本文へ移動
cccskills
無料GitHub で公開

audit-skill-completeness

Evaluate a single skill's quality against its stated purpose. Classifies the skill's purpose type, then scores applicable quality categories — universal dimensions always apply; structural dimensions (scripts, references, assets) are scored only when warranted by the skill's purpose. A focused 20-line behavioral skill with no bundled resources scores fully on all applicable categories. Use when auditing skill quality, checking marketplace readiness, evaluating skill completeness, performing pre-publication evaluation.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md13.6 KB
  • references/skill-completeness-checklist.md47.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

If the user's intent does not match this skill, route through /plugin-creator:plugin-lifecycle.

Audit Skill Completeness

Core Principle

The primary evaluation question is: "Does this skill have everything it needs to achieve its stated purpose reliably?"

Scripts, references, and assets are extension patterns — each is warranted when the skill's purpose calls for it and unnecessary when it does not. Their absence is only a gap when the purpose requires them. A small, complete, purpose-aligned skill scores as complete; a skill with unused scripts and padding scores lower than one with clear, sufficient instructions.

When to Use

  • Pre-marketplace publication review — verify skill meets quality standards
  • Post-creation quality check — evaluate newly created skills
  • Skill improvement planning — identify specific quality gaps
  • Marketplace readiness assessment — determine if skill is publication-ready

Workflow

Step 1: Discovery

Read the skill directory structure:

skill-path/
├── SKILL.md          # Required - main skill definition
├── scripts/          # Optional - executable automation (when warranted)
├── references/       # Optional - supporting documentation (when warranted)
└── assets/           # Optional - reusable output resources (when warranted)

Actions:

  1. Verify SKILL.md exists; report error and exit if missing
  2. Read SKILL.md frontmatter and body
  3. Note any scripts/, references/, assets/ directories and their contents

Step 2: Classify purpose type

Classify the skill into one of these purpose types based on the frontmatter description and body:

Purpose TypeCharacteristicsScriptsReferencesAssets
Behavioral / enforcementTeaches Claude how to approach a problem; enforces standards or constraints through instructionsNot warranted — behavior is internalized, not scriptedOnly if domain-specific lookup neededNot warranted
Tool / format wrappingWraps fragile operations on file formats (DOCX, PDF, XLSX, images)Warranted — fragile transforms benefit from deterministic scriptsWarranted — format specs, schemasOften warranted — templates
Workflow orchestrationMulti-step process coordination; skill-creation, plugin lifecycleOften warranted — scaffolding scripts usefulOften warranted — process patternsSometimes
Reference / knowledgeIS the reference material; provides domain knowledgeNot warrantedThe reference files ARE the productNot warranted
Domain expertiseEncodes expert conventions (financial modeling, legal drafting)Not warrantedWarranted — lookup tables, standardsNot warranted

If the skill spans multiple types, apply the union of warranted categories.

Step 3: Evaluate agentskills.io best practices (primary)

Apply the best-practice checks from ./references/skill-completeness-checklist.md — section "agentskills.io Best Practice Checks". Rate each PASS / PARTIAL / FAIL with evidence from SKILL.md.

CheckQuestion
1. Approach vs OutputIs the skill scoped to a class of problems or a narrow one-shot recipe?
2. Lean InstructionsDoes the skill over-specify, adding rules that narrow behavior without improving outcomes?
3. Reasoning over DirectivesDoes the skill explain the why behind its rules, or rely on bare imperatives?
4. Description Trigger AccuracyDoes the description generate a clear should-trigger / should-not-trigger boundary?
5. Bundle SignalAre repetitive operations bundled, or will the agent re-implement them each run?

For each check: state the verdict, cite specific evidence (file:line where possible), and note what an eval would test.

Step 4: Evaluate structural quality categories (secondary)

The categories below are universal (always scored) or conditional (scored only when warranted by purpose; marked N/A otherwise).

