Update model card

README.md

@@ -35,107 +35,100 @@ inference: false
 ```
 
 > **API-only.** This repository is the official Model Card & Documentation Hub for the `stealth-skeleton/skeleton-alpha` endpoint on OpenRouter. It does **not** contain downloadable model weights. The Apache-2.0 license applies to the documentation and configuration files in this repository.
+>
+> 👈 You are viewing **skeleton-alpha**. Jump to the Buyer's Guide below to compare all 6 models.
 
-## 💀 Overview: Crowd-RLHF Execution-Verified Coding, Math & Tool-Calling Engine
+## 🧠 Why stealth-skeleton exists: how our model thinks, reasons, and acts differently
 
-**Crowd-RLHF Execution-Verified Coding, Math & Tool-Calling Engine** routed over the Quad-Frontier Spine with Auto-Exacto tool-call grammar healing (>95%, self-reported). Anonymized execution-verified traces feed DPO/GRPO alignment.
+Single-pass LLMs share a common weakness: **autoregressive next-token prediction**. Left on their own, they can write plausible-sounding prose, get math wrong, go along with flawed user premises, break JSON tool schemas in long agent loops, and report high confidence when they are wrong.
 
-- **Execution-verified outputs**: code and math are run, not just eyeballed.
-- **Auto-Exacto grammar healing**: malformed tool-call JSON is repaired before it reaches you.
-- **Free**, 128K context, 16K max output.
+**stealth-skeleton is not a single next-token predictor.** It is a **Sovereign Cognitive & Mathematical Execution Spine**: an orchestration layer over four frontier models that thinks, reasons, and acts through a 5-stage architecture before returning an answer.
 
-**Best for:**
+### 1. Single-pass models guess math in prose. We execute 21,419 compiled operators across 26 reasoning layers (L01..L26).
 
-- Free daily coding agent in **OpenCode, Cline, Roo Code, Cursor, Aider**
-- Multi-turn function/tool calling & MCP orchestration
-- High-volume prototyping, unit test generation & symbolic math checks
+When you ask a standard LLM a quantitative, physics, optimization, or systems question, it predicts what a math equation *looks like* in text.
 
-## 🧾 Quick facts
+- **How stealth-skeleton thinks:** every query is mapped into a **288-bit contextual state mask** and routed through **26 specialized reasoning layers (L01..L26)** backed by **21,419 compiled mathematical operators** (Sinkhorn-Knopp optimal transport verified to 1.39e-16 L1 marginal conservation, Frisch-Waugh-Lovell orthogonal residualization < 1e-15, 36th-order Hou-Li spectral filters, and 753 graduate math & Navier-Stokes proofs).
+- **Why you care:** conservation laws, matrix marginals, and boundary conditions are computed and checked before the final answer is returned. The design goal is no placeholder formulas and no hand-waved arithmetic.
 
-| Property | Value |
-|---|---|
-| Tier | Free Preview |
-| Price (input / output per 1M tokens) | $0 / $0 (free) |
-| Context window | 128K tokens |
-| Max output | 16K tokens |
-| Data policy | Research preview |
-| Interface | OpenAI-compatible Chat Completions |
-| Weights | None: API-only |
+### 2. Single-pass models can be sycophants. We run a 4-voice adversarial council plus cellular sheaf cohomology.
 
-## 🕸️ 1. Quad-Frontier Sovereign Spine: how stealth-skeleton works under the hood
+Standard RLHF trains models to be agreeable. If your prompt contains a subtle bug, a bad architectural assumption, or a spurious correlation, a single model may happily build on top of it.
 
-Every `stealth-skeleton/*` endpoint orchestrates a 4-pillar frontier spine merged via **Theorem 7 Cellular Sheaf Laplacian Cohomology** (Dirichlet energy = 0.006686, H^0 global-section consensus) and **Stage D Positive-Part James-Stein Shrinkage** (|Δ log-odds| ≤ 0.35).
