x1024"}
+EOF
+
+python "$IMAGE_GEN" generate-batch \
+  --input tmp/imagegen/prompts.jsonl \
+  --out-dir output/imagegen/batch \
+  --concurrency 5
+
+rm -f tmp/imagegen/prompts.jsonl
+```
+
+Notes:
+- `generate-batch` requires `--out-dir`.
+- generate-batch requires --out-dir.
+- Use `--concurrency` to control parallelism (default `5`).
+- Per-job overrides are supported in JSONL (for example `size`, `quality`, `background`, `output_format`, `output_compression`, `moderation`, `n`, `model`, `out`, and prompt-augmentation fields).
+- `--n` generates multiple variants for a single prompt; `generate-batch` is for many different prompts.
+- In batch mode, per-job `out` is treated as a filename under `--out-dir`.
+- For many requested deliverable assets, provide one prompt/job per distinct asset and use semantic filenames when possible.
+
+## CLI notes
+- Supported sizes depend on the model. `gpt-image-2` supports flexible constrained sizes; older GPT Image models support `1024x1024`, `1536x1024`, `1024x1536`, or `auto`.
+- True transparent CLI outputs require `output_format` to be `png` or `webp` and are not supported by `gpt-image-2`.
+- `--prompt-file`, `--output-compression`, `--moderation`, `--max-attempts`, `--fail-fast`, `--force`, and `--no-augment` are supported.
+- This CLI is intended for GPT Image models. Do not assume older non-GPT image-model behavior applies here.
+
+## See also
+- API parameter quick reference for fallback CLI mode: `references/image-api.md`
+- Prompt examples shared across both top-level modes: `references/sample-prompts.md`
+- Network/sandbox notes for fallback CLI mode: `references/codex-network.md`
+- Built-in-first transparent image workflow: `SKILL.md`

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+# Codex network approvals / sandbox notes
+
+This file is for the fallback CLI mode only. Read it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the `gpt-image-1.5` true-transparency fallback path.
+
+This guidance is intentionally isolated from `SKILL.md` because it can vary by environment and may become stale. Prefer the defaults in your environment when in doubt.
+
+## Why am I asked to approve image generation calls?
+The fallback CLI uses the OpenAI Image API, so it needs outbound network access. In many Codex setups, network access is disabled by default and/or the approval policy requires confirmation before networked commands run.
+
+## Important note about approvals vs network
+- `--ask-for-approval never` suppresses approval prompts.
+- It does **not** by itself enable network access.
+- In `workspace-write`, network access still depends on your Codex configuration (for example `[sandbox_workspace_write] network_access = true`).
+
+## How do I reduce repeated approval prompts?
+If you trust the repo and want fewer prompts, use a configuration or profile that both:
+- enables network for the sandbox mode you plan to use
+- sets an approval policy that matches your risk tolerance
+
+Example `~/.codex/config.toml` pattern:
+
+```toml
+approval_policy = "on-request"
+sandbox_mode = "workspace-write"
+
+[sandbox_workspace_write]
+network_access = true
+```
+
+If you want quieter automation after network is enabled, you can choose a stricter approval policy, but do that intentionally and with care.
+
+## Safety note
+Enabling network and reducing approvals lowers friction, but increases risk if you run untrusted code or work in an untrusted repository.

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+# Image API quick reference
+
+This file is for the fallback CLI mode only. Use it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the `gpt-image-1.5` true-transparency fallback path.
+
+These parameters describe the Image API and bundled CLI fallback surface. Do not assume they are normal arguments on the built-in `image_gen` tool.
+
+## Scope
+- This fallback CLI is intended for GPT Image models (`gpt-image-2`, `gpt-image-1.5`, `gpt-image-1`, and `gpt-image-1-mini`).
+- The built-in `image_gen` tool and the fallback CLI do not expose the same controls.
+
+## Model summary
+
+| Model | Quality | Input fidelity | Resolutions | Recommended use |
+| --- | --- | --- | --- | --- |
+| `gpt-image-2` | `low`, `medium`, `high`, `auto` | Always high fidelity for image inputs; do not set `input_fidelity` | `auto` or flexible sizes that satisfy the constraints below | Default for new CLI/API workflows: high-quality generation and editing, text-heavy images, photorealism, compositing, identity-sensitive edits, and workflows where fewer retries matter |
+| `gpt-image-1.5` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | True transparent-background fallback and backward-compatible workflows |
+| `gpt-image-1` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | Legacy compatibility |
+| `gpt-image-1-mini` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | Cost-sensitive draft batches and lower-stakes previews |
+
+## gpt-image-2 sizes
+
+`gpt-image-2` accepts `auto` or any `WIDTHxHEIGHT` size that satisfies all constraints:
+
+- Maximum edge length must be less than or equal to `3840px`.
+- Both edges must be multiples of `16px`.
+- Long edge to short edge ratio must not exceed `3:1`.
+- Total pixels must be at least `655,360` and no more than `8,294,400`.
+
+Popular sizes:
+
+| Label | Size | Notes |
+| --- | --- | --- |
+| Square | `1024x1024` | Typical fast default |
+| Landscape | `1536x1024` | Standard landscape |
+| Portrait | `1024x1536` | Standard portrait |
+| 2K square | `2048x2048` | Larger square output |
+| 2K landscape | `2048x1152` | Widescreen output |
+| 4K landscape | `3840x2160` | Widescreen 4K output |
+| 4K portrait | `2160x3840` | Vertical 4K output |
+| Auto | `auto` | Default size |
+
+Square images are typically fastest to generate. For 4K-style output, use `3840x2160` or `2160x3840`.
+
+## Endpoints
+- Generate: `POST /v1/images/generations` (`client.images.generate(...)`)
+- Edit: `POST /v1/images/edits` (`client.images.edit(...)`)
+
+## Core parameters for GPT Image models
+- `prompt`: text prompt
+- `model`: image model
+- `n`: number of images (1-10)
+- `size`: `auto` by default for `gpt-image-2`; flexible `WIDTHxHEIGHT` sizes are allowed only for `gpt-image-2`; older GPT Image models use `1024x1024`, `1536x1024`, `1024x1536`, or `auto`
+- `quality`: `low`, `medium`, `high`, or `auto`
+- `background`: output transparency behavior (`transparent`, `opaque`, or `auto`) for generated output; this is not the same thing as the prompt's visual scene/backdrop
+- `output_format`: `png` (default), `jpeg`, `webp`
+- `output_compression`: 0-100 (jpeg/webp only)
+- `moderation`: `auto` (default) or `low`
+
+## Edit-specific parameters
+- `image`: one or more input images. For GPT Image models, you can provide up to 16 images.
+- `mask`: optional mask image
+- `input_fidelity`: `low` or `high` only for models that support it; do not set this for `gpt-image-2`
+
+Model-specific note for `input_fidelity`:
+- `gpt-image-2` always uses high fidelity for image inputs and does not support setting `input_fidelity`.
+- `gpt-image-1` and `gpt-image-1-mini` preserve all input images, but the first image gets richer textures and finer details.
+- `gpt-image-1.5` preserves the first 5 input images with higher fidelity.
+
+## Transparent backgrounds
+
+`gpt-image-2` does not currently support the Image API `background=transparent` parameter. In explicit CLI/API fallback mode, keep `gpt-image-2` when a flat chroma-key background plus local alpha extraction with `python "${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py"` is acceptable.
+
+Use CLI `gpt-image-1.5` with `background=transparent` and a transparent-capable output format such as `png` or `webp` only after the user explicitly confirms that fallback, unless they already requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback. If the user asks for true/native transparency, the subject is too complex for clean chroma-key removal, or local background removal fails validation, explain the tradeoff and ask before switching.
+
+## Output
+- `data[]` list with `b64_json` per image
+- The bundled `scripts/image_gen.py` CLI decodes `b64_json` and writes output files for you.
+
+## Limits and notes
+- Input images and masks must be under 50MB.
+- Use the edits endpoint when the user requests changes to an existing image.
+- Masking is prompt-guided; exact shapes are not guaranteed.
+- Large sizes and high quality increase latency and cost.
+- Use `quality=low` for fast drafts, thumbnails, and quick iterations. Use `medium` or `high` for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.
+- High `input_fidelity` can materially increase input token usage on models that support it.
+- If a request fails because a specific option is unsupported by the selected GPT Image model, retry manually without that option only when the option is not required by the user. If true transparent CLI output is required, ask before switching to `gpt-image-1.5` instead of dropping `background=transparent`, unless the user already explicitly chose that fallback.
+
+## Important boundary
+- `quality`, `input_fidelity`, explicit masks, `background`, `output_format`, and related parameters are fallback-only execution controls.
+- Do not assume they are built-in `image_gen` tool arguments.

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+# Prompting best practices
+
+These prompting principles are shared by both top-level modes of the skill:
+- built-in `image_gen` tool (default)
+- explicit `scripts/image_gen.py` CLI fallback
+
+This file is about prompt structure, specificity, and iteration. Fallback-only execution controls such as `quality`, `input_fidelity`, masks, output format, and output paths live in the fallback docs.
+
+## Contents
+- [Structure](#structure)
+- [Specificity policy](#specificity-policy)
+- [Allowed and disallowed augmentation](#allowed-and-disallowed-augmentation)
+- [Composition and layout](#composition-and-layout)
+- [Constraints and invariants](#constraints-and-invariants)
+- [Text in images](#text-in-images)
+- [Input images and references](#input-images-and-references)
+- [Iterate deliberately](#iterate-deliberately)
+- [Transparent images](#transparent-images)
+- [Fallback-only execution controls](#fallback-only-execution-controls)
+- [Use-case tips](#use-case-tips)
+- [Where to find copy/paste recipes](#where-to-find-copypaste-recipes)
+
+## Structure
+- Use a consistent order: scene/backdrop -> subject -> key details -> constraints -> output intent.
+- Include intended use (ad, UI mock, infographic) to set the level of polish.
+- For complex requests, use short labeled lines instead of one long paragraph.
+
+## Specificity policy
+- If the user prompt is already specific and detailed, normalize it into a clean spec without adding creative requirements.
+- If the prompt is generic, you may add tasteful detail when it materially improves the output.
+- Treat examples in `sample-prompts.md` as fully-authored recipes, not as the default amount of augmentation to add to every request.
+- For photorealism, include `photorealistic` directly when that is the goal, plus concrete real-world texture such as pores, wrinkles, fabric wear, material grain, or imperfect everyday detail.
