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PatronusAI
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figmatrace

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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 Sources

Repository: https://huggingface.co/datasets/PatronusAI/figmatrace
Paper: FigmaTrace: Capturing Creative Nuances in Human Figma Design Workflows
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.

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:

@article{deshpande2026figmatrace,
  title={FigmaTrace: Capturing Creative Nuances in Human Figma Design Workflows},
  author={Deshpande, Darshan and Fujinuma, Yoshinari and Markiewicz, Martyna
          and Bansal, Devanshu and Jain, Shivani and Saban, Nicholas
          and Maheshwari, Chirag and Kannappan, Anand},
  journal={https://cdn.patronus.ai/FigmaTrace.pdf},
  year={2026}
}

Dataset Card Contact darshan@patronus.ai