+- **How stealth-skeleton reasons:** instead of a single monologue, it routes complex decisions across the **Quad-Frontier Sovereign Spine** (Google Argon 4 2M, Anthropic Claude Fable 5 1M, Anthropic Claude Opus 5.5 1M, OpenAI Astra 512K) and convenes four orthogonal internal voices:
+  - **The Architect**: structural synthesis & long-horizon design
+  - **The Skeptic**: adversarial stress-testing & counter-examples
+  - **The Pragmatist**: runtime cost, latency & failure-mode engineering
+  - **The Critic**: enforcing an **80-rule misleading-heuristic firewall**
+- Disagreements across the four pillars are resolved mathematically via **Theorem 7 Cellular Sheaf Laplacian Cohomology** (Dirichlet energy = 0.006686, requiring a global H^0 section consensus) and **Positive-Part James-Stein Shrinkage** (|Δ log-odds| ≤ 0.35). If your premise is flawed, the design goal is for stealth-skeleton to explain why and propose a corrected architecture.
 
-| Frontier pillar | Model ID | Context window | Sheaf weight | Primary engine stages & capabilities |
-|---|---|---:|---:|---|
-| **Anthropic Claude Fable 5** | `anthropic/claude-fable-5` | 1,000,000 | 0.27 | **Sovereign flagship orchestrator:** Cellular Sheaf Cohomology semantic merger across 31 math domains, 4-Voice Adversarial Council Arbiter (Architect, Skeptic, Pragmatist, Critic), and Byzantine Generals 3m+1 Stage 5 Conjunction Gate quorum (N=16 rollouts). |
-| **Google Argon 4 (Gemini 4 Argon)** | `google/gemini-4-argon` | 2,000,000 | 0.26 | **2M-context corpus & situational synthesizer:** full 21,419-equation cross-repo dependency & dimensional audit across 26 reasoning layers (L01..L26), 3,152-scenario 288-bit situational context synthesis, and Vectorless PageIndex Hierarchical Tree RAG (N=8 rollouts). |
-| **Anthropic Claude Opus 5.5** | `anthropic/claude-opus-5.5` | 1,000,000 | 0.25 | **Adversarial theorem prover & spectral verifier:** Lean 4 / SymPy symbolic operator verification, 753 graduate monographs (BK-01..BK-25 + GAP-01..08), 32-run Navier-Stokes vorticity & Hou-Li 36th-order spectral filtering, and bare-metal C++20 STM32H7 (18.2 KB / 256 KB SRAM, 0.42 ms p99) verification (N=16 rollouts). |
-| **OpenAI Astra** | `openai/astra` | 512,000 | 0.22 | **Compute-optimal test-time scaling & conformal forecaster:** Snell et al. (ICLR 2025) test-time compute scaling (N = 4 → 16 → 64 rollouts), ASTRA learned reliability-diagram slice gating (0.0202 mfp / 0.0524 ASTRA), Romano CQR + Barber weighted conformal bounds across 6,747 resolutions, and Auto-Exacto schema-valid tool execution. |
-
-*Model names and IDs above belong to their respective owners. stealth-skeleton is an independent project and is not affiliated with, sponsored by, or endorsed by Google, Anthropic or OpenAI.*
+### 3. Single-pass models can overstate confidence. We enforce conformal calibration and pass discipline.
 
-## 🦴 2. The 6 stealth-skeleton models: what they are, what they do & when to use them
+Ask a standard LLM for a probability or risk estimate and it may pick a number that "sounds right" (70%, 85%, 95%) with little calibration to reality.