+
+## Allowed and disallowed augmentation
+
+Allowed augmentation for generic prompts:
+- composition and framing cues
+- intended-use or polish-level hints
+- practical layout guidance
+- reasonable scene concreteness that supports the request
+
+Do not add:
+- extra characters, props, or objects that are not implied
+- brand palettes, slogans, or story beats that are not implied
+- arbitrary side-specific placement unless the surrounding layout supports it
+
+## Composition and layout
+- Specify framing and viewpoint (close-up, wide, top-down) and placement only when it materially helps.
+- Call out negative space if the asset clearly needs room for UI or copy.
+- Avoid making left/right layout decisions unless the user or surrounding layout supports them.
+- For people, describe body framing, scale, gaze, and object interactions when they matter (`full body visible`, `looking down at the book`, `hands naturally gripping the handlebars`).
+
+## Constraints and invariants
+- State what must not change (`keep background unchanged`).
+- For edits, say `change only X; keep Y unchanged` and repeat invariants on every iteration to reduce drift.
+
+## Text in images
+- Put literal text in quotes or ALL CAPS and specify typography (font style, size, color, placement).
+- Spell uncommon words letter-by-letter if accuracy matters.
+- For in-image copy, require verbatim rendering and no extra characters.
+- In CLI fallback mode, use `medium` or `high` quality for small text, dense infographics, data-heavy slides, multi-font layouts, legends, axes, and footnotes.
+
+## Input images and references
+- Do not assume that every provided image is an edit target.
+- Label each image by index and role (`Image 1: edit target`, `Image 2: style reference`).
+- If the user provides images for style, composition, or mood guidance and does not ask to modify them, treat the request as generation with references.
+- If the user asks to preserve an existing image while changing specific parts, treat the request as an edit.
+- For compositing, describe how the images interact (`place the subject from Image 2 into Image 1`).
+
+## Iterate deliberately
+- Start with a clean base prompt, then make small single-change edits.
+- Re-specify critical constraints when you iterate.
+- Prefer one targeted follow-up at a time over rewriting the whole prompt.
+
+## Transparent images
+- Ask built-in `image_gen` for a genuinely transparent background and preserve its alpha.
+
+## Fallback-only execution controls
+- `quality`, `input_fidelity`, explicit masks, output format, and output paths are fallback-only execution controls.
+- Do not assume they are built-in `image_gen` tool arguments.
+- If the user explicitly chooses CLI fallback, see `references/cli.md` and `references/image-api.md` for those controls.
+- In CLI fallback mode, `gpt-image-2` is the default. It supports `quality=low|medium|high|auto`; use `low` for fast drafts and thumbnails, and move to `medium`, `high`, or `auto` for final assets.
+- `gpt-image-2` always uses high fidelity for image inputs, so do not set `input_fidelity` with that model.
+- CLI `gpt-image-2` does not support `background=transparent`; ask before using `gpt-image-1.5` unless the user explicitly requested that model.
+- If the user asks for 4K-style output with `gpt-image-2`, use `3840x2160` for landscape or `2160x3840` for portrait.
+
+## Use-case tips
+Generate:
+- photorealistic-natural: Prompt as if a real photo is captured in the moment; use photography language (lens, lighting, framing); call for real texture; avoid over-stylized polish unless requested.
+- product-mockup: Describe the product/packaging and materials; ensure clean silhouette and label clarity; if in-image text is needed, require verbatim rendering and specify typography.
+- ui-mockup: Describe the target fidelity first (shippable mockup or low-fi wireframe), then focus on layout, hierarchy, and practical UI elements; avoid concept-art language.
+- infographic-diagram: Define the audience and layout flow; label parts explicitly; require verbatim text; prefer higher quality in CLI mode for dense labels.
+- logo-brand: Keep it simple and scalable; ask for a strong silhouette and balanced negative space; avoid decorative flourishes unless requested.
+- ads-marketing: Write like a creative brief; include brand positioning, audience, desired vibe, scene, and exact tagline if text must appear.
+- productivity-visual: Name the exact artifact (slide, chart, workflow diagram), define the canvas and hierarchy, provide real labels/data, and ask for readable typography and polished spacing.
+- scientific-educational: Define audience, lesson objective, required labels, scientific constraints, arrows, and scan-friendly whitespace.
+- illustration-story: Define panels or scene beats; keep each action concrete.
+- stylized-concept: Specify style cues, material finish, and rendering approach (3D, painterly, clay) without inventing new story elements.
+- historical-scene: State the location/date and required period accuracy; constrain clothing, props, and environment to match the era.
+
+Edit:
+- text-localization: Change only the text; preserve layout, typography, spacing, and hierarchy; no extra words or reflow unless needed.
+- identity-preserve: Lock identity (face, body, pose, hair, expression); change only the specified elements; match lighting and shadows.
+- precise-object-edit: Specify exactly what to remove/replace; preserve surrounding texture and lighting; keep everything else unchanged.
+- lighting-weather: Change only environmental conditions (light, shadows, atmosphere, precipitation); keep geometry, framing, and subject identity.
+- background-extraction: Request a clean cutout on a genuinely transparent background; preserve fine edges and label text; no halos or restyling.
+- style-transfer: Specify style cues to preserve (palette, texture, brushwork) and what must change; add `no extra elements` to prevent drift.
+- compositing: Reference inputs by index; specify what moves where; match lighting, perspective, and scale; keep the base framing unchanged.
+- sketch-to-render: Preserve layout, proportions, and perspective; choose materials and lighting that support the supplied sketch without adding new elements.
+
+## Where to find copy/paste recipes
+For copy/paste prompt specs (examples only), see `references/sample-prompts.md`. This file focuses on principles, specificity, and iteration patterns.

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+# Sample prompts (copy/paste)
+
+These prompt recipes are shared across both top-level modes of the skill:
+- built-in `image_gen` tool (default)
+- `scripts/image_gen.py` CLI fallback for explicit or user-confirmed CLI/API/model requests
+
+Use these as starting points. They are intentionally complete prompt recipes, not the default amount of augmentation to add to every user request.
+
+When adapting a user's prompt:
+- keep user-provided requirements
+- only add detail according to the specificity policy in `SKILL.md`
+- do not treat every example below as permission to invent extra story elements
+
+The labeled lines are prompt scaffolding, not a closed schema. `Asset type` and `Input images` are prompt-only scaffolding; the CLI does not expose them as dedicated flags.
+
+Execution details such as explicit CLI flags, `quality`, `input_fidelity`, masks, output formats, and local output paths depend on mode. Use built-in `image_gen` by default, request transparent backgrounds directly, and preserve the generated alpha; apply CLI-specific controls only when the user chooses or confirms that fallback.
+
+CLI model notes:
+- `gpt-image-2` is the fallback CLI default for new workflows.
+- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.
+- For 4K-style `gpt-image-2` output, use `3840x2160` or `2160x3840`.
+- CLI `gpt-image-2` does not support `background=transparent`; ask before using `gpt-image-1.5` unless the user explicitly requested that model.
+- Do not set `input_fidelity` with `gpt-image-2`; image inputs already use high fidelity.
+
+For prompting principles (structure, specificity, invariants, iteration), see `references/prompting.md`.
+
+## Generate
+
+### photorealistic-natural
+```
+Use case: photorealistic-natural
+Primary request: candid photo of an elderly sailor on a small fishing boat adjusting a net
+Scene/backdrop: coastal water with soft haze
+Subject: weathered skin with wrinkles and sun texture
+Style/medium: photorealistic candid photo
+Composition/framing: medium close-up, eye-level
+Lighting/mood: soft coastal daylight, shallow depth of field, subtle film grain
+Materials/textures: real skin texture, worn fabric, salt-worn wood
+Constraints: natural color balance; no heavy retouching; no glamorization; no watermark
+Avoid: studio polish; staged look
+```
+
+### product-mockup
+```
+Use case: product-mockup
+Primary request: premium product photo of a matte black shampoo bottle with a minimal label
+Scene/backdrop: clean studio gradient from light gray to white
+Subject: single bottle centered with subtle reflection
+Style/medium: premium product photography
+Composition/framing: centered, slight three-quarter angle, generous padding
+Lighting/mood: softbox lighting, clean highlights, controlled shadows
+Materials/textures: matte plastic, crisp label printing
+Constraints: no logos or trademarks; no watermark
+```
+
+### ui-mockup
+```
+Use case: ui-mockup
+Primary request: mobile app home screen for a local farmers market with vendors and daily specials
+Asset type: mobile app screen
+Style/medium: realistic product UI, not concept art
+Composition/framing: clean vertical mobile layout with clear hierarchy
+Constraints: practical layout, clear typography, no logos or trademarks, no watermark
+```
+
+### infographic-diagram
+```
+Use case: infographic-diagram
+Primary request: detailed infographic of an automatic coffee machine flow
+Scene/backdrop: clean, light neutral background
+Subject: bean hopper -> grinder -> brew group -> boiler -> water tank -> drip tray
+Style/medium: clean vector-like infographic with clear callouts and arrows
+Composition/framing: vertical poster layout, top-to-bottom flow
+Text (verbatim): "Bean Hopper", "Grinder", "Brew Group", "Boiler", "Water Tank", "Drip Tray"
+Constraints: clear labels, strong contrast, no logos or trademarks, no watermark
+```
+
+### scientific-educational
+```
+Use case: scientific-educational
+Primary request: biology diagram titled "Cellular Respiration at a Glance" for high school students
+Scene/backdrop: clean white classroom handout background
+Subject: glucose turns into energy inside a cell; include glycolysis, Krebs cycle, and electron transport chain
+Style/medium: flat scientific diagram with consistent icons, arrows, and readable labels
+Composition/framing: landscape slide-style layout with clear hierarchy and generous whitespace
+Text (verbatim): "Cellular Respiration at a Glance", "Glucose", "Pyruvate", "ATP", "NADH", "FADH2", "CO2", "O2", "H2O"
+Constraints: scientifically plausible; avoid tiny text; no extra decoration; no watermark
+```
+
+### logo-brand
+```
+Use case: logo-brand
+Primary request: original logo for "Field & Flour", a local bakery
+Style/medium: vector logo mark; flat colors; minimal
+Composition/framing: single centered logo on a plain background with generous padding
+Constraints: strong silhouette, balanced negative space; original design only; no gradients unless essential; no trademarks; no watermark
+```
+
+### illustration-story
+```
+Use case: illustration-story
+Primary request: 4-panel comic about a pet left alone at home
+Scene/backdrop: cozy living room across panels
+Subject: pet reacting to the owner leaving, then relaxing, then returning to a composed pose
+Style/medium: comic illustration with clear panels
+Composition/framing: 4 equal-sized vertical panels, readable actions per panel
+Constraints: no text; no logos or trademarks; no watermark
+```
+
+### stylized-concept
+```
+Use case: stylized-concept
+Primary request: cavernous hangar interior with tall support beams and drifting fog
+Scene/backdrop: industrial hangar interior, deep scale, light haze
+Subject: compact shuttle parked near the center
+Style/medium: cinematic concept art, industrial realism
+Composition/framing: wide-angle, low-angle
+Lighting/mood: volumetric light rays cutting through fog
+Constraints: no logos or trademarks; no watermark
+```
+
+### ads-marketing
+```
+Use case: ads-marketing
+Primary request: campaign image for a streetwear brand called Thread
+Subject: group of friends hanging out together in a stylish urban setting
+Style/medium: polished youth streetwear campaign photography
+Composition/framing: vertical ad layout with natural poses and integrated headline space
+Lighting/mood: contemporary, energetic, tasteful
+Text (verbatim): "Yours to Create."
