Update README.md

README.md

@@ -27,4 +27,122 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: mit
+language:
+- en
+tags:
+- figma-to-code
+- autonomous-agents
+pretty_name: CREW:Figma-to-Code
+size_categories:
+- n<1K
 ---
+
+  
+  # CREW: Figma to Code
+
+  A benchmark for evaluating AI coding agents on **Figma-to-code generation** — converting real-world Figma community designs
+   into production-ready React + Tailwind CSS applications.
+
+  Each task gives an agent full access to a Figma file via MCP tools. The agent must extract the design system, generate
+  components, build successfully, and deploy a live preview. Outputs are evaluated through human preference (ELO) and
+  automated verifiers.
+
+  ## Benchmark at a Glance
+
+  | | |
+  |---|---|
+  | **Tasks** | 37 real Figma community designs |
+  | **Agents tested** | Claude Code (Opus 4.6), Codex (GPT-5.2), Gemini CLI (3.1 Pro) |
+  | **Total runs** | 96 autonomous agent executions |
+  | **Human evaluations** | 135 pairwise preference votes |
+  | **Primary metric** | Human Preference ELO (Bradley-Terry) |
+  | **Task duration** | 6–40 expert-hours equivalent per task |
+
+  ## Leaderboard (Human Preference ELO)
+
+  | Rank | Agent | Model | ELO | 95% CI | Win % |
+  |------|-------|-------|-----|--------|-------|
+  | 1 | Codex | GPT-5.2 | 1054 | [1005, 1114] | 56.8% |
+  | 2 | Claude Code | Opus 4.6 | 1039 | [987, 1093] | 55.1% |
+  | 3 | Gemini CLI | 3.1 Pro | 907 | [842, 965] | 31.1% |
+
+  Top two agents are statistically indistinguishable (p=0.67, Cohen's h=0.08); both significantly outperform Gemini CLI
+  (p<0.05).
+
+  Live leaderboard: [evals.metaphi.ai](https://evals.metaphi.ai)
+
+  ## Dataset Schema
+
+  Each row represents one Figma design task:
+
+  | Column | Type | Description |
+  |--------|------|-------------|
+  | `id` | string | Unique task identifier |
+  | `figma_data.figma_file_key` | string | Figma file ID for API access |
+  | `figma_data.figma_file_url` | string | Full Figma URL |
+  | `figma_data.figma_file_name` | string | Design name/title |
+  | `figma_data.is_site` | bool | Whether design is a Figma Site |
+  | `description` | string | Design context and task description |
+
+  ## Task Scenarios
+
+  Tasks span 7 complexity levels, from single-component extraction to full multi-page applications:
+
+  1. **E-commerce Product Page** (12 hrs) — PDP with image gallery, variant selectors, inventory states, cart integration
+  2. **Mobile Onboarding Flow** (16 hrs) — Multi-step flow with transitions, conditional branching, state management
+  3. **Component Set with States** (6 hrs) — Variant matrix extraction, typed props, conditional rendering
+  4. **Design Tokens to Theme** (8 hrs) — Variables, typography, effects → Tailwind config + CSS custom properties
+  5. **Multi-Page Webapp** (40 hrs) — 5+ pages with routing, shared components, consistent theming
+  6. **Animation-Heavy Interface** (20 hrs) — Smart Animate → Framer Motion with precise timing choreography
+  7. **Messy Enterprise File** (24 hrs) — Real-world chaos: inconsistent naming, duplicates, orphaned components
+
+  ## Data Curation
+
+   Sourced from licensing partnerships with enterprises, domain-experts and community dataset curation.
+
+  ## Evaluation Framework
+
+  | Verifier | Type | Method |
+  |----------|------|--------|
+  | **Human Preference** | Subjective | Pairwise comparison → Bradley-Terry ELO |
+  | **Visual Judge** | Subjective | VLM screenshot comparison (design vs. output) |
+  | **Skill Verifier** | Subjective | Task-specific rubrics (build, tokens, components, fidelity) |
+  | **Behavior Verifier** | Subjective | Agent trajectory analysis (error recovery, tool usage) |
+
+  ## Agent Error Recovery
+
+  Across 96 runs, agents encountered 590 errors with a 70.3% autonomous recovery rate:
+
+  | Error Type | Count | Recovery |
+  |------------|-------|----------|
+  | Tool call failure | 419 | 66.3% |
+  | Git error | 64 | 75.0% |
+  | Syntax error | 33 | 90.9% |
+  | Build error | 11 | 100.0% |
+
+  ## Usage
+
+  ```python
+  from datasets import load_dataset
+
+  ds = load_dataset("metaphilabs/figma", split="train")
+
+  # Each row contains a Figma file key for API access
+  for task in ds:
+      print(task["id"], task["figma_data"]["figma_file_name"])
+
+  Citation
+
+  @misc{metaphi2026crew,
+    title={CREW: Enterprise Agent Benchmarks},
+    author={Metaphi Labs},
+    year={2026},
+    url={https://evals.metaphi.ai}
+  }
+
+  Links
+
+  - Leaderboard: https://evals.metaphi.ai
+  - Website: https://metaphi.ai
+  - Collection: https://huggingface.co/collections/metaphilabs/crew