Conciseness

The body adds only project-specific process knowledge (no explanation of concepts Claude already knows), but is noticeably padded by repetition: the ownership/never-edit-managed-files boundary is restated roughly six times (the 'Critical: Respect Skill Ownership' section, Step 3 scope constraint, Step 4 'IMPORTANT', Step 7.2 item 5, and Rules 7/12/13), and the skill-context-priority principle is explained three times (Step 0.2 lines on precedence, the 'How to apply evolve's own skill-context rules' block, and the Step 3 priority warning). This fits 'mostly efficient but could be tightened' rather than 2, since the redundancy is a minority of a genuinely complex 8-step workflow.

Actionability

Highly actionable throughout: exact paths ('.ai-factory/patches/*.md', 'patch-cursor.json'), a concrete cursor JSON shape, a Prevention Point Registry table format, complete report and skill-context file templates, bash commands (mkdir -p / rm / rmdir), AskUserQuestion batching sizes (3 and 4), and enumerated decision options. Minor gaps keep it below 5: 'Reuse the project's scenario mechanism when available' defers to an undefined external mechanism, and there is no worked example of an improvement that was validated and applied with observed results (the one example honestly records NOTRUN/postpone).

Workflow Clarity

A clearly sequenced 8-step workflow with numbered sub-steps and explicit validation checkpoints: Step 4 gates Step 5 ('Do NOT proceed to Step 5 until all stale rule decisions are collected'), Step 6.5 validates every candidate before approval, 'Do NOT apply any changes until the user answers', post-output enforcement verification, and cursor-failure handling. Feedback loops are present (revalidate before apply, full rescan procedure, refine/postpone outcomes) alongside checklists (Rules 1-14, quality rules) — a strong match for the top anchor.

Progressive Disclosure

Good bundle structure: three real reference files (TARGET-OWNERSHIP.md, SHARED-CONTEXT.md, CANDIDATE-VALIDATION.md), each clearly signaled and read at the right step, one level deep (they cross-link each other but contain the actual content — no pass-through nesting), with heavy procedural detail properly offloaded from the 693-line body. Minor gaps: the sizable stale-rules report format and evolution-log template are fully inline and could be split out, and the empty '### Context Cleanup' heading (immediately followed by '## Artifact Ownership') is a structural glitch.

Specificity

Lists several actions ("Self-improve AI Factory skills", "Analyzes what went wrong, what works", "enhances skills to prevent future issues") plus named inputs ("project context, accumulated patches, and codebase patterns"), matching the anchor for several specific actions with minor gaps. Not a 5 because the verbs remain abstract and no concrete artifacts (e.g., skill-context rule files, evolution reports) are named; not a 3 because it goes beyond 1-2 actions and specifies its data sources.

Completeness

Both answered: a clear multi-clause 'what' and an explicit "Use when you want to make AI smarter for your project" trigger. Not a 5 because the 'when' is broad and could name concrete triggering situations (e.g., after accumulated fixes/patches); comfortably above a 3 since the when is explicit, not merely implied.

Trigger Term Quality

Good natural keyword coverage: "Self-improve", "skills", "patches", "project context", and the user-voiced trigger "when you want to make AI smarter for your project". Not a 5 because common synonyms a user might say ("improve your skills", "learn from past fixes", "skill evolution") are missing.

Distinctiveness Conflict Risk

A clear niche — evolving AI Factory skills from accumulated patch history — with distinct triggers unlikely to fire for unrelated skills. Minor overlap risk remains with closely related AI Factory skills (e.g., aif-improve, aif-skill-generator) whose descriptions could compete for 'improve skills' requests.