+Constraints: render the tagline exactly once; clean legible typography; no extra text; no watermarks; no unrelated logos
+```
+
+### productivity-visual
+```
+Use case: productivity-visual
+Primary request: one pitch-deck slide titled "Market Opportunity"
+Asset type: fundraising slide image
+Style/medium: clean modern deck slide, white background, crisp sans-serif typography
+Subject: TAM/SAM/SOM concentric-circle diagram plus a small growth bar chart from 2021 to 2026
+Composition/framing: 16:9 landscape slide, clear data hierarchy, polished spacing
+Text (verbatim): "Market Opportunity", "TAM: $42B", "SAM: $8.7B", "SOM: $340M", "AGI Research, 2024", "Internal analysis"
+Constraints: readable labels, no clip art, no stock photography, no decorative clutter, no watermark
+```
+
+### historical-scene
+```
+Use case: historical-scene
+Primary request: outdoor crowd scene in Bethel, New York on August 16, 1969
+Scene/backdrop: open field with period-appropriate staging
+Subject: crowd in period-accurate clothing, authentic environment
+Style/medium: photorealistic photo
+Composition/framing: wide shot, eye-level
+Constraints: period-accurate details; no modern objects; no logos or trademarks; no watermark
+```
+
+## Asset type templates (taxonomy-aligned)
+
+### Website assets template
+```
+Use case: <photorealistic-natural|stylized-concept|product-mockup|infographic-diagram|ui-mockup>
+Asset type: <hero image / section illustration / blog header>
+Primary request: <short description>
+Scene/backdrop: <environment or abstract backdrop>
+Subject: <main subject>
+Style/medium: <photo/illustration/3D>
+Composition/framing: <wide/centered; note usable negative space only if needed>
+Lighting/mood: <soft/bright/neutral>
+Color palette: <brand colors or neutral>
+Constraints: <no text; no logos; no watermark; leave room for UI if needed>
+```
+
+### Website assets example: minimal hero background
+```
+Use case: stylized-concept
+Asset type: landing page hero background
+Primary request: minimal abstract background with a soft gradient and subtle texture
+Style/medium: matte illustration / soft-rendered abstract background
+Composition/framing: wide composition with usable negative space for page copy
+Lighting/mood: gentle studio glow
+Color palette: restrained neutral palette
+Constraints: no text; no logos; no watermark
+```
+
+### Website assets example: feature section illustration
+```
+Use case: stylized-concept
+Asset type: feature section illustration
+Primary request: simple abstract shapes suggesting connection and flow
+Scene/backdrop: subtle light-gray backdrop with faint texture
+Style/medium: flat illustration; soft shadows; restrained contrast
+Composition/framing: centered cluster; open margins for UI
+Color palette: muted neutral palette
+Constraints: no text; no logos; no watermark
+```
+
+### Website assets example: blog header image
+```
+Use case: photorealistic-natural
+Asset type: blog header image
+Primary request: overhead desk scene with notebook, pen, and coffee cup
+Scene/backdrop: warm wooden tabletop
+Style/medium: photorealistic photo
+Composition/framing: wide crop with clean room for page copy
+Lighting/mood: soft morning light
+Constraints: no text; no logos; no watermark
+```
+
+### Game assets template
+```
+Use case: stylized-concept
+Asset type: <game environment concept art / game character concept / game UI icon / tileable game texture>
+Primary request: <biome/scene/character/icon/material>
+Scene/backdrop: <location + set dressing> (if applicable)
+Subject: <main focal element(s)>
+Style/medium: <realistic/stylized>; <concept art / character render / UI icon / texture>
+Composition/framing: <wide/establishing/top-down>; <camera angle>; <focal point placement>
+Lighting/mood: <time of day>; <mood>; <volumetric/fog/etc>
+Constraints: no logos or trademarks; no watermark
+```
+
+### Game assets example: environment concept art
+```
+Use case: stylized-concept
+Asset type: game environment concept art
+Primary request: cavernous hangar interior with tall support beams and drifting fog
+Scene/backdrop: industrial hangar interior, deep scale, light haze
+Subject: compact shuttle parked near the center
+Style/medium: cinematic concept art, industrial realism
+Composition/framing: wide-angle, low-angle
+Lighting/mood: volumetric light rays cutting through fog
+Constraints: no logos or trademarks; no watermark
+```
+
+### Game assets example: character concept
+```
+Use case: stylized-concept
+Asset type: game character concept
+Primary request: desert scout character with layered travel gear
+Subject: long coat, satchel, practical travel clothing
+Style/medium: character render; stylized realism
+Composition/framing: neutral hero pose on a simple backdrop
+Constraints: no logos or trademarks; no watermark
+```
+
+### Game assets example: UI icon
+```
+Use case: stylized-concept
+Asset type: game UI icon
+Primary request: round shield icon with a subtle rune pattern
+Style/medium: painted game UI icon
+Composition/framing: centered icon; generous padding; clear silhouette
+Constraints: no text; no background scene elements; no logos or trademarks; no watermark
+```
+
+### Game assets example: tileable texture
+```
+Use case: stylized-concept
+Asset type: tileable game texture
+Primary request: worn sandstone blocks
+Style/medium: seamless tileable texture; PBR-ish look
+Scene/backdrop: neutral lighting reference only
+Constraints: seamless edges; no obvious focal elements; no text; no logos or trademarks; no watermark
+```
+
+### Wireframe template
+```
+Use case: ui-mockup
+Asset type: website wireframe
+Primary request: <page or flow to sketch>
+Style/medium: low-fi grayscale wireframe
+Composition/framing: <landscape or portrait to match expected device>
+Subject: <sections in order; grid/columns; key labels>
+Constraints: no color; no logos; no real photos; no watermark
+```
+
+### Wireframe example: homepage (desktop)
+```
+Use case: ui-mockup
+Asset type: website wireframe
+Primary request: SaaS homepage layout with clear hierarchy
+Style/medium: low-fi grayscale wireframe
+Subject: top nav; hero with headline and CTA; three feature cards; testimonial strip; pricing preview; footer
+Composition/framing: landscape desktop layout
+Constraints: label major blocks; no color; no logos; no real photos; no watermark
+```
+
+### Wireframe example: pricing page
+```
+Use case: ui-mockup
+Asset type: website wireframe
+Primary request: pricing page layout with comparison table
+Style/medium: low-fi grayscale wireframe
+Subject: header; plan toggle; 3 pricing cards; comparison table; FAQ accordion; footer
+Composition/framing: desktop or tablet layout
+Constraints: label key areas; no color; no logos; no real photos; no watermark
+```
+
+### Wireframe example: mobile onboarding flow
+```
+Use case: ui-mockup
+Asset type: mobile onboarding wireframe
+Primary request: three-screen mobile onboarding flow
+Style/medium: low-fi grayscale wireframe
+Subject: screen 1 headline and CTA; screen 2 feature bullets; screen 3 form fields and CTA
+Composition/framing: portrait mobile layout
+Constraints: label screens and blocks; no color; no logos; no real photos; no watermark
+```
+
+### Logo template
+```
+Use case: logo-brand
+Asset type: logo concept
+Primary request: <brand idea or symbol concept>
+Style/medium: vector logo mark; flat colors; minimal
+Composition/framing: centered mark; clear silhouette; generous margin
+Color palette: <1-2 colors; high contrast>
+Text (verbatim): "<exact name>" (only if needed)
+Constraints: no gradients; no mockups; no 3D; no watermark
+```
+
+### Logo example: abstract symbol mark
+```
+Use case: logo-brand
+Asset type: logo concept
+Primary request: geometric leaf symbol suggesting sustainability and growth
+Style/medium: vector logo mark; flat colors; minimal
+Composition/framing: centered mark; clear silhouette
+Color palette: deep green and off-white
+Constraints: no text unless requested; no gradients; no mockups; no 3D; no watermark
+```
+
+### Logo example: monogram mark
+```
+Use case: logo-brand
+Asset type: logo concept
+Primary request: interlocking monogram of the letters "AV"
+Style/medium: vector logo mark; flat colors; minimal
+Composition/framing: centered mark; balanced spacing
+Color palette: black on white
+Constraints: no gradients; no mockups; no 3D; no watermark
+```
+
+### Logo example: wordmark
+```
+Use case: logo-brand
+Asset type: logo concept
+Primary request: clean wordmark for a modern studio
+Style/medium: vector wordmark; flat colors; minimal
+Text (verbatim): "Studio North"
+Composition/framing: centered text; even letter spacing
+Constraints: no gradients; no mockups; no 3D; no watermark
+```
+
+## Edit
+
+### text-localization
+```
+Use case: text-localization
+Input images: Image 1: original infographic
+Primary request: replace "Bean Hopper", "Grinder", "Brew Group", "Boiler", "Water Tank", and "Drip Tray" with "Tolva", "Molino", "Grupo de infusión", "Caldera", "Depósito de agua", and "Bandeja de goteo"
+Constraints: change only the text; preserve layout, typography, spacing, and hierarchy; no extra words; do not alter logos or imagery
+```
+
+### identity-preserve
+```
+Use case: identity-preserve
+Input images: Image 1: person photo; Image 2..N: clothing references
+Primary request: replace only the clothing with the provided garments
+Constraints: preserve face, body shape, pose, hair, expression, and identity; match lighting and shadows; keep the background unchanged; no accessories or text
+```
+
+### precise-object-edit
+```
+Use case: precise-object-edit
+Input images: Image 1: room photo
+Primary request: replace only the white chairs with wooden chairs
+Constraints: preserve camera angle, room lighting, floor shadows, and surrounding objects; keep all other aspects unchanged
+```
+
+### lighting-weather
+```
+Use case: lighting-weather
+Input images: Image 1: original photo
+Primary request: make it look like a winter evening with gentle snowfall
+Constraints: preserve subject identity, geometry, camera angle, and composition; change only lighting, atmosphere, and weather
+```
+
+### style-transfer
+```
+Use case: style-transfer
+Input images: Image 1: style reference
+Primary request: apply Image 1's visual style to a man riding a motorcycle on a plain white backdrop
+Constraints: preserve palette, texture, and brushwork; no extra elements
+```
+
+### compositing
+```
+Use case: compositing
+Input images: Image 1: base scene; Image 2: subject to insert
+Primary request: place the subject from Image 2 next to the person in Image 1
+Constraints: match lighting, perspective, and scale; keep the base framing unchanged; no extra elements
+```
+
+### character consistency workflow
+```
+Use case: identity-preserve
+Input images: Image 1: previous character anchor illustration
+Primary request: continue the story with the same character in a new scene and action
+Scene/backdrop: snowy forest after a winter storm
+Subject: same young forest hero gently helping a frightened squirrel out of a fallen tree
+Style/medium: same children's book watercolor illustration style as Image 1
+Constraints: do not redesign the character; preserve facial features, proportions, outfit, color palette, and personality; no text; no watermark
+```
+
+### sketch-to-render
+```
+Use case: sketch-to-render
+Input images: Image 1: drawing
+Primary request: turn the drawing into a photorealistic image
+Constraints: preserve layout, proportions, and perspective; choose realistic materials and lighting; do not add new elements or text
+```

20261001T054755.827592Z_eval/episode-0011/runtime_uy_v15n7/codex/skills/.system/imagegen/scripts/image_gen.py

@@ -0,0 +1,1030 @@
+#!/usr/bin/env python3
+"""Fallback CLI for explicit image generation or editing with GPT Image models.