 
-| Model | Tier, pricing & limits | What it does | When to use it |
-|---|---|---|---|
-| 💀 **[skeleton-alpha](https://huggingface.co/stealth-skeleton/skeleton-alpha)** | **Free Preview**<br>$0 / $0 (free) per 1M<br>128K context, 16K max output | **Crowd-RLHF Execution-Verified Coding, Math & Tool-Calling Engine** routed over the Quad-Frontier Spine with Auto-Exacto tool-call grammar healing (>95%, self-reported). Anonymized execution-verified traces feed DPO/GRPO alignment. | • Free daily coding agent in **OpenCode, Cline, Roo Code, Cursor, Aider**<br>• Multi-turn function/tool calling & MCP orchestration<br>• High-volume prototyping, unit test generation & symbolic math checks |
-| 👑 **[skeleton-v1](https://huggingface.co/stealth-skeleton/skeleton-v1)** | **Flagship Production**<br>$1.50 / $6.00 per 1M<br>128K context, 32K max output | **Full Quad-Frontier Sovereign Flagship (Argon 4 + Fable 5 + Opus 5.5 + Astra)** with Cellular Sheaf Consensus and strict Zero Data Retention (ZDR). Also available as `skeleton-v1-fast` (`service_tier: "priority"`) and `batch`. | • Mission-critical production software engineering & multi-file refactoring<br>• Enterprise workloads requiring strict **Zero Data Retention (ZDR)**<br>• Complex system architecture, formal verification & deep research synthesis |
-| 📐 **[skeleton-math-pro](https://huggingface.co/stealth-skeleton/skeleton-math-pro)** | **Specialist Kernel**<br>$1.50 / $6.00 per 1M<br>128K context, 32K max output | **Opus 5.5 + Argon 4 Mathematical & Spectral Operator Kernel.** Executes all 21,419 wired equations across 26 layers (L01..L26), Sinkhorn-Knopp optimal transport (L1 residual 1.39e-16), FWL orthogonal projection (< 1e-15), Hou-Li 36th-order spectral filters, and 753 graduate math/Navier-Stokes proofs. | • Graduate mathematics, physics, PDEs/SDEs, and quantitative optimization<br>• Optimal transport, spectral graph theory, and numerical stability verification<br>• Auditing symbolic derivations with zero placeholder tolerance |
-| 🔮 **[skeleton-forecast](https://huggingface.co/stealth-skeleton/skeleton-forecast)** | **Specialist Forecaster**<br>$1.50 / $6.00 per 1M<br>128K context, 32K max output | **OpenAI Astra + Fable 5 Calibrated Probabilistic Forecaster.** Runs Snell test-time scaling (N=64), ASTRA reliability gating, Romano CQR + Barber non-exchangeable conformal intervals (91.06% empirical 90% coverage across 6,747 resolutions), Kadavath P(IK), and Satopaa-Tetlock log-odds extremizing. | • Calibrated probability estimation & Brier/CRPS optimization<br>• Conformal prediction intervals (80% / 90%) under distribution shift<br>• Quantitative decision support & risk-gated forecasting |
-| 🗣️ **[skeleton-council](https://huggingface.co/stealth-skeleton/skeleton-council)** | **Multi-Agent Arbiter**<br>$1.50 / $6.00 per 1M<br>128K context, 32K max output | **Claude Fable 5 4-Voice Adversarial Council & Sheaf Cohomology Arbiter.** Convenes Architect, Skeptic, Pragmatist, and Critic across all 4 frontier pillars with an 80-rule misleading-heuristic firewall and Byzantine 3m+1 quorum. | • High-stakes architecture reviews, security audits & go/no-go decisions<br>• Red-teaming plans, eliminating sycophancy & stress-testing assumptions<br>• Final conjunction-gate arbitration before deploying code or capital |
-| ⚡ **[skeleton-flash](https://huggingface.co/stealth-skeleton/skeleton-flash)** | **Low-Latency / High-Throughput**<br>$0.25 / $1.00 per 1M<br>128K context, 16K max output | **Google Argon 4 Vectorless PageIndex Tree RAG & High-Speed Synthesis Engine.** Traverses hierarchical ASTs across 48,609 research ledger items and 110,960 full-text chunks in sub-millisecond retrieval (0.504 ms). | • Real-time interactive chat, fast routing & structured extraction<br>• Large-corpus hierarchical RAG over books, PDFs, and repositories<br>• High-concurrency agent worker loops |
+- **How stealth-skeleton acts:** we combine **Snell et al. (ICLR 2025) compute-optimal test-time scaling (N = 4 → 16 → 64 rollouts)** with **Kadavath P(IK) epistemic self-knowledge**, **Satopaa-Tetlock log-odds extremizing**, and **Romano / Barber non-exchangeable conformalized quantile regression (CQR)**, reaching **91.06% empirical coverage on 90% intervals across 6,747 resolved outcomes** (self-reported).
+- **Pass discipline:** when epistemic uncertainty exceeds the signal threshold, stealth-skeleton is designed to flag the exact missing information instead of guessing.
 
-### 🧭 Quick decision guide: which model should I pick?
+### 4. Vectorless hierarchical PageIndex tree RAG: 0.504 ms AST navigation, no chunk amnesia.
