README.md

---
pretty_name: FigmaTrace
license: cc-by-4.0
language:
- en
size_categories:
- 1K<n<10K
tags:
- gui-agents
- computer-use
- design
- figma
- trajectories
task_categories:
- image-text-to-text
---

# Dataset Card for FigmaTrace

FigmaTrace is a dataset of expert human Figma design workflows: 200+ hours of
screen-recorded work converted into 3,469 agent trajectories using a design
phase-based segmentation method. It is built to teach vision language models
the creative skills and decisions behind design work, not just the finished
artifact.

## Dataset Details

### Dataset Description

- **Curated by:** Patronus AI
- **Language(s):** English
- **License:** CC-BY-4.0

### Dataset Sources

- **Raw Assets:** [Google Drive](https://drive.google.com/drive/folders/1d7NQxjiAzALu3odUQSxbT6eO9czm96Oy?usp=drive_link)
- **Paper:** [FigmaTrace: Capturing Creative Nuances in Human Figma Design Workflows](https://cdn.patronus.ai/FigmaTrace.pdf)
- **Best fine-tuned model:** https://huggingface.co/PatronusAI/Qwen3.8-27B-Figmatrace-SFT

## Uses

### Direct Use

Supervised fine-tuning and evaluation of VLM-based GUI/design agents.

### Out-of-Scope Use

Trajectories carry no pre-annotated reasoning chains, so the dataset is not
suited for training reasoning-trace models without further annotation.

## Dataset Structure

- 126 long-horizon tasks across 8 designer workflow categories (pixel-perfect
  replication, responsive adaptation, theming with variables, sketch-to-Figma,
  flaw injection/repair, edge-content resilience, a11y remediation, prototype
  wiring), covering a 10-skill expert taxonomy.
- 3,469 trajectories: 2,883 training / 586 evaluation.
- Each trajectory is a sequence of action-frame pairs in the Playwright-MCP
  action space (e.g. `mouse_click`, `keyboard_type`), with `observe` probes
  inserted for input-free screen transitions.
- Trajectories carry phase labels from a closed 12-label vocabulary
  (e.g. `blocking_layout`, `componentising`, `refinement_polish`) and
  skill labels assigned by frequency.

![FigmaTrace 10-skill expert taxonomy](assets/skills_taxonomy.png)

## Dataset Creation

### Curation Rationale

Existing design datasets capture final artifacts rather than the sequence of
decisions that produced them. FigmaTrace records full expert sessions so
agents can learn the workflow itself.

### Source Data

#### Data Collection and Processing

OS-level actions and screen captures were recorded from experts solving the
126 tasks. Processing: ~95% of raw actions (idle mouse movement) filtered
out; two-pass frame extraction with settle detection; effect filtering by
changed-pixel fraction; phase segmentation with Gemini-3.6-Flash using
consensus boundaries across 3/6/12-way shardings. Total compaction: 179x
versus raw OS events.

#### Who are the source data producers?

Subject-matter experts hired through Upwork, each with 2+ years of Figma
experience, aged 18+, and vetted with a starter task.

## Bias, Risks, and Limitations

- Open-ended tasks (theming, sketch-to-Figma, prototyping) reflect individual
  SME preferences by design.
- Some noisy actions leak through preprocessing; models trained on the data
  can repeat near-identical clicks or over-favor screen-center targets.

## Citation

**BibTeX:**
```bibtex
@misc{deshpande2026figmatracecapturingcreativenuances,
      title={FigmaTrace: Capturing Creative Nuances in Human Figma Design Workflows}, 
      author={Darshan Deshpande and Yoshinari Fujinuma and Martyna Markiewicz and Devanshu Bansal and Shivani Jain and Nicholas Saban and Chirag Maheshwari and Anand Kannappan},
      year={2026},
      eprint={2608.21460},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2608.21460}, 
}
```

Dataset Card Contact
darshan@patronus.ai