| name | benchmark-audit |
| description | Automatically evaluates an LLM coding benchmark result using a standardized 0-100 rubric across 8 dimensions. Use when a benchmark finishes, when the user asks to evaluate a model, analyze a run's quality, or generate a score for a result. Also activates for 'score', 'evaluate benchmark', 'audit model', or 'analyze result'.
|
| compatibility | Requires Python 3.10+ and access to the project's filesystem. |
| metadata | {"author":"llm-coding-benchmark","version":"1.0"} |
Benchmark Audit Skill
Evaluates a model's benchmark output using a 0-100 rubric across 8 dimensions.
Reference material: Load references/rubric.md for the full scoring rubric
and RubyLLM API verification table (penalty amounts, valid vs hallucinated API
calls, Golden Rules). Load it when scoring dimensions 1-8.
Workflow
1. Identify the model
The user may provide a slug (e.g. claude_opus_4_6), a results directory, or
ask to evaluate all pending. If not specified, list available models in
results/ and ask.
2. Load run metadata
Read results/<slug>/result.json and extract status, project_summary.present
(9-artifact checklist), file_count, elapsed_seconds, phase tokens/time, and
phase 2 validation results when available.
If status != "completed":
- Do not auto-disqualify. Evaluate generated code anyway.
- Apply a structural penalty of -5 to -15 in Deliverable Completeness
(timeout phase 1 = -15, failed phase 2 = -5, missing compose/broken Docker = -5).
- Document phase 2 outcome explicitly.
3. Run structural scan
python scripts/benchmark_audit_scan.py results/<slug>
(Script lives at scripts/benchmark_audit_scan.py inside this skill.)
Returns JSON with: test counts, Gemfile gems, artifacts, RubyLLM patterns,
mocks/stubs, and common issues.
4. Evaluate the 8 dimensions
See references/rubric.md for the full scoring rules. Use data from steps 2+3
and manual reading of key files. Never invent scores — justify each rating
with a file:line reference.
- 1. Deliverable Completeness (0-25) — artifacts per prompt checklist
- 2. RubyLLM Correctness (0-20) — verify against API table in rubric
- 3. Test Quality (0-15) — quality over quantity, read actual test files
- 4. Error Handling (0-10) — rescue blocks, API key preflight, degraded UI
- 5. Persistence / Multi-turn (0-10) — session/cache vs singleton/none
- 6. Hotwire / Turbo / Stimulus (0-10) — Turbo Streams, Stimulus, partials
- 7. Architecture (0-5) — service objects, partial decomposition
- 8. Production Readiness (0-5) — XSS, secrets, CSRF, AR cleanup
5. Classify Runtime Tier
| Tier | Score | Meaning |
|---|
| A | 80-100 | Ship as-is or <30 min patches |
| B | 60-79 | 1-2 hrs to ship, sound architecture |
| C | 40-59 | Major rework, core bugs |
| D | 0-39 | Throw away or architectural inspiration only |
6. Generate the Audit Report
MUST contain these sections (800 word limit):
- A. Quick summary line (1 sentence)
- B. Scores by dimension with 1-line justification (file:line refs)
- C. Total score / 100
- D. Practical tier (A/B/C/D)
- E. Verification section — for each claimed hallucination, show gem source
grep proof. If unprovable, mark "unverified, likely correct."
- F. One killer strength + one killer weakness
- G. Final recommendation — worth using for greenfield Rails with RubyLLM?
No speculation — code excerpts + gem source proofs only.
7. Update the Success Report (if requested)
- AMD/cloud profile →
docs/success_report.md
- NVIDIA workstation profile →
docs/success_report.nvidia.md
Add model row to comparison tables, add failure analysis paragraph for Tier B/C/D,
update runtime viability summary.
Activation Examples
User: "Evaluate the kimi_k2_5 result"
→ Activate skill → run scanner → read rubric → score 8 dimensions → report
User: "Score the deepseek_v3_2 benchmark"
→ Activate skill → follow full workflow
User: "Benchmark finished, evaluate everything"
→ Activate skill → iterate over all slugs in results/ → consolidated report
