Announce: "I'm using the skill-review skill to evaluate skill quality."

Skill Review

Philosophy

Modern agents (Opus 4.6, Sonnet 4.5) are highly capable reasoners. Skills should provide context the agent DOESN'T already have — project-specific knowledge, non-obvious conventions, fragile sequences. They should NOT explain general concepts, babysit through obvious steps, or micromanage decisions the agent can make better in context.

The deletion test: if you removed a sentence and the agent would still do the right thing, that sentence is noise.

The preservation test: if a section contains specific values (country codes, error strings, exact enum cases, character pairs, file paths), assume it encodes a debugging discovery until proven otherwise. Domain knowledge looks like noise to outsiders — the author had a reason. Your job is to find it or ask, not assume it's absent.

Knowing When You Can't

If you lack domain context to judge most of a skill's content, say so upfront. Scope the review to what you can evaluate — structure, description quality, staleness — and flag the rest as beyond your confidence. A partial honest review beats a complete fabricated one.

Evaluation Axes

1. Signal-to-noise ratio

Does each section teach something the agent doesn't already know? Noise candidates:

• Explanations of general concepts (what TDD is, how HTTP works)
• Procedural hand-holding where the agent can reason from goals
• Redundancy with AGENTS.md / CLAUDE.md rules

Every token competes with conversation history for context window space.

Escalation rule: when you can't explain WHY a section exists — it looks like a random detail — that's a signal to mark it "ask user" rather than "noise." Intentional redundancy (e.g., a critical warning repeated at two decision points) is a pattern, not a mistake.

2. Description quality (CSO)

• Describes WHEN to trigger, not WHAT the skill does
• Avoids summarizing workflow (causes agents to shortcut actual content)
• Third-person, specific, keyword-rich
• Would the agent select this skill from 100+ candidates given a matching task?

3. Degrees of freedom

• High freedom for judgment calls (architecture, review, design)
• Low freedom ONLY for fragile operations (exact CLI commands, paths, sequences that break if reordered)
• Over-constraining: step-by-step for tasks agents handle naturally
• Under-constraining: vague guidance for operations needing precision

4. Progressive disclosure

• SKILL.md is a lean overview pointing to details
• Supporting files for heavy reference (100+ lines)
• References one level deep (no chains)

5. Practical effectiveness

• Does the skill actually change behavior vs. what the agent would do without it?
• Are guardrails based on observed failures or hypothetical ones?
• Would a fresh agent instance find and use this successfully?

6. Staleness

• Do referenced file paths, module names, APIs still exist?
• Are code examples accurate against current codebase?

Output Format

Per skill:

**Skill:** `name`
**Verdict:** Keep as-is / Needs refinement / Needs rewrite / Consider retiring

**Section-by-section** (every section must appear):
- [signal / noise / ask user]: [section name]
  Reason: [specific: duplicates AGENTS.md §X / general knowledge / encodes gotcha / ...]

**Strengths:**
- [What works well]

**Suggested changes** (only for sections marked noise, with specific fix)

Rules for the section-by-section audit:

• Every section gets a verdict — no skipping with a blanket percentage
• "noise" requires a specific reason (not "verbose" or "could be shorter")
• "ask user" is mandatory when you suspect domain knowledge but can't confirm
• Do NOT use percentage-based noise estimates — they sound authoritative but are consistently wrong and lead fixers to over-cut

What NOT to Flag

• Don't enforce rigid structural templates — structure serves content
• Don't penalize short skills — 30 lines of signal beats 300 lines of padding
• Don't require sections that don't apply (no empty "Common Mistakes" sections)
• Don't flag missing tests — that's a separate concern from content quality