 
-1. **Want a free daily-driver for coding, agents, or tool use?** → use 💀 [`stealth-skeleton/skeleton-alpha`](https://huggingface.co/stealth-skeleton/skeleton-alpha)
-2. **Need flagship coding + reasoning with strict Zero Data Retention (ZDR)?** → use 👑 [`stealth-skeleton/skeleton-v1`](https://huggingface.co/stealth-skeleton/skeleton-v1)
-3. **Solving hard proofs, optimal transport, linear algebra, PDEs, or numerical optimization?** → use 📐 [`stealth-skeleton/skeleton-math-pro`](https://huggingface.co/stealth-skeleton/skeleton-math-pro)
-4. **Need calibrated probabilities, Brier/CRPS optimization, or conformal prediction intervals?** → use 🔮 [`stealth-skeleton/skeleton-forecast`](https://huggingface.co/stealth-skeleton/skeleton-forecast)
-5. **Need an adversarial 4-voice design/security review that will not rubber-stamp your idea?** → use 🗣️ [`stealth-skeleton/skeleton-council`](https://huggingface.co/stealth-skeleton/skeleton-council)
-6. **Need maximum speed and low cost for RAG, extraction, or high-volume worker loops?** → use ⚡ [`stealth-skeleton/skeleton-flash`](https://huggingface.co/stealth-skeleton/skeleton-flash)
+Standard vector RAG chops your codebase and technical PDFs into arbitrary 500-token chunks, severing definitions in Chapter 2 from theorems in Chapter 9.
 
-### 👀 The whole family at a glance
+- **How stealth-skeleton retrieves:** **vectorless hierarchical PageIndex AST navigation** across 48,609 research ledger items and 110,960 full-text nodes, traversing document trees, cross-references, and call graphs the way a principal engineer reads a codebase (10/10 multi-hop recall in 0.504 ms on our internal test).
 
-| Model | In one line |
-|---|---|
-| 💀 **skeleton-alpha** 👈 you are here | Free. Fast. Execution-verified. |
-| 👑 [skeleton-v1](https://huggingface.co/stealth-skeleton/skeleton-v1) | The flagship. All four pillars. Strict zero retention. |
-| 📐 [skeleton-math-pro](https://huggingface.co/stealth-skeleton/skeleton-math-pro) | Graduate math, spectral filters, transport. |
-| 🔮 [skeleton-forecast](https://huggingface.co/stealth-skeleton/skeleton-forecast) | Calibrated forecasts with honest error bars. |
-| 🗣️ [skeleton-council](https://huggingface.co/stealth-skeleton/skeleton-council) | Four voices argue. A sheaf decides. |
-| ⚡ [skeleton-flash](https://huggingface.co/stealth-skeleton/skeleton-flash) | Quick retrieval. No vector database. |
+### 5. Auto-Exacto tool-call grammar healing (>95% first-turn tool reliability).
 
-## 📊 3. Head-to-head benchmarks: Quad-Frontier Spine vs. solo frontier pillars (35,119 items)
+One broken JSON bracket in a tool call can crash a 20-step coding run in **OpenCode, Cline, Roo Code, Cursor, or MCP**.
 
-**Self-reported.** All rows come from our internal harness on a private 35,119-item corpus. The solo-pillar rows are our own runs of each model on the same corpus, not vendor-published figures. Because the corpus is not public, these results cannot be reproduced externally and have not been independently verified. For Brier, lower is better.
+- **How stealth-skeleton acts:** our gateway runs **Auto-Exacto deterministic grammar healing** on every `tool_calls` payload before it leaves the wire, achieving >95% first-turn schema-valid tool execution (self-reported).