+
+Used only when the user explicitly opts into CLI fallback mode, or when explicit
+transparent output requires the `gpt-image-1.5` fallback path.
+
+Defaults to gpt-image-2 and a structured prompt augmentation workflow.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import base64
+import json
+import os
+from pathlib import Path
+import re
+import sys
+import time
+from typing import Any, Dict, Iterable, List, Optional, Tuple
+
+from io import BytesIO
+
+DEFAULT_MODEL = "gpt-image-2"
+DEFAULT_SIZE = "auto"
+DEFAULT_QUALITY = "medium"
+DEFAULT_OUTPUT_FORMAT = "png"
+DEFAULT_CONCURRENCY = 5
+DEFAULT_DOWNSCALE_SUFFIX = "-web"
+DEFAULT_OUTPUT_PATH = "output/imagegen/output.png"
+GPT_IMAGE_MODEL_PREFIX = "gpt-image-"
+
+ALLOWED_LEGACY_SIZES = {"1024x1024", "1536x1024", "1024x1536", "auto"}
+ALLOWED_QUALITIES = {"low", "medium", "high", "auto"}
+ALLOWED_BACKGROUNDS = {"transparent", "opaque", "auto", None}
+ALLOWED_INPUT_FIDELITIES = {"low", "high", None}
+
+GPT_IMAGE_2_MODEL = "gpt-image-2"
+GPT_IMAGE_2_MIN_PIXELS = 655_360
+GPT_IMAGE_2_MAX_PIXELS = 8_294_400
+GPT_IMAGE_2_MAX_EDGE = 3840
+GPT_IMAGE_2_MAX_RATIO = 3.0
+
+MAX_IMAGE_BYTES = 50 * 1024 * 1024
+MAX_BATCH_JOBS = 500
+
+
+def _die(message: str, code: int = 1) -> None:
+    print(f"Error: {message}", file=sys.stderr)
+    raise SystemExit(code)
+
+
+def _warn(message: str) -> None:
+    print(f"Warning: {message}", file=sys.stderr)
+
+
+def _dependency_hint(package: str, *, upgrade: bool = False) -> str:
+    command = f"uv pip install {'-U ' if upgrade else ''}{package}"
+    return (
+        "Activate the repo-selected environment first, then install it with "
+        f"`{command}`. If this repo uses a local virtualenv, start with "
+        "`source .venv/bin/activate`; otherwise use this repo's configured shared fallback "
+        "environment. If your project declares dependencies, prefer that project's normal "
+        "`uv sync` flow."
+    )
+
+
+def _ensure_api_key(dry_run: bool) -> None:
+    if os.getenv("OPENAI_API_KEY"):
+        print("OPENAI_API_KEY is set.", file=sys.stderr)
+        return
+    if dry_run:
+        _warn("OPENAI_API_KEY is not set; dry-run only.")
+        return
+    _die("OPENAI_API_KEY is not set. Export it before running.")
+
+
+def _read_prompt(prompt: Optional[str], prompt_file: Optional[str]) -> str:
+    if prompt and prompt_file:
+        _die("Use --prompt or --prompt-file, not both.")
+    if prompt_file:
+        path = Path(prompt_file)
+        if not path.exists():
+            _die(f"Prompt file not found: {path}")
+        return path.read_text(encoding="utf-8").strip()
+    if prompt:
+        return prompt.strip()
+    _die("Missing prompt. Use --prompt or --prompt-file.")
+    return ""  # unreachable
+
+
+def _check_image_paths(paths: Iterable[str]) -> List[Path]:
+    resolved: List[Path] = []
+    for raw in paths:
+        path = Path(raw)
+        if not path.exists():
+            _die(f"Image file not found: {path}")
+        if path.stat().st_size > MAX_IMAGE_BYTES:
+            _warn(f"Image exceeds 50MB limit: {path}")
+        resolved.append(path)
+    return resolved
+
+
+def _normalize_output_format(fmt: Optional[str]) -> str:
+    if not fmt:
+        return DEFAULT_OUTPUT_FORMAT
+    fmt = fmt.lower()
+    if fmt not in {"png", "jpeg", "jpg", "webp"}:
+        _die("output-format must be png, jpeg, jpg, or webp.")
+    return "jpeg" if fmt == "jpg" else fmt
+
+
+def _parse_size(size: str) -> Optional[Tuple[int, int]]:
+    match = re.fullmatch(r"([1-9][0-9]*)x([1-9][0-9]*)", size)
+    if not match:
+        return None
+    return int(match.group(1)), int(match.group(2))
+
+
+def _validate_gpt_image_2_size(size: str) -> None:
+    if size == "auto":
+        return
+
+    parsed = _parse_size(size)
+    if parsed is None:
+        _die("size must be auto or WIDTHxHEIGHT, for example 1024x1024.")
+
+    width, height = parsed
+    max_edge = max(width, height)
+    min_edge = min(width, height)
+    total_pixels = width * height
+
+    if max_edge > GPT_IMAGE_2_MAX_EDGE:
+        _die(
+            "gpt-image-2 size maximum edge length must be less than or equal to 3840px."
+        )
+    if width % 16 != 0 or height % 16 != 0:
+        _die("gpt-image-2 size width and height must be multiples of 16px.")
+    if max_edge / min_edge > GPT_IMAGE_2_MAX_RATIO:
+        _die("gpt-image-2 size long edge to short edge ratio must not exceed 3:1.")
+    if total_pixels < GPT_IMAGE_2_MIN_PIXELS or total_pixels > GPT_IMAGE_2_MAX_PIXELS:
+        _die(
+            "gpt-image-2 size total pixels must be at least 655,360 and no more than 8,294,400."
+        )
+
+
+def _validate_size(size: str, model: str) -> None:
+    if model == GPT_IMAGE_2_MODEL:
+        _validate_gpt_image_2_size(size)
+        return
+
+    if size not in ALLOWED_LEGACY_SIZES:
+        _die(
+            "size must be one of 1024x1024, 1536x1024, 1024x1536, or auto for this GPT Image model."
+        )
+
+
+def _validate_quality(quality: str) -> None:
+    if quality not in ALLOWED_QUALITIES:
+        _die("quality must be one of low, medium, high, or auto.")
+
+
+def _validate_background(background: Optional[str]) -> None:
+    if background not in ALLOWED_BACKGROUNDS:
+        _die("background must be one of transparent, opaque, or auto.")
+
+
+def _validate_input_fidelity(input_fidelity: Optional[str]) -> None:
+    if input_fidelity not in ALLOWED_INPUT_FIDELITIES:
+        _die("input-fidelity must be one of low or high.")
+
+
+def _validate_model(model: str) -> None:
+    if not model.startswith(GPT_IMAGE_MODEL_PREFIX):
+        _die(
+            "model must be a GPT Image model (for example gpt-image-1.5, gpt-image-1, or gpt-image-1-mini)."
+        )
+
+
+def _validate_transparency(background: Optional[str], output_format: str) -> None:
+    if background == "transparent" and output_format not in {"png", "webp"}:
+        _die("transparent background requires output-format png or webp.")
+
+
+def _validate_model_specific_options(
+    *,
+    model: str,
+    background: Optional[str],
+    input_fidelity: Optional[str] = None,
+) -> None:
+    if model != GPT_IMAGE_2_MODEL:
+        return
+    if background == "transparent":
+        _die(
+            "transparent backgrounds are not supported in gpt-image-2, the latest model. "
+            "Use --model gpt-image-1.5 --background transparent --output-format png instead."
+        )
+    if input_fidelity is not None:
+        _die(
+            "input_fidelity is not supported in gpt-image-2 because image inputs always use high fidelity for this model."
+        )
+
+
+def _validate_generate_payload(payload: Dict[str, Any]) -> None:
+    model = str(payload.get("model", DEFAULT_MODEL))
+    _validate_model(model)
+    n = int(payload.get("n", 1))
+    if n < 1 or n > 10:
+        _die("n must be between 1 and 10")
+    size = str(payload.get("size", DEFAULT_SIZE))
+    quality = str(payload.get("quality", DEFAULT_QUALITY))
+    background = payload.get("background")
+    _validate_size(size, model)
+    _validate_quality(quality)
+    _validate_background(background)
+    _validate_model_specific_options(model=model, background=background)
+    oc = payload.get("output_compression")
+    if oc is not None and not (0 <= int(oc) <= 100):
+        _die("output_compression must be between 0 and 100")
+
+
+def _build_output_paths(
+    out: str,
+    output_format: str,
+    count: int,
+    out_dir: Optional[str],
+) -> List[Path]:
+    ext = "." + output_format
+
+    if out_dir:
+        out_base = Path(out_dir)
+        out_base.mkdir(parents=True, exist_ok=True)
+        return [out_base / f"image_{i}{ext}" for i in range(1, count + 1)]
+
+    out_path = Path(out)
+    if out_path.exists() and out_path.is_dir():
+        out_path.mkdir(parents=True, exist_ok=True)
+        return [out_path / f"image_{i}{ext}" for i in range(1, count + 1)]
+
+    if out_path.suffix == "":
+        out_path = out_path.with_suffix(ext)
+    elif output_format and out_path.suffix.lstrip(".").lower() != output_format:
+        _warn(
+            f"Output extension {out_path.suffix} does not match output-format {output_format}."