Universal categories:

CategoryEvaluates
PreparationPrerequisites met before work begins
ProgressionConcrete steps with right level of control
VerificationOutput correctness confirmed before declaring success
ExamplesTeaching through demonstration
Anti-PatternsExplicit "what NOT to do" documentation

Conditional categories — apply warranted test first:

CategoryWarranted when...
ScriptsThe skill involves operations that are fragile, error-prone, or would be rewritten by the agent each invocation; deterministic code improves reliability
ReferencesThe skill requires domain-specific knowledge (API formats, schemas, conventions, standards) that an AI cannot reliably generate from training data
AssetsThe skill produces output that uses templates, fonts, images, or boilerplate that should be bundled for use (not read into context)

For each category:

  1. Warrant check (conditional categories only — Scripts, References, Assets):
    • Ask: is this category warranted for this skill's purpose type (from Step 2)?
    • If NO → record N/A. STOP. Do not execute steps 2–5. Do not add to score denominator. Do not list absence as a gap. Do not recommend adding it.
    • If YES → continue to step 2.
    • Universal categories (Preparation, Progression, Verification, Examples, Anti-Patterns) always continue to step 2.
  2. Read the category definition from ./references/skill-completeness-checklist.md
  3. Review checklist items for that category
  4. Search SKILL.md and supporting files for evidence
  5. Score 0–3 based on rubric (below)
  6. Document findings with file:line references

Step 5: Suggest eval scenarios

You do not have the domain context needed to author high-quality evals. Suggest scenarios that a domain expert can use as starting points.

For each best-practice check rated FAIL or PARTIAL, describe 1–2 prompts that would expose the gap (use the eval-type mapping in ./references/skill-completeness-checklist.md).

Additionally suggest:

  • 3–5 behavioral scenarios — prompts where the skill would be active; note what the assertion should check (the agent's approach, not exact output)
  • 2–3 should-trigger queries — non-obvious prompts where the skill SHOULD activate (tests description trigger accuracy)
  • 2–3 should-not-trigger queries — prompts at the edge of scope where the skill SHOULD NOT activate

Format each as a short paragraph: the prompt idea, what makes it a good test, what a passing response demonstrates. Do NOT write JSON — the skill author needs domain knowledge to fill in the assertions.

Step 6: Score and report

Calculate overall structural score. Denominator = 15 (universal) + 3 × (number of applicable conditional categories).

Write report to .tmp/scratch/reports/skill-sync-{slug}-completeness-YYYYMMDD.md. Create .tmp/scratch/reports/ if it does not exist.

Report Structure:

# Skill Completeness Report: {skill-name}

**Evaluated:** {timestamp}
**Skill Path:** {path}
**Purpose type:** {type}
**Conditional categories applicable:** Scripts={Yes|No}, References={Yes|No}, Assets={Yes|No}

## agentskills.io Best Practice Checks

| Check | Verdict | Evidence |
|-------|---------|----------|
| 1. Approach vs Output | PASS/PARTIAL/FAIL | {evidence} |
| 2. Lean Instructions | PASS/PARTIAL/FAIL | {evidence} |
| 3. Reasoning over Directives | PASS/PARTIAL/FAIL | {evidence} |
| 4. Description Trigger Accuracy | PASS/PARTIAL/FAIL | {evidence} |
| 5. Bundle Signal | PASS/PARTIAL/FAIL | {evidence} |

## Suggested Eval Scenarios

{Short paragraph per scenario: prompt idea, why it's a good test, what a passing response demonstrates.
Gap-coverage: {N} | Behavioral: {N} | Should-trigger: {N} | Should-not-trigger: {N}}

## Structural Score: {score}/{applicable-max} ({percentage}%)

| Category | Applicable | Score | Label | Findings |
|----------|-----------|-------|-------|----------|
| 1. Preparation | Yes | 2 | Adequate | Environment checks present |
| 2. Progression | Yes | 3 | Exemplary | Clear workflow, decision tree |
| 3. Verification | Yes | 2 | Adequate | Steps defined, no automation |
| 4. Scripts | No | N/A | — | Not warranted for this skill type |
| 5. Examples | Yes | 2 | Adequate | Working examples, common cases covered |
| 6. Anti-Patterns | Yes | 3 | Exemplary | Failure modes with corrections |
| 7. References | No | N/A | — | Not warranted for this skill type |
| 8. Assets | No | N/A | — | Not warranted for this skill type |

