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Get Started Free →Audit, design, categorize, distribute, and measure agent skills using lessons from Anthropic's Lessons from building Claude Code: How we use skills. Use when reviewing an existing skill, deciding whether a workflow deserves a skill, planning a skill library, turning team knowledge into skills, choosing skill categories, writing trigger descriptions, designing progressive disclosure, or planning skill marketplace and usage measurement.
.claude/skills/majiayu000-skill-audit/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 43% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -68% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 66% | 0% |
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-creator: use this skill to decide what a skill should be, how it should fit a library, and what needs improvement; use skill-creator 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
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.
Create or improve a skill only when at least one of these is true:
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.
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:
Use assets/skill-brief-template.md for the output. Fill it with:
escalated before acting
path and what evidence it must cite
recovery steps should be promoted
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 docsscripts/ for deterministic actions or repetitive checksassets/ for templates, report formats, starter files, or examplesagents/ for specialized subagent prompts when the repo supports themevals/ for realistic prompts and objective assertionsTell the agent exactly when to read each support file.
Read references/writing-and-operations.md when deciding:
When the user wants the skill built, pass the brief into skill-creator and ask it to implement the files, generate realistic test prompts, and run validation.
If editing an existing skill, include the exact file paths and the smallest content changes needed. Do not rewrite unrelated skill behavior.
For agent-workflow, delivery, PR, automation, or high-impact skills, use skill-lifeguard or apply the same five-element score:
Report each element as present, partial, missing, or deferred. A missing element is not always a blocker, but it must be visible in the brief and patch plan.
For advisory requests, answer with:
trigger description
For repository work, actually create or update the files, then run the repo's skill validation command.
checklists, templates, and verification.
files.
phrases and contexts that should trigger the skill.
decision criteria, and escape hatches.
validation commands, example prompts, expected artifacts, or usage metrics.
escalation boundaries, and end-state checks that should change behavior.
checkpoints, done conditions, replay or smoke hooks, and drift signals.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 26,126 | 31,986 | +22% | 1 | 1 | 0% | 4,215 | 5,586 | +33% | 0 | 0 | — |
case-02 | fail→pass | 17,921 | 16,763 | -6% | 1 | 1 | 0% | 2,771 | 3,839 | +39% | 0 | 0 | — |
case-03 | fail→pass | 21,137 | 22,610 | +7% | 1 | 1 | 0% | 3,345 | 4,771 | +43% | 0 | 0 | — |
case-04 | fail→pass | 26,717 | 2,943 | -89% | 1 | 1 | 0% | 5,525 | 1,779 | -68% | 0 | 0 | — |
case-05 | fail→pass | 11,913 | 11,704 | -2% | 1 | 1 | 0% | 1,865 | 3,096 | +66% | 0 | 0 | — |
case-06 | pass→pass | 14,640 | 15,131 | +3% | 1 | 1 | 0% | 2,183 | 3,377 | +55% | 0 | 0 | — |
case-07 | fail→pass | 13,883 | 6,023 | -57% | 1 | 1 | 0% | 1,703 | 2,428 | +43% | 0 | 0 | — |
case-08 | fail→pass | 14,596 | 7,353 | -50% | 1 | 1 | 0% | 2,005 | 2,549 | +27% | 0 | 0 | — |
case-09 | fail→pass | 12,155 | 2,984 | -75% | 1 | 1 | 0% | 1,798 | 1,804 | +0% | 0 | 0 | — |
case-10 | fail→pass | 10,337 | 2,058 | -80% | 1 | 1 | 0% | 1,621 | 1,639 | +1% | 0 | 0 | — |
case-11 | pass→pass | 9,687 | 6,066 | -37% | 1 | 1 | 0% | 1,445 | 2,244 | +55% | 0 | 0 | — |
case-12 | pass→pass | 13,818 | 10,541 | -24% | 1 | 1 | 0% | 1,876 | 2,909 | +55% | 0 | 0 | — |
case-13 | pass→pass | 12,484 | 6,901 | -45% | 1 | 1 | 0% | 1,937 | 2,365 | +22% | 0 | 0 | — |
case-14 | pass→pass | 11,768 | 7,694 | -35% | 1 | 1 | 0% | 1,854 | 2,543 | +37% | 0 | 0 | — |
case-15 | fail→pass | 8,049 | 1,754 | -78% | 1 | 1 | 0% | 1,238 | 1,559 | +26% | 0 | 0 | — |
case-16 | fail→pass | 4,535 | 1,309 | -71% | 1 | 1 | 0% | 571 | 1,491 | +161% | 0 | 0 | — |
case-17 | fail→pass | 10,888 | 1,754 | -84% | 1 | 1 | 0% | 1,625 | 1,590 | -2% | 0 | 0 | — |
case-18 | fail→pass | 12,298 | 7,813 | -36% | 1 | 1 | 0% | 1,797 | 2,389 | +33% | 0 | 0 | — |
case-19 | fail→fail | 9,572 | 2,679 | -72% | 1 | 1 | 0% | 1,346 | 1,789 | +33% | 0 | 0 | — |
case-20 | pass→pass | 7,342 | 2,438 | -67% | 1 | 1 | 0% | 1,083 | 1,676 | +55% | 0 | 0 | — |
case-21 | pass→pass | 14,899 | 11,136 | -25% | 1 | 1 | 0% | 2,252 | 2,952 | +31% | 0 | 0 | — |
case-22 | fail→pass | 14,932 | 5,600 | -62% | 1 | 1 | 0% | 2,302 | 2,258 | -2% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +64 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.