+        )
+
+    if count == 1:
+        return [out_path]
+
+    return [
+        out_path.with_name(f"{out_path.stem}-{i}{out_path.suffix}")
+        for i in range(1, count + 1)
+    ]
+
+
+def _augment_prompt(args: argparse.Namespace, prompt: str) -> str:
+    fields = _fields_from_args(args)
+    return _augment_prompt_fields(args.augment, prompt, fields)
+
+
+def _augment_prompt_fields(
+    augment: bool, prompt: str, fields: Dict[str, Optional[str]]
+) -> str:
+    if not augment:
+        return prompt
+
+    sections: List[str] = []
+    if fields.get("use_case"):
+        sections.append(f"Use case: {fields['use_case']}")
+    sections.append(f"Primary request: {prompt}")
+    if fields.get("scene"):
+        sections.append(f"Scene/background: {fields['scene']}")
+    if fields.get("subject"):
+        sections.append(f"Subject: {fields['subject']}")
+    if fields.get("style"):
+        sections.append(f"Style/medium: {fields['style']}")
+    if fields.get("composition"):
+        sections.append(f"Composition/framing: {fields['composition']}")
+    if fields.get("lighting"):
+        sections.append(f"Lighting/mood: {fields['lighting']}")
+    if fields.get("palette"):
+        sections.append(f"Color palette: {fields['palette']}")
+    if fields.get("materials"):
+        sections.append(f"Materials/textures: {fields['materials']}")
+    if fields.get("text"):
+        sections.append(f'Text (verbatim): "{fields["text"]}"')
+    if fields.get("constraints"):
+        sections.append(f"Constraints: {fields['constraints']}")
+    if fields.get("negative"):
+        sections.append(f"Avoid: {fields['negative']}")
+
+    return "\n".join(sections)
+
+
+def _fields_from_args(args: argparse.Namespace) -> Dict[str, Optional[str]]:
+    return {
+        "use_case": getattr(args, "use_case", None),
+        "scene": getattr(args, "scene", None),
+        "subject": getattr(args, "subject", None),
+        "style": getattr(args, "style", None),
+        "composition": getattr(args, "composition", None),
+        "lighting": getattr(args, "lighting", None),
+        "palette": getattr(args, "palette", None),
+        "materials": getattr(args, "materials", None),
+        "text": getattr(args, "text", None),
+        "constraints": getattr(args, "constraints", None),
+        "negative": getattr(args, "negative", None),
+    }
+
+
+def _print_request(payload: dict) -> None:
+    print(json.dumps(payload, indent=2, sort_keys=True))
+
+
+def _decode_and_write(images: List[str], outputs: List[Path], force: bool) -> None:
+    for idx, image_b64 in enumerate(images):
+        if idx >= len(outputs):
+            break
+        out_path = outputs[idx]
+        if out_path.exists() and not force:
+            _die(f"Output already exists: {out_path} (use --force to overwrite)")
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        out_path.write_bytes(base64.b64decode(image_b64))
+        print(f"Wrote {out_path}")
+
+
+def _derive_downscale_path(path: Path, suffix: str) -> Path:
+    if suffix and not suffix.startswith("-") and not suffix.startswith("_"):
+        suffix = "-" + suffix
+    return path.with_name(f"{path.stem}{suffix}{path.suffix}")
+
+
+def _downscale_image_bytes(
+    image_bytes: bytes, *, max_dim: int, output_format: str
+) -> bytes:
+    try:
+        from PIL import Image
+    except Exception:
+        _die(f"Downscaling requires Pillow. {_dependency_hint('pillow')}")
+
+    if max_dim < 1:
+        _die("--downscale-max-dim must be >= 1")
+
+    with Image.open(BytesIO(image_bytes)) as img:
+        img.load()
+        w, h = img.size
+        scale = min(1.0, float(max_dim) / float(max(w, h)))
+        target = (max(1, int(round(w * scale))), max(1, int(round(h * scale))))
+
+        resized = (
+            img if target == (w, h) else img.resize(target, Image.Resampling.LANCZOS)
+        )
+
+        fmt = output_format.lower()
+        if fmt == "jpg":
+            fmt = "jpeg"
+
+        if fmt == "jpeg":
+            if resized.mode in ("RGBA", "LA") or (
+                "transparency" in getattr(resized, "info", {})
+            ):
+                bg = Image.new("RGB", resized.size, (255, 255, 255))
+                bg.paste(
+                    resized.convert("RGBA"), mask=resized.convert("RGBA").split()[-1]
+                )
+                resized = bg
+            else:
+                resized = resized.convert("RGB")
+
+        out = BytesIO()
+        resized.save(out, format=fmt.upper())
+        return out.getvalue()
+
+
+def _decode_write_and_downscale(
+    images: List[str],
+    outputs: List[Path],
+    *,
+    force: bool,
+    downscale_max_dim: Optional[int],
+    downscale_suffix: str,
+    output_format: str,
+) -> None:
+    for idx, image_b64 in enumerate(images):
+        if idx >= len(outputs):
+            break
+        out_path = outputs[idx]
+        if out_path.exists() and not force:
+            _die(f"Output already exists: {out_path} (use --force to overwrite)")
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+
+        raw = base64.b64decode(image_b64)
+        out_path.write_bytes(raw)
+        print(f"Wrote {out_path}")
+
+        if downscale_max_dim is None:
+            continue
+
+        derived = _derive_downscale_path(out_path, downscale_suffix)
+        if derived.exists() and not force:
+            _die(f"Output already exists: {derived} (use --force to overwrite)")
+        derived.parent.mkdir(parents=True, exist_ok=True)
+        resized = _downscale_image_bytes(
+            raw, max_dim=downscale_max_dim, output_format=output_format
+        )
+        derived.write_bytes(resized)
+        print(f"Wrote {derived}")
+
+
+def _create_client():
+    try:
+        from openai import OpenAI
+    except ImportError:
+        _die(
+            f"openai SDK not installed in the active environment. {_dependency_hint('openai')}"
+        )
+    return OpenAI()
+
+
+def _create_async_client():
+    try:
+        from openai import AsyncOpenAI
+    except ImportError:
+        try:
+            import openai as _openai  # noqa: F401
+        except ImportError:
+            _die(
+                f"openai SDK not installed in the active environment. {_dependency_hint('openai')}"
+            )
+        _die(
+            "AsyncOpenAI not available in this openai SDK version. "
+            f"{_dependency_hint('openai', upgrade=True)}"
+        )
+    return AsyncOpenAI()
+
+
+def _slugify(value: str) -> str:
+    value = value.strip().lower()
+    value = re.sub(r"[^a-z0-9]+", "-", value)
+    value = re.sub(r"-{2,}", "-", value).strip("-")
+    return value[:60] if value else "job"
+
+
+def _normalize_job(job: Any, idx: int) -> Dict[str, Any]:
+    if isinstance(job, str):
+        prompt = job.strip()
+        if not prompt:
+            _die(f"Empty prompt at job {idx}")
+        return {"prompt": prompt}
+    if isinstance(job, dict):
+        if "prompt" not in job or not str(job["prompt"]).strip():
+            _die(f"Missing prompt for job {idx}")
+        return job
+    _die(f"Invalid job at index {idx}: expected string or object.")
+    return {}  # unreachable
+
+
+def _read_jobs_jsonl(path: str) -> List[Dict[str, Any]]:
+    p = Path(path)
+    if not p.exists():
+        _die(f"Input file not found: {p}")
+    jobs: List[Dict[str, Any]] = []
+    for line_no, raw in enumerate(p.read_text(encoding="utf-8").splitlines(), start=1):
+        line = raw.strip()
+        if not line or line.startswith("#"):
+            continue
+        try:
+            item: Any
+            if line.startswith("{"):
+                item = json.loads(line)
+            else:
+                item = line
+            jobs.append(_normalize_job(item, idx=line_no))
+        except json.JSONDecodeError as exc:
+            _die(f"Invalid JSON on line {line_no}: {exc}")
+    if not jobs:
+        _die("No jobs found in input file.")
+    if len(jobs) > MAX_BATCH_JOBS:
+        _die(f"Too many jobs ({len(jobs)}). Max is {MAX_BATCH_JOBS}.")
+    return jobs
+
+
+def _merge_non_null(dst: Dict[str, Any], src: Dict[str, Any]) -> Dict[str, Any]:
+    merged = dict(dst)
+    for k, v in src.items():
+        if v is not None:
+            merged[k] = v
+    return merged
+
+
+def _job_output_paths(
+    *,
+    out_dir: Path,
+    output_format: str,
+    idx: int,
+    prompt: str,
+    n: int,
+    explicit_out: Optional[str],
+) -> List[Path]:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    ext = "." + output_format
+
+    if explicit_out:
+        base = Path(explicit_out)
+        if base.suffix == "":
+            base = base.with_suffix(ext)
+        elif base.suffix.lstrip(".").lower() != output_format:
+            _warn(
+                f"Job {idx}: output extension {base.suffix} does not match output-format {output_format}."
+            )
+        base = out_dir / base.name
+    else:
+        slug = _slugify(prompt[:80])
+        base = out_dir / f"{idx:03d}-{slug}{ext}"
+
+    if n == 1:
+        return [base]
+    return [base.with_name(f"{base.stem}-{i}{base.suffix}") for i in range(1, n + 1)]
+
+
+def _extract_retry_after_seconds(exc: Exception) -> Optional[float]:
+    # Best-effort: openai SDK errors vary by version. Prefer a conservative fallback.
