Initial dataset card

README.md

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+---
+pretty_name: FigmaTrace
+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-NC
+
+### Dataset Sources
+
+- **Repository:** https://huggingface.co/datasets/PatronusAI/figmatrace
+- **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.
+
+## 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
+@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