Content
75%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable skill: a unified record schema, an explicit decision rule, a copy-paste worked example covering the two most common runners, and an honest anti-patterns table, with heavy detail correctly offloaded to a single well-signaled reference. The main residual issues are a phantom scripts/run_perf_gate.py path referenced in two places, and the worked example omitting the baseline-delta computation and any guard against missing input artifacts.
Suggestions
Make scripts/run_perf_gate.py a real bundle file (and have the reference's CI wiring point at it), or drop the script-path labels so no dangling path is referenced in two places.
Extend the worked example to fetch the baseline artifact and compute delta_pct — the decision rule's relative-regression trigger is never exercised in the shown code.
Add a validation step (in 'How to use' and the worked script) that fails when an expected runner artifact or baseline is missing, instead of silently emitting a 'go' verdict for zero records.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and operational — schema, field table, decision function, artifact template, worked script, anti-patterns — with almost no explanation of concepts Claude already knows. Minor trimming is possible: the 'Two regression triggers' list re-explains what the gate_decision code already expresses, and 'Modern teams measure perf at multiple layers' plus 'Both matter: a metric within budget but trending up still warrants a warning' are small narrative touches, which fits the 4 anchor ('efficient; minor instances of over-explanation') rather than the every-token-earns-its-place level of 5. | 4 / 5 |
Actionability | The worked example is executable end-to-end (parses k6 summary.json and lhr-*.json, applies budgets, prints the verdict, exits with its code) and the JSON record, field-source table, and markdown artifact template are all concrete and copy-paste ready. It stops short of the 5 anchor because the central delta-vs-baseline computation is never shown executable — the script reads no baseline and never populates the delta_pct field the decision function depends on — leaving a real gap in covering the common case. | 4 / 5 |
Workflow Clarity | 'How to use' gives a clear 5-step sequence (collect artifacts → flatten → fetch baseline and compute delta → apply decision rule → emit artifact, exit non-zero on no-go) and the worked example demonstrates it. It misses 5 because validation checkpoints are implicit rather than explicit — most notably, if expected input artifacts are absent the worked script silently produces zero records and a 'go' verdict (a false pass), and no step says to verify the baseline/artifacts exist. This is a read-and-report gate rather than a destructive change, so the destructive/batch cap to 3 does not apply; the sequence itself is clearly better than the 3 anchor's checkpoint-less listing. | 4 / 5 |
Progressive Disclosure | The split is appropriate: SKILL.md stays the overview and decision logic while per-runner artifact mapping, budgets, and CI wiring are one level deep in a clearly signaled, real reference file ([references/ci-wiring-and-metric-sources.md], linked at the exact points it is needed). The 5 anchor is missed by one concrete organization gap: both the body's code-block label ('# scripts/run_perf_gate.py') and the reference's CI wiring ('python scripts/run_perf_gate.py') point to a scripts/ path that does not exist in the bundle — the script is only inline — a minor but real navigation gap. | 4 / 5 |
Total | 16 / 20 Passed |