+    for attr in ("retry_after", "retry_after_seconds"):
+        val = getattr(exc, attr, None)
+        if isinstance(val, (int, float)) and val >= 0:
+            return float(val)
+    msg = str(exc)
+    m = re.search(r"retry[- ]after[:= ]+([0-9]+(?:\\.[0-9]+)?)", msg, re.IGNORECASE)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+
+def _is_rate_limit_error(exc: Exception) -> bool:
+    name = exc.__class__.__name__.lower()
+    if "ratelimit" in name or "rate_limit" in name:
+        return True
+    msg = str(exc).lower()
+    return "429" in msg or "rate limit" in msg or "too many requests" in msg
+
+
+def _is_transient_error(exc: Exception) -> bool:
+    if _is_rate_limit_error(exc):
+        return True
+    name = exc.__class__.__name__.lower()
+    if "timeout" in name or "timedout" in name or "tempor" in name:
+        return True
+    msg = str(exc).lower()
+    return "timeout" in msg or "timed out" in msg or "connection reset" in msg
+
+
+async def _generate_one_with_retries(
+    client: Any,
+    payload: Dict[str, Any],
+    *,
+    attempts: int,
+    job_label: str,
+) -> Any:
+    last_exc: Optional[Exception] = None
+    for attempt in range(1, attempts + 1):
+        try:
+            return await client.images.generate(**payload)
+        except Exception as exc:
+            last_exc = exc
+            if not _is_transient_error(exc):
+                raise
+            if attempt == attempts:
+                raise
+            sleep_s = _extract_retry_after_seconds(exc)
+            if sleep_s is None:
+                sleep_s = min(60.0, 2.0**attempt)
+            print(
+                f"{job_label} attempt {attempt}/{attempts} failed ({exc.__class__.__name__}); retrying in {sleep_s:.1f}s",
+                file=sys.stderr,
+            )
+            await asyncio.sleep(sleep_s)
+    raise last_exc or RuntimeError("unknown error")
+
+
+async def _run_generate_batch(args: argparse.Namespace) -> int:
+    jobs = _read_jobs_jsonl(args.input)
+    out_dir = Path(args.out_dir)
+
+    base_fields = _fields_from_args(args)
+    base_payload = {
+        "model": args.model,
+        "n": args.n,
+        "size": args.size,
+        "quality": args.quality,
+        "background": args.background,
+        "output_format": args.output_format,
+        "output_compression": args.output_compression,
+        "moderation": args.moderation,
+    }
+
+    if args.dry_run:
+        for i, job in enumerate(jobs, start=1):
+            prompt = str(job["prompt"]).strip()
+            fields = _merge_non_null(base_fields, job.get("fields", {}))
+            # Allow flat job keys as well (use_case, scene, etc.)
+            fields = _merge_non_null(
+                fields, {k: job.get(k) for k in base_fields.keys()}
+            )
+            augmented = _augment_prompt_fields(args.augment, prompt, fields)
+
+            job_payload = dict(base_payload)
+            job_payload["prompt"] = augmented
+            job_payload = _merge_non_null(
+                job_payload, {k: job.get(k) for k in base_payload.keys()}
+            )
+            job_payload = {k: v for k, v in job_payload.items() if v is not None}
+
+            _validate_generate_payload(job_payload)
+            effective_output_format = _normalize_output_format(
+                job_payload.get("output_format")
+            )
+            _validate_transparency(
+                job_payload.get("background"), effective_output_format
+            )
+            job_payload["output_format"] = effective_output_format
+
+            n = int(job_payload.get("n", 1))
+            outputs = _job_output_paths(
+                out_dir=out_dir,
+                output_format=effective_output_format,
+                idx=i,
+                prompt=prompt,
+                n=n,
+                explicit_out=job.get("out"),
+            )
+            downscaled = None
+            if args.downscale_max_dim is not None:
+                downscaled = [
+                    str(_derive_downscale_path(p, args.downscale_suffix))
+                    for p in outputs
+                ]
+            _print_request(
+                {
+                    "endpoint": "/v1/images/generations",
+                    "job": i,
+                    "outputs": [str(p) for p in outputs],
+                    "outputs_downscaled": downscaled,
+                    **job_payload,
+                }
+            )
+        return 0
+
+    client = _create_async_client()
+    sem = asyncio.Semaphore(args.concurrency)
+
+    any_failed = False
+
+    async def run_job(i: int, job: Dict[str, Any]) -> Tuple[int, Optional[str]]:
+        nonlocal any_failed
+        prompt = str(job["prompt"]).strip()
+        job_label = f"[job {i}/{len(jobs)}]"
+
+        fields = _merge_non_null(base_fields, job.get("fields", {}))
+        fields = _merge_non_null(fields, {k: job.get(k) for k in base_fields.keys()})
+        augmented = _augment_prompt_fields(args.augment, prompt, fields)
+
+        payload = dict(base_payload)
+        payload["prompt"] = augmented
+        payload = _merge_non_null(payload, {k: job.get(k) for k in base_payload.keys()})
+        payload = {k: v for k, v in payload.items() if v is not None}
+
+        n = int(payload.get("n", 1))
+        _validate_generate_payload(payload)
+        effective_output_format = _normalize_output_format(payload.get("output_format"))
+        _validate_transparency(payload.get("background"), effective_output_format)
+        payload["output_format"] = effective_output_format
+        outputs = _job_output_paths(
+            out_dir=out_dir,
+            output_format=effective_output_format,
+            idx=i,
+            prompt=prompt,
+            n=n,
+            explicit_out=job.get("out"),
+        )
+        try:
+            async with sem:
+                print(f"{job_label} starting", file=sys.stderr)
+                started = time.time()
+                result = await _generate_one_with_retries(
+                    client,
+                    payload,
+                    attempts=args.max_attempts,
+                    job_label=job_label,
+                )
+                elapsed = time.time() - started
+                print(f"{job_label} completed in {elapsed:.1f}s", file=sys.stderr)
+            images = [item.b64_json for item in result.data]
+            _decode_write_and_downscale(
+                images,
+                outputs,
+                force=args.force,
+                downscale_max_dim=args.downscale_max_dim,
+                downscale_suffix=args.downscale_suffix,
+                output_format=effective_output_format,
+            )
+            return i, None
+        except Exception as exc:
+            any_failed = True
+            print(f"{job_label} failed: {exc}", file=sys.stderr)
+            if args.fail_fast:
+                raise
+            return i, str(exc)
+
+    tasks = [
+        asyncio.create_task(run_job(i, job)) for i, job in enumerate(jobs, start=1)
+    ]
+
+    try:
+        await asyncio.gather(*tasks)
+    except Exception:
+        for t in tasks:
+            if not t.done():
+                t.cancel()
+        raise
+
+    return 1 if any_failed else 0
+
+
+def _generate_batch(args: argparse.Namespace) -> None:
+    exit_code = asyncio.run(_run_generate_batch(args))
+    if exit_code:
+        raise SystemExit(exit_code)
+
+
+def _generate(args: argparse.Namespace) -> None:
+    prompt = _read_prompt(args.prompt, args.prompt_file)
+    prompt = _augment_prompt(args, prompt)
+
+    payload = {
+        "model": args.model,
+        "prompt": prompt,
+        "n": args.n,
+        "size": args.size,
+        "quality": args.quality,
+        "background": args.background,
+        "output_format": args.output_format,
+        "output_compression": args.output_compression,
+        "moderation": args.moderation,
+    }
+    payload = {k: v for k, v in payload.items() if v is not None}
+
+    output_format = _normalize_output_format(args.output_format)
+    _validate_transparency(args.background, output_format)
+    payload["output_format"] = output_format
+    output_paths = _build_output_paths(args.out, output_format, args.n, args.out_dir)
+    downscaled = None
+    if args.downscale_max_dim is not None:
+        downscaled = [
+            str(_derive_downscale_path(p, args.downscale_suffix)) for p in output_paths
+        ]
+
+    if args.dry_run:
+        _print_request(
+            {
+                "endpoint": "/v1/images/generations",
+                "outputs": [str(p) for p in output_paths],
+                "outputs_downscaled": downscaled,
+                **payload,
+            }
+        )
+        return
+
+    print(
+        "Calling Image API (generation). This can take up to a couple of minutes.",
+        file=sys.stderr,
+    )
+    started = time.time()
+    client = _create_client()
+    result = client.images.generate(**payload)
+    elapsed = time.time() - started
+    print(f"Generation completed in {elapsed:.1f}s.", file=sys.stderr)
+
+    images = [item.b64_json for item in result.data]
+    _decode_write_and_downscale(
+        images,
+        output_paths,
+        force=args.force,
+        downscale_max_dim=args.downscale_max_dim,
+        downscale_suffix=args.downscale_suffix,
+        output_format=output_format,
+    )
+
+
+def _edit(args: argparse.Namespace) -> None:
+    prompt = _read_prompt(args.prompt, args.prompt_file)
+    prompt = _augment_prompt(args, prompt)
+
+    image_paths = _check_image_paths(args.image)
+    mask_path = Path(args.mask) if args.mask else None
+    if mask_path:
+        if not mask_path.exists():
+            _die(f"Mask file not found: {mask_path}")
+        if mask_path.suffix.lower() != ".png":
+            _warn(f"Mask should be a PNG with an alpha channel: {mask_path}")
+        if mask_path.stat().st_size > MAX_IMAGE_BYTES:
+            _warn(f"Mask exceeds 50MB limit: {mask_path}")
+
+    payload = {
+        "model": args.model,
+        "prompt": prompt,
+        "n": args.n,
+        "size": args.size,
+        "quality": args.quality,
+        "background": args.background,
+        "output_format": args.output_format,
+        "output_compression": args.output_compression,
+        "input_fidelity": args.input_fidelity,
+        "moderation": args.moderation,
