Benchmark Fetcher

Benchmark Fetcher Skill

Automate the fetching of benchmark performance data from leaderboard websites and update model manifests with the latest scores using advanced browser automation.

Overview

This skill extends benchmark data collection by automating visits to 6 major AI model leaderboard websites, extracting performance scores, and updating model manifests in manifests/models/ with the latest benchmark data.

Key Features:

• Automated Data Collection: Uses Playwright MCP to visit and extract data from 6 leaderboard websites
• Intelligent Model Mapping: Maps website model names to manifest IDs using configurable mappings
• Always Overwrite: Updates manifests with latest benchmark values
• Error Resilient: Retry logic with exponential backoff and graceful degradation
• Comprehensive Reporting: Detailed completion reports with unmapped models and update statistics

Supported Benchmarks

Note: TerminalBench uses a decimal format (0-1 scale), while all other benchmarks use percentage format (0-100 scale).

Usage

Fetch All Benchmarks

Update all model manifests with latest benchmark data from all 6 websites:

node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs

Fetch Specific Benchmarks

Update only specific benchmarks:

# Fetch only SWE-bench and TerminalBench
node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs --benchmarks swebench,terminalBench

# Fetch only LiveCodeBench
node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs --benchmarks liveCodeBench

Fetch for Specific Models

Update benchmarks for specific models only:

# Update only Claude Sonnet 4.5 and GPT-4o
node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs --models claude-sonnet-4-5,gpt-4o

Dry Run Mode

Preview what would be updated without actually modifying manifests:

node .claude/skills/benchmark-fetcher/scripts/fetch-benchmarks.mjs --dry-run

Model Name Mapping

How Mapping Works

Each benchmark website uses different naming conventions for models. The references/model-name-mappings.json file maps website-specific model names to manifest IDs.

Example mapping:

{
  "swebench": {
    "websiteModels": {
      "Claude Sonnet 4.5": "claude-sonnet-4-5",
      "GPT-4o": "gpt-4o",
      "Gemini 2.5 Pro": "gemini-2-5-pro"
    }
  }
}

Mapping Strategy

The mapper uses a 3-tier fallback strategy:

1. Exact match (case-sensitive): "Claude Sonnet 4.5" → "claude-sonnet-4-5"
2. Case-insensitive match: "claude sonnet 4.5" → "claude-sonnet-4-5"
3. Fuzzy match (normalized): "Claude-Sonnet-4.5" → "claude-sonnet-4-5"

Normalization: Removes spaces, hyphens, and special characters for fuzzy matching.

Adding New Mappings

When the script reports unmapped models, add them to references/model-name-mappings.json:

{
  "swebench": {
    "websiteModels": {
      "New Model Name": "new-model-id"
    }
  }
}

Data Extraction Process

High-Level Workflow

1. Load Configuration: Read mappings, load all model manifests
2. Initialize Browser: Start Chrome DevTools MCP browser instance
3. Visit Websites: Sequentially visit each benchmark website
4. Extract Data: Parse leaderboard tables from page snapshots
5. Map Models: Match website model names to manifest IDs
6. Update Manifests: Overwrite benchmark values in manifest files
7. Generate Report: Show updates, failures, and unmapped models

Website-Specific Extractors

Each benchmark has a dedicated extractor function in scripts/lib/benchmark-extractors.mjs:

• extractSWEBench() - Extracts SWE-bench Verified scores
• extractTerminalBench() - Extracts TerminalBench 2.0 accuracy (decimal format)
• extractMMMU() - Extracts both MMMU and MMMU Pro scores
• extractSciCode() - Extracts SciCode benchmark scores
• extractLiveCodeBench() - Extracts LiveCodeBench Pass@1 scores
• extractWebDevArena() - Extracts WebDevArena scores

Special Cases

TerminalBench Format:

• Website displays percentages (42.8%)
• Must store as decimal: 0.428 (not 42.8)
• Extractor handles conversion automatically

MMMU Dual Benchmarks:

• Single website has both MMMU and MMMU Pro leaderboards
• Extractor returns both in one visit:

  {
    mmmu: Map<manifestId, score>,
    mmmuPro: Map<manifestId, score>
  }
  

Update Strategy

Always Overwrite Policy

The skill uses an always overwrite strategy for benchmark values:

• Existing benchmark values are replaced with latest data from websites
• Null values are populated if found on websites
• Non-null values are updated with latest scores
• No confirmation or comparison - latest data always wins

Rationale: Benchmark scores represent the latest model performance. Websites are the authoritative source.

What Gets Preserved

Only benchmark fields are updated. All other manifest fields are preserved:

• ✅ Preserved: id, name, description, vendor, size, contextWindow, etc.
• 🔄 Updated: benchmarks.sweBench, benchmarks.terminalBench, etc.

