eval-harness

Solid

Evaluation harness for testing agent and skill quality through structured benchmarks, regression tests, and quality scoring.

AI & Automation 814 stars 53 forks Updated today MIT

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# Eval Harness ## Overview Evaluation harness methodology adapted from the Everything Claude Code project. Provides structured frameworks for benchmarking agent performance, testing skill quality, and running regression suites. ## Evaluation Types ### 1. Agent Performance Benchmark - Define test cases with known-correct outputs - Run agent against each test case - Score: accuracy, completeness, relevance - Compare against baseline performance - Track performance over time ### 2. Skill Quality Testing - Verify skill instructions produce expected outcomes - Test edge cases and boundary conditions - Measure consistency across multiple runs - Check for harmful or incorrect outputs - Validate against ground truth ### 3. Regression Suite - Collection of previously-passing test cases - Run after any agent/skill modification - Flag regressions with before/after comparison - Maintain pass rate threshold (>= 95%) ### 4. Process Verification - End-to-end process execution with known inputs - Verify each phase produces expected outputs - Check task ordering and dependency satisfaction - Measure total execution time ## Quality Scoring ### Accuracy Score (0-100) - Correctness of output vs expected - Partial credit for partially correct outputs - Penalty for hallucinated or fabricated content ### Completeness Score (0-100) - Coverage of required output elements - Missing sections flagged and scored - Bonus for useful additional context ### Consistency Score (0-100) - Run same inp...

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

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