Skill Audit

Skill Audit

Use this skill to audit existing skills, turn workflow knowledge into useful agent skills, and review skill libraries at the strategy level. It complements skill 编写与 eval: use this skill to decide what a skill should be, how it should fit a library, and what needs improvement; use skill 编写与 eval when the user wants the concrete SKILL.md implementation and eval loop.

This workflow is based on Anthropic's June 3, 2026 blog post, "Lessons from building Claude Code: How we use skills": https://claude.com/blog/lessons-from-building-claude-code-how-we-use-skills

Core Principle

A good skill is not "some markdown about a topic." It is a compact extension point that gives the agent non-obvious domain knowledge, reusable files, deterministic helpers, setup rules, verification habits, and guardrails at the moment they matter.

Workflow

1. Decide Whether This Should Be A Skill

Create or improve a skill only when at least one of these is true:

• The workflow repeats often enough that users should not re-explain it.
• The agent regularly makes the same domain-specific mistake.
• The work needs local scripts, templates, examples, assets, hooks, or setup.
• The output must follow a stable structure or verification path.
• The knowledge is team-specific, product-specific, infrastructure-specific, or otherwise not inferable from general model knowledge.

Do not make a skill when the content only restates obvious coding behavior, generic best practices, or one-off instructions.

2. Classify The Skill

Read references/skill-taxonomy.md and classify the candidate into exactly one primary category. If it appears to span several categories, tighten the scope or split it.

Report:

• Primary category
• Secondary category, if truly needed
• Why this category is the cleanest fit
• What would make the skill too broad

3. Draft A Skill Brief

Use assets/skill-brief-template.md for the output. Fill it with:

• Trigger description written for the model, not as a human-facing summary
• High-signal knowledge the model would not otherwise know
• Gotchas and failure modes
• Autonomy boundaries: what the skill may do directly, and what must be escalated before acting
• Evidence-backed pushback rules: when the agent should challenge the proposed path and what evidence it must cite
• Feedback loop: where repeated corrections, false-success signals, or manual recovery steps should be promoted
• Progressive disclosure map: SKILL.md vs references vs scripts vs assets
• Setup requirements or config questions
• Verification strategy
• Distribution path
• Measurement plan

4. Design Progressive Disclosure

Keep SKILL.md focused on activation, decisions, and the main workflow. Move details into support files:

• references/ for tables, API conventions, taxonomy, playbooks, and long docs
• scripts/ for deterministic actions or repetitive checks
• assets/ for templates, report formats, starter files, or examples
• agents/ for specialized subagent prompts when the repo supports them
• evals/ for realistic prompts and objective assertions

Tell the agent exactly when to read each support file.

5. Add Operational Design

Read references/writing-and-operations.md when deciding:

• Whether a setup step or config file is needed
• Whether the skill should remember past runs
• Whether scripts or hooks would improve reliability
• Whether the skill belongs in a repo, a shared plugin, or a marketplace
• What usage signals indicate undertriggering, overtriggering, or decay

6. Hand Off To Implementation

When the user wants the skill built, pass the brief into skill 编写与 eval and ask it to implement the files, generate realistic test prompts, and run validation.

Auditing this repo's skill library: read references/library-audit.md and run scripts/validate-skills.sh from the repository root.

If editing an existing skill, include the exact file paths and the smallest content changes needed. Do not rewrite unrelated skill behavior.

Output format and gotchas: references/audit-output.md.