Evaluate Skill

Orchestrate a cross-tier evaluation of an AI skill to determine its clarity and robustness.

Procedure

1. Load inputs

   • Read the skill file at {{ skill-path }}
   • Read the test cases file at {{ test-cases-path }}
   • Validate that test cases is a JSON array of objects with input and expectedOutcome fields

2. Set up evaluation matrix

   • Model tiers to test: opus, sonnet, haiku
   • For each tier, for each test case: plan one blind test run

3. Execute blind tests (highest tier first)

   • For each model tier (opus → sonnet → haiku):
     • For each test case:
       • Spawn a test-subject agent at the current tier
       • Provide it ONLY the skill content and the test case input
       • Do NOT provide the expectedOutcome to the test subject
       • Collect the test subject's output

4. Evaluate results

   • For each test run, compare the test subject's output against the expectedOutcome
   • Determine pass/fail using semantic similarity (the output need not be identical, but must achieve the same goal)
   • Record: tier, test case index, pass/fail, output summary

5. Generate refinement report

   • Identify the lowest tier where all test cases pass ("clarity floor")
   • For each failure, analyze WHY the lower-tier agent failed:
     • Ambiguous instructions?
     • Missing context or assumptions?
     • Overly complex multi-step reasoning?
     • Implicit knowledge requirements?
   • Produce specific, actionable recommendations to improve the skill
   • Format as a structured report with sections: Summary, Per-Tier Results, Failure Analysis, Recommendations

6. Output the report to the user