Conciseness

The body is mostly dense and functional (templates, checklists, commands), but includes unnecessary padding: the motivational blog quote ("If a failure taught you something important..."), an intro paragraph that repeats the frontmatter description verbatim, and the verification methods being defined twice — once in the Eval Case Format section and again under Execution. Not 2 because there is no basic-concept exposition and most sections earn their tokens.

Actionability

Highly actionable throughout: a complete eval-case markdown template, five verification methods with concrete examples (grep patterns, "npm run typecheck", "python .claude/skills/skill-creator/scripts/quick_validate.py <target>"), a runnable mcp-script example, and report/index formats. Minor gaps: "Run by Pattern-Key — Filter to evals matching a specific pattern" and "Run by Area" give no concrete filtering mechanics.

Workflow Clarity

The Execution section is a clear 6-step sequence with explicit validation checkpoints (precondition check → skip, evidence-strength check that marks "fail" with "insufficient_evidence" rather than upgrading claims), the eval case template mandates a Recovery Action ending in "Re-run this eval to verify" (a feedback loop), and the regression report format closes the loop. This matches the anchor-5 pattern of explicit validation steps plus error-recovery feedback loops for a batch operation.

Progressive Disclosure

Sections are clearly headed and well-ordered, but this is a ~290-line single-file skill with zero bundle files: the ~90-line eval case template, the ~50-line Skill Validation section, and the mcp-scripts extension section are all inlined when they clearly belong in one-level-deep reference files. Not 4 because content that should be separate is inline throughout, not just in minor spots.

Specificity

Lists several concrete actions — "Creates permanent eval cases from promoted learnings", "runs regression checks", "verify promoted rules still hold" — but "Creates permanent eval cases" and "Turns failures into test cases" restate the same action, leaving coverage with minor gaps rather than comprehensive distinct actions.

Completeness

Explicitly answers both: what ("Creates permanent eval cases from promoted learnings and runs regression checks") and when ("Use when a learning is promoted and has a clear pass/fail condition, or on cadence to verify promoted rules still hold") — the when-clause is concrete and multi-condition, matching the anchor-5 example's structure. Not 4 because the trigger guidance is more specific than the anchor-4 example's generic 'Use when working with X'.

Trigger Term Quality

Good natural keywords like "eval", "regression checks", "pass/fail condition", and "promoted rules", but terms like "promoted learning", "outer loop", and "regress-test step" are workflow-internal jargon users outside this ecosystem would not naturally say, and common variations (e.g. "test case", "regression test") are missing.

Distinctiveness Conflict Risk

Clear niche (eval cases derived from promoted learnings, regression verification of the harness) that is well-distinguished from sibling skills like learning-aggregator and self-improvement, but "runs regression checks" carries minor overlap risk with generic test-runner skills. Not 5 because the niche is defined by ecosystem jargon rather than fully distinct trigger phrases.