Content
86%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 tight, opinionated guidance skill that conveys hard-won, non-obvious rules concisely with a clear before/after benchmark workflow. The only weakness is that the eval-running mechanics are outsourced to another skill, slightly reducing inline actionability and error-recovery clarity.
Suggestions
Add a one-line pointer to the concrete eval command or the ai-evals skill's run reference so the before/after step is executable inline rather than deferred.
Include an explicit regression-recovery checkpoint (e.g., 'if the post-change A/B regresses on the affected mode, revert the change') to close the workflow's feedback loop.
Name the metric/threshold that decides a passing A/B (what counts as 'no regression') so the validation step is unambiguous.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and dense — it assumes competence, skips generic explanations, and packs in non-obvious domain specifics (e.g., per-iteration schema tax, the {success, message} invariant) where every token earns its place. | 5 / 5 |
Actionability | Guidance is concrete and specific — 'return { success, message }', 'Optimize finalContextTokens', named files like global/core.ts and the copilot/chat path — but the actual eval invocation is deferred to the external ai-evals skill rather than given inline, leaving a minor execution gap. | 4 / 5 |
Workflow Clarity | The before/after benchmark sequence is explicit and the A/B requirement acts as a validation checkpoint, but there is no explicit regression-recovery loop (e.g., 'if the eval regresses, revert'), leaving minor checkpoint gaps. | 4 / 5 |
Progressive Disclosure | Under 50 lines with no bundle files needed, organized into four clearly headed sections; per the simple-skill exception this is well-structured and easily navigable. | 5 / 5 |
Total | 18 / 20 Passed |