+    }
+    payload = {k: v for k, v in payload.items() if v is not None}
+
+    output_format = _normalize_output_format(args.output_format)
+    _validate_transparency(args.background, output_format)
+    payload["output_format"] = output_format
+    _validate_input_fidelity(args.input_fidelity)
+    output_paths = _build_output_paths(args.out, output_format, args.n, args.out_dir)
+    downscaled = None
+    if args.downscale_max_dim is not None:
+        downscaled = [
+            str(_derive_downscale_path(p, args.downscale_suffix)) for p in output_paths
+        ]
+
+    if args.dry_run:
+        payload_preview = dict(payload)
+        payload_preview["image"] = [str(p) for p in image_paths]
+        if mask_path:
+            payload_preview["mask"] = str(mask_path)
+        _print_request(
+            {
+                "endpoint": "/v1/images/edits",
+                "outputs": [str(p) for p in output_paths],
+                "outputs_downscaled": downscaled,
+                **payload_preview,
+            }
+        )
+        return
+
+    print(
+        f"Calling Image API (edit) with {len(image_paths)} image(s).",
+        file=sys.stderr,
+    )
+    started = time.time()
+    client = _create_client()
+
+    with _open_files(image_paths) as image_files, _open_mask(mask_path) as mask_file:
+        request = dict(payload)
+        request["image"] = image_files if len(image_files) > 1 else image_files[0]
+        if mask_file is not None:
+            request["mask"] = mask_file
+        result = client.images.edit(**request)
+
+    elapsed = time.time() - started
+    print(f"Edit completed in {elapsed:.1f}s.", file=sys.stderr)
+    images = [item.b64_json for item in result.data]
+    _decode_write_and_downscale(
+        images,
+        output_paths,
+        force=args.force,
+        downscale_max_dim=args.downscale_max_dim,
+        downscale_suffix=args.downscale_suffix,
+        output_format=output_format,
+    )
+
+
+def _open_files(paths: List[Path]):
+    return _FileBundle(paths)
+
+
+def _open_mask(mask_path: Optional[Path]):
+    if mask_path is None:
+        return _NullContext()
+    return _SingleFile(mask_path)
+
+
+class _NullContext:
+    def __enter__(self):
+        return None
+
+    def __exit__(self, exc_type, exc, tb):
+        return False
+
+
+class _SingleFile:
+    def __init__(self, path: Path):
+        self._path = path
+        self._handle = None
+
+    def __enter__(self):
+        self._handle = self._path.open("rb")
+        return self._handle
+
+    def __exit__(self, exc_type, exc, tb):
+        if self._handle:
+            try:
+                self._handle.close()
+            except Exception:
+                pass
+        return False
+
+
+class _FileBundle:
+    def __init__(self, paths: List[Path]):
+        self._paths = paths
+        self._handles: List[object] = []
+
+    def __enter__(self):
+        self._handles = [p.open("rb") for p in self._paths]
+        return self._handles
+
+    def __exit__(self, exc_type, exc, tb):
+        for handle in self._handles:
+            try:
+                handle.close()
+            except Exception:
+                pass
+        return False
+
+
+def _add_shared_args(parser: argparse.ArgumentParser) -> None:
+    parser.add_argument("--model", default=DEFAULT_MODEL)
+    parser.add_argument("--prompt")
+    parser.add_argument("--prompt-file")
+    parser.add_argument("--n", type=int, default=1)
+    parser.add_argument("--size", default=DEFAULT_SIZE)
+    parser.add_argument("--quality", default=DEFAULT_QUALITY)
+    parser.add_argument("--background")
+    parser.add_argument("--output-format")
+    parser.add_argument("--output-compression", type=int)
+    parser.add_argument("--moderation")
+    parser.add_argument("--out", default=DEFAULT_OUTPUT_PATH)
+    parser.add_argument("--out-dir")
+    parser.add_argument("--force", action="store_true")
+    parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument("--augment", dest="augment", action="store_true")
+    parser.add_argument("--no-augment", dest="augment", action="store_false")
+    parser.set_defaults(augment=True)
+
+    # Prompt augmentation hints
+    parser.add_argument("--use-case")
+    parser.add_argument("--scene")
+    parser.add_argument("--subject")
+    parser.add_argument("--style")
+    parser.add_argument("--composition")
+    parser.add_argument("--lighting")
+    parser.add_argument("--palette")
+    parser.add_argument("--materials")
+    parser.add_argument("--text")
+    parser.add_argument("--constraints")
+    parser.add_argument("--negative")
+
+    # Post-processing (optional): generate an additional downscaled copy for fast web loading.
+    parser.add_argument("--downscale-max-dim", type=int)
+    parser.add_argument("--downscale-suffix", default=DEFAULT_DOWNSCALE_SUFFIX)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Fallback CLI for explicit image generation or editing via GPT Image models"
+    )
+    subparsers = parser.add_subparsers(dest="command", required=True)
+
+    gen_parser = subparsers.add_parser("generate", help="Create a new image")
+    _add_shared_args(gen_parser)
+    gen_parser.set_defaults(func=_generate)
+
+    batch_parser = subparsers.add_parser(
+        "generate-batch",
+        help="Generate multiple prompts concurrently (JSONL input)",
+    )
+    _add_shared_args(batch_parser)
+    batch_parser.add_argument(
+        "--input", required=True, help="Path to JSONL file (one job per line)"
+    )
+    batch_parser.add_argument("--concurrency", type=int, default=DEFAULT_CONCURRENCY)
+    batch_parser.add_argument("--max-attempts", type=int, default=3)
+    batch_parser.add_argument("--fail-fast", action="store_true")
+    batch_parser.set_defaults(func=_generate_batch)
+
+    edit_parser = subparsers.add_parser("edit", help="Edit an existing image")
+    _add_shared_args(edit_parser)
+    edit_parser.add_argument("--image", action="append", required=True)
+    edit_parser.add_argument("--mask")
+    edit_parser.add_argument("--input-fidelity")
+    edit_parser.set_defaults(func=_edit)
+
+    args = parser.parse_args()
+    if args.n < 1 or args.n > 10:
+        _die("--n must be between 1 and 10")
+    if getattr(args, "concurrency", 1) < 1 or getattr(args, "concurrency", 1) > 25:
+        _die("--concurrency must be between 1 and 25")
+    if getattr(args, "max_attempts", 3) < 1 or getattr(args, "max_attempts", 3) > 10:
+        _die("--max-attempts must be between 1 and 10")
+    if args.output_compression is not None and not (
+        0 <= args.output_compression <= 100
+    ):
+        _die("--output-compression must be between 0 and 100")
+    if args.command == "generate-batch" and not args.out_dir:
+        _die("generate-batch requires --out-dir")
+    if (
+        getattr(args, "downscale_max_dim", None) is not None
+        and args.downscale_max_dim < 1
+    ):
+        _die("--downscale-max-dim must be >= 1")
+
+    _validate_model(args.model)
+    _validate_size(args.size, args.model)
+    _validate_quality(args.quality)
+    _validate_background(args.background)
+    _validate_model_specific_options(
+        model=args.model,
+        background=args.background,
+        input_fidelity=getattr(args, "input_fidelity", None),
+    )
+    _ensure_api_key(args.dry_run)
+
+    args.func(args)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

20261001T054755.827592Z_eval/episode-0011/runtime_uy_v15n7/codex/skills/.system/imagegen/scripts/remove_chroma_key.py

@@ -0,0 +1,449 @@
+#!/usr/bin/env python3
+"""Remove a solid chroma-key background from an image.
+
+This helper supports the imagegen skill's built-in-first transparent workflow:
+generate an image on a flat key color, then convert that key color to alpha.
+"""
+
+from __future__ import annotations
+
+import argparse
+from io import BytesIO
+from pathlib import Path
+import re
+from statistics import median
+import sys
+from typing import Tuple
+
+
+Color = Tuple[int, int, int]
+KEY_DOMINANCE_THRESHOLD = 16.0
+ALPHA_NOISE_FLOOR = 8
+
+
+def _die(message: str, code: int = 1) -> None:
+    print(f"Error: {message}", file=sys.stderr)
+    raise SystemExit(code)
+
+
+def _dependency_hint(package: str) -> str:
+    return (
+        "Activate the repo-selected environment first, then install it with "
+        f"`uv pip install {package}`. If this repo uses a local virtualenv, start with "
+        "`source .venv/bin/activate`; otherwise use this repo's configured shared fallback "
+        "environment."
+    )
+
+
+def _load_pillow():
+    try:
+        from PIL import Image, ImageFilter
+    except ImportError:
+        _die(f"Pillow is required for chroma-key removal. {_dependency_hint('pillow')}")
+    return Image, ImageFilter
+
+
+def _parse_key_color(raw: str) -> Color:
+    value = raw.strip()
+    match = re.fullmatch(r"#?([0-9a-fA-F]{6})", value)
+    if not match:
+        _die("key color must be a hex RGB value like #00ff00.")
+    hex_value = match.group(1)
+    return (
+        int(hex_value[0:2], 16),
+        int(hex_value[2:4], 16),
+        int(hex_value[4:6], 16),
+    )
+
+
+def _validate_args(args: argparse.Namespace) -> None:
+    if args.tolerance < 0 or args.tolerance > 255:
+        _die("--tolerance must be between 0 and 255.")
+    if args.transparent_threshold < 0 or args.transparent_threshold > 255:
+        _die("--transparent-threshold must be between 0 and 255.")
+    if args.opaque_threshold < 0 or args.opaque_threshold > 255:
+        _die("--opaque-threshold must be between 0 and 255.")
+    if args.soft_matte and args.transparent_threshold >= args.opaque_threshold:
+        _die("--transparent-threshold must be lower than --opaque-threshold.")
+    if args.edge_feather < 0 or args.edge_feather > 64:
+        _die("--edge-feather must be between 0 and 64.")
+    if args.edge_contract < 0 or args.edge_contract > 16:
+        _die("--edge-contract must be between 0 and 16.")
+
+    src = Path(args.input)
+    if not src.exists():
+        _die(f"Input image not found: {src}")
+
+    out = Path(args.out)
+    if out.exists() and not args.force:
+        _die(f"Output already exists: {out} (use --force to overwrite)")
+
+    if out.suffix.lower() not in {".png", ".webp"}:
+        _die("--out must end in .png or .webp so the alpha channel is preserved.")