Atomic Updates

Manifests are updated using atomic file writes:

1. Validate JSON structure
2. Write to temporary file (.tmp)
3. Atomic rename to target file
4. No partial updates - all or nothing

Error Handling

Retry Logic

Each benchmark extraction uses a 3-attempt retry strategy with exponential backoff:

Attempt 1: Direct extraction (immediate) Attempt 2: Retry after 2 seconds Attempt 3: Final retry after 4 seconds

After 3 failures:

• Take debug screenshot (/tmp/benchmark-{id}-error.png)
• Log error details
• Skip benchmark and continue with others

Error Categories

Website Access Errors:

• Cause: Site down, network timeout, rate limiting
• Handling: Retry 3 times, then skip benchmark

Extraction Errors:

• Cause: Page structure changed, data not found
• Handling: Screenshot for debugging, skip benchmark

Mapping Errors:

• Cause: Model name not in mapping configuration
• Handling: Log unmapped model, continue with others

Manifest Update Errors:

• Cause: File write errors, invalid JSON
• Handling: Atomic write protects against corruption, rollback on error

Graceful Degradation

The skill continues processing even when errors occur:

• If 1 benchmark fails, others still process
• If 1 model can't be mapped, others still update
• Partial success is better than no success
• Completion report shows exactly what succeeded/failed

Completion Report

After execution, a detailed report shows:

Summary Section

📊 Benchmark Fetch Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✅ Successfully Fetched (5/6 benchmarks)
   ✓ SWE-bench (swebench.com)
   ✓ TerminalBench (tbench.ai)
   ✓ MMMU + MMMU Pro (mmmu-benchmark.github.io)
   ✓ SciCode (scicode-bench.github.io)
   ✓ LiveCodeBench (livecodebench.github.io)

❌ Failed to Fetch (1/6 benchmarks)
   ✗ WebDevArena (web.lmarena.ai)
     Reason: Timeout after 3 retries

Manifest Updates

📝 Manifest Updates

✅ Updated: 15 manifests
   • claude-sonnet-4-5: 3 benchmarks updated
     - sweBench: null → 74.4
     - terminalBench: 0.428 → 0.604
     - liveCodeBench: 47.1 → 52.3

   • gpt-4o: 2 benchmarks updated
     - sweBench: 21.62 → 23.5
     - sciCode: 1.5 → 2.1

Unmapped Models

⚠️ Unmapped Models (require manual mapping)

SWE-bench:
  • "Qwen-Coder-2.5" → Add to model-name-mappings.json
  • "DeepSeek-Coder-V2" → Add to model-name-mappings.json

Suggestion: Update references/model-name-mappings.json

Statistics

📈 Statistics

Total benchmarks fetched:     247 values
Total manifests updated:      15 files
Execution time:               45.2s
Average time per benchmark:   7.5s

Next Steps

✅ Complete! Next steps:

1. Review updated manifests in manifests/models/
2. Add unmapped models to references/model-name-mappings.json
3. Retry failed benchmarks if needed
4. Run validation: npm run test:validate
5. Commit changes when satisfied

Tool Integration

Chrome DevTools MCP

The skill uses Chrome DevTools MCP tools for browser automation:

Navigation:

await mcp__chrome-devtools__navigate_page({
  url: 'https://www.swebench.com',
  type: 'url'
})

Wait for Content:

await mcp__chrome-devtools__wait_for({
  text: 'Leaderboard'
})

Take Snapshot:

const snapshot = await mcp__chrome-devtools__take_snapshot()
// Parse snapshot.content for leaderboard data

Debug Screenshots:

await mcp__chrome-devtools__take_screenshot({
  filePath: '/tmp/debug-screenshot.png'
})

Best Practices

Running the Skill

1. Run during off-peak hours to avoid rate limiting
2. Review unmapped models and update mappings before next run
3. Validate manifests after updates: npm run test:validate
4. Check for website changes if extraction fails repeatedly
5. Keep mappings updated as new models appear on leaderboards

Maintaining Mappings

1. Check completion reports for unmapped models
2. Add mappings immediately after discovering new models
3. Use canonical manifest IDs as mapping targets
4. Test mappings with --models flag to verify
5. Document special cases in mapping file comments

Troubleshooting

Extraction fails for a benchmark:

• Check if website structure changed
• Review debug screenshots in /tmp/
• Update extractor logic if needed

Model not updating:

• Verify model exists in manifests/models/
• Check mapping configuration
• Ensure model appears on leaderboard website

TerminalBench shows wrong values:

• Verify decimal format (0.428 not 42.8)
• Check extractor conversion logic
• Validate against website directly

Files Modified

After running this skill:

1. Model manifests: manifests/models/*.json - Updated with latest benchmark scores
2. No other files modified: The skill only updates benchmark fields in manifests

Validation

Always validate manifests after updates:

# Run schema validation
npm run test:validate

# Check JSON formatting
node -c manifests/models/*.json

Next Steps After Execution

1. Review updates: Check manifest changes make sense
2. Update mappings: Add newly discovered models to model-name-mappings.json
3. Retry failures: Re-run with --benchmarks for failed benchmarks
4. Validate: Run npm run test:validate to ensure schema compliance
5. Commit changes: Commit updated manifests to repository

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