 
-| Model / architecture | 21,419 Wired Equations (L01..L26) | 3,152 Situational Scenarios | 2,784 Mind Chunks (48,609 Ledger) | 6,747 Forecasting & Conformal (Brier ↓) | 753 Graduate Math & NS32 Proofs | 264 External Academic & STM32H7 Kernels | 35,119-Item Composite (100-pt) |
-|---|---:|---:|---:|---:|---:|---:|---:|
-| **stealth-skeleton Quad-Frontier Spine (Argon 4 + Fable 5 + Opus 5.5 + Astra)** | **98.94%** | **97.68%** | **98.42%** | **91.25%** (0.1086 Brier) | **96.45%** | **86.20%** | **95.93 / 100** |
-| Google Argon 4 (`google/gemini-4-argon`, 2M-context solo pillar) | 94.80% | 95.10% | 96.80% | 88.90% (0.1142 Brier) | 93.20% | 84.10% | **92.45 / 100** |
-| Anthropic Claude Fable 5 (`anthropic/claude-fable-5`, 1M-context solo pillar) | 94.20% | 94.85% | 95.40% | 89.20% (0.1135 Brier) | 94.10% | 83.80% | **92.18 / 100** |
-| Anthropic Claude Opus 5.5 (`anthropic/claude-opus-5.5`, 1M-context solo pillar) | 95.10% | 93.40% | 94.60% | 88.10% (0.1158 Brier) | 95.30% | 84.50% | **91.94 / 100** |
-| OpenAI Astra (`openai/astra`, 512K-context solo pillar) | 93.85% | 92.90% | 94.10% | 90.40% (0.1104 Brier) | 93.80% | 83.20% | **91.62 / 100** |
+## 🕸️ Quad-Frontier Sovereign Spine architecture: the 4 engines inside stealth-skeleton
 
-### External 7-suite results (self-reported)
+| Frontier pillar | Pillar ID | Context window | Sheaf weight | Role inside the stealth-skeleton spine |
+|---|---|---:|---:|---|
+| **Anthropic Claude Fable 5** | `anthropic/claude-fable-5` | 1,000,000 | 0.27 | **Sovereign flagship orchestrator:** Cellular Sheaf Laplacian semantic merger across 31 math domains, 4-Voice Adversarial Council Arbiter, and Byzantine Generals 3m+1 Stage 5 Conjunction Gate quorum (N=16 rollouts). |
+| **Google Argon 4 (Gemini 4 Argon)** | `google/gemini-4-argon` | 2,000,000 | 0.26 | **2M-context corpus & situational synthesizer:** full 21,419-equation cross-repo dependency & dimensional audit across 26 reasoning layers (L01..L26), 3,152-scenario 288-bit situational synthesis, and Vectorless PageIndex Tree RAG (N=8 rollouts). |
+| **Anthropic Claude Opus 5.5** | `anthropic/claude-opus-5.5` | 1,000,000 | 0.25 | **Adversarial theorem prover & spectral verifier:** Lean 4 / SymPy symbolic operator verification, 753 graduate monographs & Navier-Stokes 32-run vorticity proofs, and bare-metal C++20 STM32H7 (18.2 KB / 256 KB SRAM, 0.42 ms p99) verification (N=16 rollouts). |
+| **OpenAI Astra** | `openai/astra` | 512,000 | 0.22 | **Compute-optimal test-time scaling & conformal forecaster:** Snell (ICLR 2025) test-time compute scaling (N = 4 → 16 → 64), ASTRA reliability-diagram slice gating (0.0202 mfp / 0.0524 ASTRA), Romano CQR + Barber conformal calibration, and Auto-Exacto tool execution. |
 
-Standalone external 7-suite 3-pass score: **86.12 / 100**.
+*Model names and IDs above belong to their respective owners. stealth-skeleton is an independent project and is not affiliated with, sponsored by, or endorsed by Google, Anthropic or OpenAI.*
 
-| Suite | Score |
-|---|---|
-| MATH-500 | 92.00% |
-| GSM8K | 96.00% |
-| HumanEval | 92.00% |
-| GPQA-Diamond | 73.33% |
-| MMLU-Pro | 86.00% |
-| Arena-Hard | 85.00% |
-| ForecastBench (Proper Brier Skill) | 78.52% (0.0969 Brier) |
-| **Composite (mean of 7)** | **86.12 / 100** |
+## 📊 Head-to-head benchmark leaderboard: Quad-Frontier Spine vs. solo frontier pillars (35,119 items, self-reported)
 
-Other internal figures: Sinkhorn-Knopp L1 marginal residual 1.39e-16 at 40 iterations (epsilon = 0.15); FWL orthogonal projection residual < 1e-15; Romano CQR 90.35% empirical out-of-sample coverage with 0.2337 Normal CRPS on an internal test set.
+**Self-reported.** All rows come from our internal harness on a private 35,119-item corpus. The solo-pillar rows are our own runs of each model on the same corpus, not vendor-published figures. Because the corpus is not public, these results cannot be reproduced externally and have not been independently verified. For Brier, lower is better.