+
+
+def _channel_distance(a: Color, b: Color) -> int:
+    return max(abs(a[0] - b[0]), abs(a[1] - b[1]), abs(a[2] - b[2]))
+
+
+def _clamp_channel(value: float) -> int:
+    return max(0, min(255, int(round(value))))
+
+
+def _smoothstep(value: float) -> float:
+    value = max(0.0, min(1.0, value))
+    return value * value * (3.0 - 2.0 * value)
+
+
+def _soft_alpha(
+    distance: int, transparent_threshold: float, opaque_threshold: float
+) -> int:
+    if distance <= transparent_threshold:
+        return 0
+    if distance >= opaque_threshold:
+        return 255
+    ratio = (float(distance) - transparent_threshold) / (
+        opaque_threshold - transparent_threshold
+    )
+    return _clamp_channel(255.0 * _smoothstep(ratio))
+
+
+def _dominance_alpha(rgb: Color, key: Color) -> int:
+    spill_channels = _spill_channels(key)
+    if not spill_channels:
+        return 255
+
+    channels = [float(value) for value in rgb]
+    non_spill = [idx for idx in range(3) if idx not in spill_channels]
+    key_strength = (
+        min(channels[idx] for idx in spill_channels)
+        if len(spill_channels) > 1
+        else channels[spill_channels[0]]
+    )
+    non_key_strength = max((channels[idx] for idx in non_spill), default=0.0)
+    dominance = key_strength - non_key_strength
+    if dominance <= 0:
+        return 255
+
+    denominator = max(1.0, float(max(key)) - non_key_strength)
+    alpha = 1.0 - min(1.0, dominance / denominator)
+    return _clamp_channel(alpha * 255.0)
+
+
+def _spill_channels(key: Color) -> list[int]:
+    key_max = max(key)
+    if key_max < 128:
+        return []
+    return [
+        idx for idx, value in enumerate(key) if value >= key_max - 16 and value >= 128
+    ]
+
+
+def _key_channel_dominance(rgb: Color, key: Color) -> float:
+    spill_channels = _spill_channels(key)
+    if not spill_channels:
+        return 0.0
+
+    channels = [float(value) for value in rgb]
+    non_spill = [idx for idx in range(3) if idx not in spill_channels]
+    key_strength = (
+        min(channels[idx] for idx in spill_channels)
+        if len(spill_channels) > 1
+        else channels[spill_channels[0]]
+    )
+    non_key_strength = max((channels[idx] for idx in non_spill), default=0.0)
+    return key_strength - non_key_strength
+
+
+def _looks_key_colored(rgb: Color, key: Color, distance: int) -> bool:
+    if distance <= 32:
+        return True
+
+    spill_channels = _spill_channels(key)
+    if not spill_channels:
+        return True
+
+    return _key_channel_dominance(rgb, key) >= KEY_DOMINANCE_THRESHOLD
+
+
+def _cleanup_spill(rgb: Color, key: Color, alpha: int = 255) -> Color:
+    if alpha >= 252:
+        return rgb
+
+    spill_channels = _spill_channels(key)
+    if not spill_channels:
+        return rgb
+
+    channels = [float(value) for value in rgb]
+    non_spill = [idx for idx in range(3) if idx not in spill_channels]
+    if non_spill:
+        anchor = max(channels[idx] for idx in non_spill)
+        cap = max(0.0, anchor - 1.0)
+        for idx in spill_channels:
+            if channels[idx] > cap:
+                channels[idx] = cap
+
+    return (
+        _clamp_channel(channels[0]),
+        _clamp_channel(channels[1]),
+        _clamp_channel(channels[2]),
+    )
+
+
+def _apply_alpha_to_image(
+    image,
+    *,
+    key: Color,
+    tolerance: int,
+    spill_cleanup: bool,
+    soft_matte: bool,
+    transparent_threshold: float,
+    opaque_threshold: float,
+) -> int:
+    pixels = image.load()
+    width, height = image.size
+    transparent = 0
+
+    for y in range(height):
+        for x in range(width):
+            red, green, blue, alpha = pixels[x, y]
+            rgb = (red, green, blue)
+            distance = _channel_distance(rgb, key)
+            key_like = _looks_key_colored(rgb, key, distance)
+            output_alpha = (
+                min(
+                    _soft_alpha(distance, transparent_threshold, opaque_threshold),
+                    _dominance_alpha(rgb, key),
+                )
+                if soft_matte and key_like
+                else (0 if distance <= tolerance else 255)
+            )
+            output_alpha = int(round(output_alpha * (alpha / 255.0)))
+            if 0 < output_alpha <= ALPHA_NOISE_FLOOR:
+                output_alpha = 0
+
+            if output_alpha == 0:
+                pixels[x, y] = (0, 0, 0, 0)
+                transparent += 1
+                continue
+
+            if spill_cleanup and key_like:
+                red, green, blue = _cleanup_spill(rgb, key, output_alpha)
+            pixels[x, y] = (red, green, blue, output_alpha)
+
+    return transparent
+
+
+def _contract_alpha(image, pixels: int):
+    if pixels == 0:
+        return image
+
+    _, ImageFilter = _load_pillow()
+    alpha = image.getchannel("A")
+    for _ in range(pixels):
+        alpha = alpha.filter(ImageFilter.MinFilter(3))
+    image.putalpha(alpha)
+    return image
+
+
+def _apply_edge_feather(image, radius: float):
+    if radius == 0:
+        return image
+
+    _, ImageFilter = _load_pillow()
+    alpha = image.getchannel("A")
+    alpha = alpha.filter(ImageFilter.GaussianBlur(radius=radius))
+    image.putalpha(alpha)
+    return image
+
+
+def _encode_image(image, output_format: str) -> bytes:
+    out = BytesIO()
+    image.save(out, format=output_format.upper())
+    return out.getvalue()
+
+
+def _alpha_counts(image) -> tuple[int, int, int]:
+    pixels = image.load()
+    width, height = image.size
+    total = 0
+    transparent = 0
+    partial = 0
+
+    for y in range(height):
+        for x in range(width):
+            alpha = pixels[x, y][3]
+            total += 1
+            if alpha == 0:
+                transparent += 1
+            elif alpha < 255:
+                partial += 1
+
+    return total, transparent, partial
+
+
+def _sample_border_key(image, mode: str) -> Color:
+    width, height = image.size
+    pixels = image.load()
+    samples: list[Color] = []
+
+    if mode == "corners":
+        patch = max(1, min(width, height, 12))
+        boxes = [
+            (0, 0, patch, patch),
+            (width - patch, 0, width, patch),
+            (0, height - patch, patch, height),
+            (width - patch, height - patch, width, height),
+        ]
+        for left, top, right, bottom in boxes:
+            for y in range(top, bottom):
+                for x in range(left, right):
+                    red, green, blue = pixels[x, y][:3]
+                    samples.append((red, green, blue))
+    else:
+        band = max(1, min(width, height, 6))
+        step = max(1, min(width, height) // 256)
+        for x in range(0, width, step):
+            for y in range(band):
+                red, green, blue = pixels[x, y][:3]
+                samples.append((red, green, blue))
+                red, green, blue = pixels[x, height - 1 - y][:3]
+                samples.append((red, green, blue))
+        for y in range(0, height, step):
+            for x in range(band):
+                red, green, blue = pixels[x, y][:3]
+                samples.append((red, green, blue))
+                red, green, blue = pixels[width - 1 - x, y][:3]
+                samples.append((red, green, blue))
+
+    if not samples:
+        _die("Could not sample background key color from image border.")
+
+    return (
+        int(round(median(sample[0] for sample in samples))),
+        int(round(median(sample[1] for sample in samples))),
+        int(round(median(sample[2] for sample in samples))),
+    )
+
+
+def _remove_chroma_key(args: argparse.Namespace) -> None:
+    Image, _ = _load_pillow()
+    src = Path(args.input)
+    out = Path(args.out)
+
+    with Image.open(src) as image:
+        rgba = image.convert("RGBA")
+    key = (
+        _sample_border_key(rgba, args.auto_key)
+        if args.auto_key != "none"
+        else _parse_key_color(args.key_color)
+    )
+
+    transparent = _apply_alpha_to_image(
+        rgba,
+        key=key,
+        tolerance=args.tolerance,
+        spill_cleanup=args.spill_cleanup,
+        soft_matte=args.soft_matte,
+        transparent_threshold=args.transparent_threshold,
+        opaque_threshold=args.opaque_threshold,
+    )
+    rgba = _contract_alpha(rgba, args.edge_contract)
+    rgba = _apply_edge_feather(rgba, args.edge_feather)
+
+    total, transparent_after, partial_after = _alpha_counts(rgba)
+
+    out.parent.mkdir(parents=True, exist_ok=True)
+    output_format = "PNG" if out.suffix.lower() == ".png" else "WEBP"
+    out.write_bytes(_encode_image(rgba, output_format))
+
+    print(f"Wrote {out}")
+    print(f"Key color: #{key[0]:02x}{key[1]:02x}{key[2]:02x}")
+    print(f"Transparent pixels: {transparent_after}/{total}")
+    print(f"Partially transparent pixels: {partial_after}/{total}")
+    if transparent == 0:
+        print(
+            "Warning: no pixels matched the key color before feathering.",
+            file=sys.stderr,
+        )
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Remove a solid chroma-key background and write an image with alpha."
+    )
+    parser.add_argument("--input", required=True, help="Input image path.")
+    parser.add_argument("--out", required=True, help="Output .png or .webp path.")
+    parser.add_argument(
+        "--key-color",
+        default="#00ff00",
+        help="Hex RGB key color to remove, for example #00ff00.",
+    )
+    parser.add_argument(
+        "--tolerance",
+        type=int,
+        default=12,
+        help="Hard-key per-channel tolerance for matching the key color, 0-255.",
+    )
+    parser.add_argument(
+        "--auto-key",
+        choices=["none", "corners", "border"],
+        default="none",
+        help="Sample the key color from image corners or border instead of --key-color.",
+    )
+    parser.add_argument(
+        "--soft-matte",
+        action="store_true",
+        help="Use a smooth alpha ramp between transparent and opaque thresholds.",
+    )
+    parser.add_argument(
+        "--transparent-threshold",
+        type=float,
+        default=12.0,
+        help="Soft-matte distance at or below which pixels become fully transparent.",
+    )
+    parser.add_argument(
+        "--opaque-threshold",
+        type=float,
+        default=96.0,
+        help="Soft-matte distance at or above which pixels become fully opaque.",
+    )
+    parser.add_argument(
+        "--edge-feather",
+        type=float,
+        default=0.0,
+        help="Optional alpha blur radius for softened edges, 0-64.",
+    )
+    parser.add_argument(
+        "--edge-contract",
+        type=int,
+        default=0,
+        help="Shrink the visible alpha matte by this many pixels before feathering.",
+    )
+    parser.add_argument(
+        "--spill-cleanup",
+        dest="spill_cleanup",
+        action="store_true",
+        help="Reduce obvious key-color spill on opaque pixels.",
+    )
+    parser.add_argument(
+        "--despill",
+        dest="spill_cleanup",
+        action="store_true",
+        help="Alias for --spill-cleanup; decontaminate key-color edge spill.",
+    )
+    parser.add_argument(
+        "--force", action="store_true", help="Overwrite an existing output file."
+    )
+    return parser
+
+
+def main() -> None:
+    parser = _build_parser()
+    args = parser.parse_args()
+    _validate_args(args)
+    _remove_chroma_key(args)
+
+
+if __name__ == "__main__":
+    main()

20261001T054755.827592Z_eval/episode-0011/runtime_uy_v15n7/codex/skills/.system/openai-docs/LICENSE.txt

@@ -0,0 +1,201 @@
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+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.