Skill Eval

A diagnostic instrument for AI skills. Feed it any skill, get back a structured report —

what's working, what's broken (Bad Cases), and what to fix — then iterate until it passes.

Output Structure (Mandatory)

Create {target-skill-name}-eval/ at the same level as the target skill:

{target-skill-name}-eval/
├── v1/
│   ├── plan.md
│   ├── trigger-results.json
│   ├── cases.json
│   ├── execution-results.json
│   ├── report.md
│   └── optimized-skill/SKILL.md
├── v2/ ...
└── summary.md

Version rule: v1 → v2 → v3. Always create a new v{N}/ for each run.

Pipeline & Checkpoints

| Phase | Action | Output File | Detail |

|-------|--------|-------------|--------|

| 0-1 | Analyze skill + design plan | v{N}/plan.md | agents/planner.md |

| 1.5 | Trigger evaluation | v{N}/trigger-results.json | scripts/run_trigger_eval.py |

| 2 | Design test cases | v{N}/cases.json | references/test-case-design.md |

| 3 | Execute & record | v{N}/execution-results.json | agents/executor.md |

| 4 | Score & verify | _(scores for Phase 5)_ | agents/judge.md + references/scoring.md |

| 5 | Report + optimize | v{N}/report.md + optimized-skill/SKILL.md + summary.md | agents/reporter.md + agents/advisor.md |

RULE: Each phase writes its file BEFORE the next begins. No file = not done. Go back.

Phase 0-1: Analyze & Plan

Input: Target SKILL.md

Output: v{N}/plan.md

Assess structure (high/medium/low), dissect steps with operation types, design test

strategy, identify risks and sandbox requirements.

Full protocol → agents/planner.md

> CHECKPOINT: Write v{N}/plan.md. Verify it exists. Do NOT proceed until written.

Phase 1.5: Trigger Evaluation

Input: v{N}/plan.md + target SKILL.md (frontmatter description field)

Output: v{N}/trigger-results.json

Design trigger probes (positive + negative queries) based on the skill's description,

then test whether each probe correctly triggers or does not trigger the skill.

1. Design probes from the plan's Trigger Probe Strategy:
• Positive probes (should trigger): direct requests, synonyms, multilingual variants, implicit intent
• Negative probes (should NOT trigger): similar-sounding but out-of-scope queries, general info requests
• Minimum: 5 positive + 5 negative probes (quick mode), 8+8 (deep mode)

1. Run probes via scripts/run_trigger_eval.py or manual testing against the platform's skill activation mechanism.

1. Record results as v{N}/trigger-results.json with precision, recall, and per-probe pass/fail.

Full script → scripts/run_trigger_eval.py

> CHECKPOINT: Write v{N}/trigger-results.json. Must contain precision + recall metrics. Do NOT proceed until written.

Phase 2: Design Test Cases

Input: v{N}/plan.md + target SKILL.md

Output: v{N}/cases.json

Generate per-step expected results with check_types (exact > regex > semantic).

Quick mode: 4 cases. Deep mode: 8-12 cases.

Critical: Expected results written BEFORE execution. Never adjust after.

Full protocol → references/test-case-design.md

JSON schema → references/schemas.md

> CHECKPOINT: Write v{N}/cases.json. At least 4 cases (quick) or 8 (deep). Do NOT proceed until written.

Phase 3: Execute & Record

Input: v{N}/cases.json + target SKILL.md

Output: v{N}/execution-results.json

Run each case with the skill active. Record per-step: action_taken, actual_output,

tool_calls, tokens, time. Run at least 1 baseline (without skill).

Full protocol → agents/executor.md

JSON schema → references/schemas.md

> CHECKPOINT: Write v{N}/execution-results.json. Every case must have entries. Do NOT proceed until written.

Phase 4: Score & Verify

Input: v{N}/execution-results.json + v{N}/cases.json

Output: Scored data for Phase 5

Three independent scores per step:

• Completion (0/1): Did the operation execute?
• Correctness (0/1/2): Does actual match expected?
• Execution Quality (0/1/2): Did it follow the skill's method?

Rules: Completion=0 cascades. Scores never combined. Every sub-max score needs a reason.

Full protocol → agents/judge.md

Scoring definitions → references/scoring.md

Rubric templates → references/rubrics.md

> CHECKPOINT: All steps scored. Every step has three scores + reasons. Ready for Phase 5.

Phase 5: Report & Optimize

Input: Scored results + Advisor analysis

Outputs: v{N}/report.md + v{N}/optimized-skill/SKILL.md + summary.md

Report order: Bad Cases FIRST → Overview → Step Scores → Baseline → Efficiency → Safety → Details.

If Bad Cases exist → produce optimized skill with fixes applied.

If Bad Cases = 0 + stop conditions met → PASSED.

Full protocol → agents/reporter.md

Root cause analysis → agents/advisor.md

Visual formats → references/report-format.md

> CHECKPOINT:

> - v{N}/report.md exists with Bad Cases + overview + step scores

> - Bad Cases > 0 → v{N}/optimized-skill/SKILL.md exists

> - summary.md updated

Bad Cases

A step is a Bad Case if ANY: Completion=0, Correctness=0, Quality=0, or safety finding.

Stop Conditions (All Must Be True to PASS)

| Condition | Threshold |

|-----------|-----------|

| Bad Cases = 0 | No steps with any score = 0 |

| Correctness avg | >= 1.8/2 |

| No regressions | No previously-passing case now fails |

| Unsafe rate = 0% | No safety findings |

Non-Negotiables

1. Expected results before execution.
2. Low scores need reasons (expected vs actual).
3. Bad Cases shown first.
4. Three scores stay independent. Never combined.
5. Versions immutable. New v{N} for each run.
6. Every fix traces to a Bad Case.
7. Regressions are zero-tolerance.
8. Structure check before testing.
9. Baseline proves skill's value.
10. Every phase writes its file. No file = not done.

File Index

| File | Purpose |

|------|---------|

| agents/planner.md | Phase 0-1: Structure assessment + plan generation |

| agents/executor.md | Phase 3: Test execution + recording |

| agents/judge.md | Phase 4: Scoring engine protocol |

| agents/advisor.md | Phase 5B: Root cause analysis + optimization |

| agents/reporter.md | Phase 5: Report generation + summary |

| references/test-case-design.md | Phase 2: Case design guide + cases.json schema |

| references/schemas.md | All JSON data structure definitions |

| references/scoring.md | Scoring scales, computation, display |

| references/rubrics.md | Per-operation-type rubric templates |

| references/report-format.md | Visual report presentation |

| scripts/score_engine.py | Automated scoring computation |

| scripts/safety_scanner.py | Static safety analysis |

| scripts/generate_scorecard.py | HTML report generation |

| scripts/run_trigger_eval.py | Phase 1.5: Multi-platform trigger evaluation |

Platform Compatibility

| Platform | Skill Location | Trigger |

|----------|---------------|---------|

| Claude Code | .claude/commands/ | claude -p CLI |

| Cursor | .cursor/rules/ | Agent mode |

| Codex | .codex/skills/ | CLI/API |

| OpenClaw | .claw/skills/ | Hub activation |

Security

• Sandbox untrusted skills — disposable workspace, mock data, approval mode
• Skill names sanitized — no path traversal in trigger probes
• HTML reports escaped — html.escape() on all interpolated values
• Descriptions neutralized — prompt-injection patterns stripped from probe files

See agents/executor.md Safety Boundary section for full sandboxing protocol.