eval
Skill3 starsChanged 7 months agoMITView on GitHub
Evaluate code or implementation against specific criteria
We split this skill into the rules it gives an agent
3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 31 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 29
Run tests and capture results
- Distinctive: line 30
Run linters and type checkers
- Very specific: line 62
When $ARGUMENTS specifies custom criteria, use those instead of defaults.
“Common” is measured: kept by at least 6 in 10 of the 31 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
/eval
Evaluate the current implementation against defined criteria, standards, or a rubric. Produces a structured assessment with scores.
Arguments
$ARGUMENTS— Criteria to evaluate against, or "default" for standard quality gate
Default Criteria
| Category | Weight | Checks |
| Correctness | 30% | Tests pass, edge cases handled, no regressions |
| Security | 20% | OWASP checks, input validation, secrets management |
| Performance | 15% | No N+1 queries, efficient algorithms, caching |
| Maintainability | 15% | Readability, modularity, naming clarity |
| Testing | 10% | Coverage ≥80%, edge cases, test quality |
| Documentation | 10% | API docs current, README accurate, comments useful |
Process
1. Gather Evidence
For each category, collect evidence:
- Run tests and capture results
- Run linters and type checkers
- Review code changes
- Check documentation accuracy
2. Score Each Category
Rate each category: 1 (Poor) to 5 (Excellent)
3. Generate Report
## Evaluation Report
### Overall Score: X.X / 5.0
| Category | Score | Weight | Weighted |
|----------|-------|--------|----------|
| Correctness | 4/5 | 30% | 1.20 |
| Security | 3/5 | 20% | 0.60 |
| Performance | 4/5 | 15% | 0.60 |
| Maintainability | 5/5 | 15% | 0.75 |
| Testing | 3/5 | 10% | 0.30 |
| Documentation | 4/5 | 10% | 0.40 |
| **Total** | | | **3.85** |
### Detailed Findings
[Per-category evidence and recommendations]
### Verdict
**SHIP IT** (≥4.0) | **IMPROVE** (3.0-3.9) | **REWORK** (<3.0)Custom Criteria
When $ARGUMENTS specifies custom criteria, use those instead of defaults.
Format each criterion as: name: description (weight%).
Other versions39 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 39
- PrimeIntellect-ai/prime-rl★ 2.2ksame name
- rohanpsingh/LearningHumanoidWalking★ 1.2ksame name
- mikeyobrien/rho★ 372same name
- ai-analyst-lab/ai-analyst★ 304same name
- jh941213/my-claude-code-asset★ 126same name
- jh941213/ADLC-harness★ 125same name
- jh941213/my-cc-harness★ 125same name
- qtzx06/yolodex★ 96same name
- Kastalien-Research/thoughtbox★ 64same name
- Kanevry/session-orchestrator★ 53same name
29 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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