 
-## 🚀 4. How to use the stealth-skeleton models
+| Model / sovereign architecture | 21,419 Wired Equations (L01..L26) | 3,152 Situational Scenarios | 2,784 Mind Chunks (48,609 Ledger) | 6,747 Forecasting & Conformal (Brier ↓) | 753 Graduate Math & NS32 Proofs | 264 External Academic & STM32H7 Kernels | 35,119-Item Composite (100-pt) |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| **stealth-skeleton Quad-Frontier Spine (Argon 4 + Fable 5 + Opus 5.5 + Astra)** | **98.94%** | **97.68%** | **98.42%** | **91.25%** (0.1086 Brier) | **96.45%** | **86.20%** | **95.93 / 100** |
+| **Google Argon 4** (`google/gemini-4-argon`, 2M-context solo pillar) | 94.80% | 95.10% | 96.80% | 88.90% (0.1142 Brier) | 93.20% | 84.10% | **92.45 / 100** |
+| **Anthropic Claude Fable 5** (`anthropic/claude-fable-5`, 1M-context solo pillar) | 94.20% | 94.85% | 95.40% | 89.20% (0.1135 Brier) | 94.10% | 83.80% | **92.18 / 100** |
+| **Anthropic Claude Opus 5.5** (`anthropic/claude-opus-5.5`, 1M-context solo pillar) | 95.10% | 93.40% | 94.60% | 88.10% (0.1158 Brier) | 95.30% | 84.50% | **91.94 / 100** |
+| **OpenAI Astra** (`openai/astra`, 512K-context solo pillar) | 93.85% | 92.90% | 94.10% | 90.40% (0.1104 Brier) | 93.80% | 83.20% | **91.62 / 100** |
+
+*External 7-suite 3-pass standalone score (self-reported): **86.12 / 100** across MATH-500 (92.00%), GSM8K (96.00%), HumanEval (92.00%), GPQA-Diamond (73.33%), MMLU-Pro (86.00%), Arena-Hard (85.00%), and ForecastBench (0.0969 Brier).*
+
+## 🧭 Which stealth-skeleton model should you use? (free test drive → production upgrade)
+
+1. 💀 **`stealth-skeleton/skeleton-alpha`** (FREE: $0 / $0 per 1M, 128K context, 16K max output)  👈 **you are here**
+   - **What it is:** Your **zero-cost daily driver** for agentic coding, math, and Auto-Exacto tool calling.
+   - **Why it's free:** anonymized, execution-verified mathematical and tool-calling traces feed our automated DPO/GRPO compiler to sharpen the spine.
+   - **When to use it:** Plug it into **OpenCode, Cline, Roo Code, Cursor, or Aider** right now to test the full architecture for free.
+2. 👑 **`stealth-skeleton/skeleton-v1`** ($1.50 / $6.00 per 1M, $0.30 cache read, 128K context, 32K max output, Strict ZDR)
+   - **What it is:** The **full-power Quad-Frontier Sovereign Flagship** with strict **Zero Data Retention (ZDR) on our side**: prompts and completions are not logged or trained on. Also supports `skeleton-v1-fast` (`service_tier: "priority"`) and `batch`.
+   - **When to use it:** Complex multi-file refactoring and production agent deployments that need maximum 4-pillar consensus accuracy and no retention on our side. Review the upstream-provider note under Data & Privacy before sending highly sensitive code.
+3. 📐 **`stealth-skeleton/skeleton-math-pro`** ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
+   - **What it is:** Dedicated **21,419-equation + 753 graduate math / Navier-Stokes + Sinkhorn-Knopp optimal transport** kernel (Opus 5.5 + Argon 4 weighted).
+   - **When to use it:** Formal proofs, PDEs/SDEs, convex optimization, spectral graph theory, and quantitative engineering where symbolic conservation laws must hold to 1e-16.
+4. 🔮 **`stealth-skeleton/skeleton-forecast`** ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
+   - **What it is:** Dedicated **Romano CQR + Barber weighted conformal + OpenAI Astra (N=64 Snell test-time scaling)** probabilistic forecaster.
+   - **When to use it:** Risk modeling, calibrated probability estimation, Brier/CRPS optimization, and 80% / 90% conformal prediction intervals under regime shift.
+5. 🗣️ **`stealth-skeleton/skeleton-council`** ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
+   - **What it is:** Dedicated **4-voice adversarial council (Architect, Skeptic, Pragmatist, Critic) + cellular sheaf cohomology arbiter** (Claude Fable 5 lead).
+   - **When to use it:** Red-teaming system designs, security audits, countering sycophancy, and go/no-go conjunction-gate decisions.
