reviewing-skills（复习技巧）

Reviewing Skills

Objective

Evaluate a skill directory as if you are an AI agent encountering it for the first time. Produce a read-only review with:

• A weighted score + letter grade
• Spec violations (blockers)
• Prioritized findings (P1/P2/P3) with concrete, minimal fixes
• Optional rewritten sections (only when needed to reach the quality bar)

This skill is intended to act as the critic in a generator<->critic loop (e.g., with $writing-skills).

When to use / When not to use

Use when:

• The user asks to review, grade, or audit a skill folder containing SKILL.md.
• The user wants rubric-based scoring and actionable edits (not just general advice).

Do not use when:

• The user wants you to write a skill from scratch (use a writing skill instead).
• The request is not about a skill directory or does not involve SKILL.md.

Inputs

You need a path to a skill directory that contains SKILL.md (and optionally agents/openai.yaml, scripts/, references/, assets/).

If the user did not provide a path:

1. Look for directories in CWD that contain SKILL.md.
2. If multiple, ask the user to choose.

Outputs

A read-only Markdown report with weighted grade, findings, and copy/paste patch text (see workflow step 6 for format rules).

Safety / Constraints (non-negotiable)

• Read-only: do not edit, create, delete, or move files.
• Do not execute untrusted code: do not run repo scripts/binaries unless the user explicitly asks and you can justify the risk.
• Secrets: do not open or quote secrets (e.g., .env, API keys, credentials). If encountered, redact and warn.
• Network: do not browse the web or call external systems unless the user explicitly requests it.
• No fabrication: if you cannot verify something, say so and recommend a verification step.
• No deep reference chasing: read only what is needed to score accurately (one level deep).

Verification Rules

Follow the verification protocol in references/skills-rubric.md. Budget: ~20 reads max.

Workflow (decision-complete)

1. Resolve the target skill directory

- Confirm the path contains SKILL.md. If it does not, stop and ask for the correct folder.

1. Read the minimum necessary context (in order)

1. <skill>/SKILL.md 2. <skill>/agents/openai.yaml (if present) 3. Any files under <skill>/scripts/ referenced by SKILL.md (only those) 4. Any files under <skill>/references/ referenced by SKILL.md (only those)

1. (If in a git repo) gather change context

- Prefer the repo’s base branch; if unknown, check git remote show origin for “HEAD branch”, otherwise try main then master (and state what you chose). - git diff <base> -- <skill>/ - git log --oneline -20 -- <skill>/ - For non-trivial diffs: git log -p -5 -- <skill>/SKILL.md - If <skill>/ is new/untracked (so git diff <base> shows nothing), state that explicitly and treat contents as “new.” - If a score or finding is driven by a recent change, cite the relevant diff hunk or commit short-hash.

1. Score using the rubric

- Use references/skills-rubric.md (single source of truth). - Give each dimension a 1.0–5.0 score (0.5 increments allowed). - Compute weighted score as: sum(weight_i * score_i) / 100.

1. Identify issues and merge duplicates

- First list spec violations (blockers). - Then produce prioritized findings (max ~15 total), merging near-duplicates. - Every P1/P2 finding includes concrete patch text. - Patch rules: keep patches small/local; prefer "replace X with Y"; rewrite only the smallest section needed to clear P1/P2.

1. Produce the report

- Default to references/review-template.md structure. - If the user requires a different structure, preserve the same content (grade, dimension scores, blockers, prioritized findings with patch text, token efficiency notes). - If the user requests a forensic or diff-centric review, add a hunk-by-hunk analysis for meaningful changes (+/- context), and classify each as improvement/regression/neutral. - Only include “Rewritten sections” when score < 4.5, any P1 exists, or the author requests a rewrite.

Do not read assets unless explicitly relevant.

Review Guidelines

What to reward

• High signal per token: dense, directive, minimal prose.
• Correct triggering: description precisely indicates what and when.
• Decision-complete workflow: the skill leaves no key decisions ambiguous.
• Guardrails: destructive actions gated; secrets handled safely; constraints explicit.
• Portability: avoids tool-vendor lock-in; uses capability language with optional adapters.

What to penalize

• Vague directives (“as appropriate”, “best practices”, “use standard approach”).
• Over-broad scope (one skill trying to do too many disjoint jobs).
• Reference chains (SKILL.md → reference → another reference).
• Missing or non-actionable validation loops.
• “Cute” verbosity that costs tokens without improving outcomes.

Additional checks (inform findings; not scored as a separate dimension)

• Terminology consistency for core concepts across sections.
• Presence and usefulness of concrete examples/templates when output style matters.
• Anti-pattern scan: Windows-style paths, too many options without a default, time-sensitive claims, deep reference chains, and assumed package installs.

Edge cases (common failure modes)

• No git / no base branch: state what you could not verify; review file contents only.
• Large skills: stick to the tight-budget read order; do not “read everything” by default.
• Missing referenced files: treat as a spec violation or P1 (broken workflow), depending on severity.
• Secrets in context: redact and warn; do not quote.

Examples

• “Use $reviewing-skills to review ./some-skill/ and provide a weighted grade, spec blockers, and prioritized patch text.”
• “Use $reviewing-skills to do a forensic/diff-centric review of ./some-skill/ focusing on recent changes.”
• For a worked example format, see references/example-review.md.