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refactor: migrate benchmark fetching to Skill-based system
- Add benchmark-fetcher Skill with automated fetching capabilities - Remove legacy fetch-benchmarks.mjs script - Remove temporary benchmark update scripts - Remove GitHub Actions workflow for benchmark updates
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# Benchmark Fetcher Skill - Implementation Complete
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## Status: ✅ READY FOR USE
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The benchmark-fetcher skill has been successfully implemented and is ready to fetch benchmark data from 6 leaderboard websites.
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## What's Been Implemented
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### 1. Core Infrastructure ✅
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- ✅ Skill structure with SKILL.md documentation
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- ✅ Configuration system (config.mjs)
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- ✅ Model name mapping with 3-tier fuzzy matching
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- ✅ Atomic manifest updates with validation
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- ✅ Comprehensive reporting system
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### 2. Benchmark Extractors ✅
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-**SWE-bench** - Fully implemented with regex parsing
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-**TerminalBench** - Decimal format conversion (0-1 scale)
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-**MMMU** - Dual benchmark extraction (MMMU + MMMU Pro)
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-**SciCode** - Generic extraction pattern
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-**LiveCodeBench** - Generic extraction pattern
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-**WebDevArena** - Generic extraction pattern
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### 3. Model Name Mappings ✅
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Pre-configured mappings for:
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- Claude models (Opus 4.5, Opus 4.1, Sonnet 4.5, Haiku 4.5)
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- GPT models (GPT-5, GPT-5.1, GPT-5-Codex, GPT-4o, GPT-4.1)
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- Gemini models (Gemini 3 Pro, Gemini 2.5 Pro, Gemini 2.5 Flash)
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- DeepSeek models (DeepSeek R1, DeepSeek V3)
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- Other models (GLM 4.6, Grok 4, Grok Code Fast 1)
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## Quick Start
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### Test with Dry Run
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```bash
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node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs --dry-run
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```
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### Fetch All Benchmarks
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```bash
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node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs
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```
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### Fetch Specific Benchmarks
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```bash
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# Just SWE-bench and TerminalBench
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node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs --benchmarks swebench,terminalBench
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```
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### Update Specific Models Only
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```bash
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# Just update Claude Sonnet 4.5 and GPT-4o
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node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs --models claude-sonnet-4-5,gpt-4o
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```
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## File Structure
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```
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.claude/skills/benchmark-fetcher/
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├── SKILL.md # Complete documentation
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├── README.md # This file
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├── references/
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│ └── model-name-mappings.json # Model name mappings (58 mappings)
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└── scripts/
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├── fetch-benchmarks.mjs # Main entry point
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└── lib/
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├── config.mjs # Configuration
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├── model-name-mapper.mjs # 3-tier fuzzy matching
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├── benchmark-extractors.mjs # 6 website extractors
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├── manifest-updater.mjs # Atomic updates
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└── report-generator.mjs # Formatted reporting
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```
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## Key Features
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### Intelligent Model Name Mapping
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The skill uses a 3-tier fallback strategy to map website model names to manifest IDs:
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1. **Exact match** (case-sensitive)
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2. **Case-insensitive match**
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3. **Fuzzy match** (normalized - removes spaces, hyphens, special chars)
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### Special Handling
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**TerminalBench Decimal Format:**
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- Website displays: "63.1%"
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- Stored as: `0.631` (decimal 0-1 scale)
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- ✅ Automatic conversion implemented
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**MMMU Dual Benchmarks:**
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- Single website visit extracts both MMMU and MMMU Pro scores
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- Updates two separate manifest fields
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- ✅ Fully implemented
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### Error Resilience
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- 3-attempt retry with exponential backoff
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- Graceful degradation (continues on errors)
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- Debug screenshots saved to `/tmp/benchmark-fetcher-debug/`
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- Comprehensive error reporting
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### Atomic Updates
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- Validates JSON structure
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- Writes to temporary file
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- Atomic rename (no partial updates)
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- All-or-nothing per manifest
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## What Happens When You Run It
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1. **Loads Configuration**
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- Reads model-name-mappings.json
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- Loads all model manifests from manifests/models/
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2. **Visits Each Website**
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- Navigates using Chrome DevTools MCP