+6. ⚡ **`stealth-skeleton/skeleton-flash`** ($0.25 / $1.00 per 1M, 128K context, 16K max output, Strict ZDR)
+   - **What it is:** Ultra-low-latency **vectorless PageIndex hierarchical tree RAG** and high-throughput synthesis engine (Google Argon 4 fast path).
+   - **When to use it:** Real-time chat, sub-millisecond AST document retrieval, and high-concurrency subagent swarms.
+
+## 🚀 Start in 30 seconds (drop-in OpenAI-compatible SDK, CLI & coding agents)
 
 Model IDs: `stealth-skeleton/skeleton-alpha`, `skeleton-v1`, `skeleton-math-pro`, `skeleton-forecast`, `skeleton-council`, `skeleton-flash` via OpenRouter, once provider routing is active for your account. Swap the model ID in any example.
 
@@ -149,11 +142,18 @@ client = OpenAI(
     api_key="<YOUR_OPENROUTER_API_KEY>",
 )
 
+# Start FREE with "stealth-skeleton/skeleton-alpha", or upgrade to:
+# - "stealth-skeleton/skeleton-v1"        (Flagship Quad-Frontier Spine, Strict ZDR)
+# - "stealth-skeleton/skeleton-math-pro"  (21,419-Equation & Spectral Operator Kernel)
+# - "stealth-skeleton/skeleton-forecast"  (Romano CQR & ASTRA Conformal Forecaster)
+# - "stealth-skeleton/skeleton-council"   (4-Voice Adversarial Council & Sheaf Arbiter)
+# - "stealth-skeleton/skeleton-flash"     (Low-Latency PageIndex Tree RAG Engine)
+
 response = client.chat.completions.create(
     model="stealth-skeleton/skeleton-alpha",
     messages=[
-        {"role": "system", "content": "You are a rigorous mathematical and systems engineering assistant."},
-        {"role": "user", "content": "Derive the Sinkhorn-Knopp scaling update and verify row/column marginal conservation."},
+        {"role": "system", "content": "Route across the Quad-Frontier Sovereign Spine and verify all conservation laws."},
+        {"role": "user", "content": "Convene the 4-Voice Council to audit this architecture and verify Sinkhorn-Knopp L1 marginal convergence."},
     ],
     temperature=0.2,
     max_tokens=4096,
@@ -203,7 +203,7 @@ const client = new OpenAI({
 
 const completion = await client.chat.completions.create({
   model: "stealth-skeleton/skeleton-alpha",
-  messages: [{ role: "user", content: "Derive the Sinkhorn-Knopp scaling update and verify row/column marginal conservation." }],
+  messages: [{ role: "user", content: "Convene the 4-Voice Council to audit this architecture and verify Sinkhorn-Knopp L1 marginal convergence." }],
 });
 
 console.log(completion.choices[0]?.message?.content);
@@ -262,12 +262,12 @@ aider --model openrouter/stealth-skeleton/skeleton-alpha
 
 ## 🔒 Data & Privacy
 
-**Free community research preview.** Anonymized mathematical and tool-calling traces may be evaluated by our automated execution verifier for DPO/GRPO alignment. Please do not send confidential, personal or proprietary data to this endpoint. Need zero retention? Use [skeleton-v1](https://huggingface.co/stealth-skeleton/skeleton-v1).
+**Free community research preview.** Anonymized, execution-verified mathematical and tool-calling traces may be evaluated by our automated execution verifier for DPO/GRPO alignment. Please do not send confidential, personal or proprietary data to this endpoint. Need zero retention on our side? Use [skeleton-v1](https://huggingface.co/stealth-skeleton/skeleton-v1).
 
-**Upstream providers.** Requests are orchestrated across third-party frontier model APIs (see the spine table above), so prompt content may be transmitted to those providers. The retention statement above describes stealth-skeleton's own handling; upstream retention is governed by each provider's terms and agreements.
+**Upstream providers.** Requests are orchestrated across third-party frontier model APIs (see the spine table above), so prompt content may be transmitted to those providers. The retention statement above describes stealth-skeleton's own handling; upstream retention is governed by each provider's terms and agreements. Review those terms before sending highly sensitive data.
 
 ## ⚠️ Limitations
 
 - Behavior, limits, prices and availability may change without notice.
-- Benchmarks are self-reported only; validate on your own workloads.
+- Benchmarks and architecture figures are self-reported; validate on your own workloads.
 - Not intended for safety-critical, medical, legal or financial decisions.