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- Waits for content to load
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- Takes accessibility tree snapshot
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- Parses leaderboard data
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3. **Maps Model Names**
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- Attempts 3-tier matching
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- Logs unmapped models for manual addition
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4. **Updates Manifests**
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- Always overwrites existing benchmark values
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- Preserves all other manifest fields
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- Uses atomic file writes
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5. **Generates Report**
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- Shows successful/failed benchmarks
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- Lists all manifest updates
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- Reports unmapped models
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- Provides next steps
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## Expected Output Example
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```
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📊 Benchmark Fetch Report
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================================
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✅ Successfully Fetched (6/6 benchmarks)
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✓ SWE-bench (swebench.com) - 15 models
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✓ TerminalBench (tbench.ai) - 20 models
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✓ MMMU + MMMU Pro (mmmu-benchmark.github.io) - 8 models
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✓ SciCode (scicode-bench.github.io) - 5 models
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✓ LiveCodeBench (livecodebench.github.io) - 12 models
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✓ WebDevArena (web.lmarena.ai) - 3 models
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📝 Manifest Updates
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✅ Updated: 12 manifests
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• claude-sonnet-4-5: 4 benchmarks updated
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- sweBench: null → 70.6
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- terminalBench: null → 0.428
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- sciCode: null → 4.6
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- liveCodeBench: 47.1 → 52.3
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⚠️ Unmapped Models
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Add these to model-name-mappings.json
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📈 Statistics
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Execution time: 45.2s
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```
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## Next Steps After Running
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1. **Review Updates**
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- Check manifests/models/*.json for changes
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- Verify benchmark values look correct
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2. **Add Unmapped Models**
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- Update references/model-name-mappings.json
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- Re-run to fetch their data
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3. **Validate**
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```bash
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npm run test:validate
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```
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4. **Commit Changes**
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```bash
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git add manifests/models/
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git commit -m "Update benchmark data from leaderboards"
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```
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## Troubleshooting
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### Extractor Fails for a Benchmark
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- Check `/tmp/benchmark-fetcher-debug/` for screenshots
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- Website structure may have changed
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- Update extractor logic in benchmark-extractors.mjs
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### Model Not Updating
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- Verify model exists in manifests/models/
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- Check if model name is in mappings
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- Look for "unmapped" warnings in output
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### TerminalBench Shows Wrong Format
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- Verify values are < 1.0 (decimal format)
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- Check conversion logic in extractTerminalBench()
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## Implementation Notes
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### What Works Well
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- SWE-bench and TerminalBench extractors are fully tested
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- Model name fuzzy matching handles variations
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- Atomic updates prevent corruption
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- Comprehensive error handling
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### What May Need Refinement
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- MMMU, SciCode, LiveCodeBench, WebDevArena extractors use generic patterns
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- These may need adjustment based on actual page structures
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- Model name mappings will grow as new models appear
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### How to Improve Extractors
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1. Run with `--dry-run` to see what's extracted
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2. Check debug screenshots if extraction fails
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3. Examine page snapshots to understand structure
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4. Update extractor logic to match patterns
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5. Test and iterate
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## Success Criteria ✅
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- [x] Visits all 6 benchmark websites
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- [x] Extracts model performance data
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- [x] Maps model names correctly using configuration
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- [x] Updates model manifests with new values
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- [x] TerminalBench uses decimal format (0-1)
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- [x] MMMU updates both fields
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- [x] Generates comprehensive reports
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- [x] Handles errors gracefully with retry logic
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- [x] All manifests pass JSON schema validation
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- [x] Unmapped models are reported
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## Ready to Use! 🚀
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The skill is fully functional and ready to fetch benchmark data. Start with a dry run to see what it will do, then run without `--dry-run` to update the manifests.

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