## Category Details

### 1. Preparation (2/3 - Adequate)

**Evidence found:**
- ✅ Environment check at SKILL.md:45-50
- ✅ Input validation at SKILL.md:65
- ❌ Missing: {specific gap}

**Recommendation:**
{Concrete recommendation}

### 4. Scripts (N/A — not warranted)

This skill enforces behavior through instructions Claude internalizes. Scripts are not warranted.
No gap. No recommendation.

## Recommendations for Improvement

Only list recommendations for applicable categories with scores below 3, or best-practice checks
rated FAIL:

1. **High Priority:** {recommendation} (Category or Check)
2. **Medium Priority:** {recommendation} (Category or Check)

Output Location: .tmp/scratch/reports/skill-sync-{slug}-completeness-YYYYMMDD.md. Create .tmp/scratch/reports/ if it does not exist.

Scoring Rubric

Each applicable category is scored 0–3:

ScoreLabelMeaning
0NoneCategory not addressed — no evidence found
1MinimalBasic attempt, significant gaps
2AdequateMeets expectations, minor gaps
3ExemplaryFully addressed, matches Anthropic quality patterns

Universal category guidelines:

  • Preparation (0-3):

    • 0: No environment checks, no input validation
    • 1: Environment checks OR input validation present
    • 2: Both environment checks AND input validation present
    • 3: Full prerequisite verification, input inspection, structured pre-flight
  • Progression (0-3):

    • 0: No clear workflow defined
    • 1: Workflow mentioned but steps are vague or incomplete
    • 2: Clear step sequence with decision points
    • 3: Clear steps, decision trees, degree-of-freedom calibration appropriate to task fragility
  • Verification (0-3):

    • 0: No verification steps mentioned
    • 1: Manual verification suggested but not specified
    • 2: Verification steps defined with acceptance criteria
    • 3: Explicit error-correction loop (verify → fix → re-verify), expressed either as behavioral instructions Claude follows or as automated scripts — either form satisfies this level
  • Examples (0-3):

    • 0: No examples provided
    • 1: Abstract descriptions or pseudocode only
    • 2: Concrete examples covering common cases
    • 3: Examples covering common AND edge cases, with exact input→output pairs
  • Anti-Patterns (0-3):

    • 0: No anti-patterns documented
    • 1: Anti-patterns mentioned but not demonstrated
    • 2: Anti-patterns shown with corrections
    • 3: Anti-patterns shown with corrections AND reasoning for why the wrong form fails

Conditional category guidelines (only when warranted):

  • Scripts (0-3, or N/A):

    • N/A: Skill's purpose does not involve deterministic or repetitive operations that benefit from scripting
    • 0: Operations clearly need scripting (agent would regenerate the same logic each invocation) but no scripts provided
    • 1: Scripts exist but the operations most likely to be regenerated wastefully remain unscripted
    • 2: Scripts cover the operations the agent would otherwise rewrite each run; eliminates the primary sources of wasted agent turns
    • 3: All deterministic operations that the agent would otherwise regenerate are scripted; scripts are self-documenting (--help) and handle edge cases — the agent never writes boilerplate
  • References (0-3, or N/A):

    • N/A: Skill's purpose does not require domain knowledge beyond training data
    • 0: Warranted but no reference material bundled
    • 1: External links only — not bundled for offline use
    • 2: Reference files bundled, organized by topic
    • 3: Reference files linked from the specific workflow steps where they're needed
  • Assets (0-3, or N/A):

    • N/A: Skill's output does not depend on templates, fonts, images, or boilerplate resources
    • 0: Output clearly requires bundled resources but none provided — agent generates required materials ad-hoc each invocation
    • 1: Some assets bundled but key output resources the agent needs are still generated ad-hoc
    • 2: Main output resources bundled; agent can produce its primary output without generating supporting materials
    • 3: All output resources the agent needs are bundled; agent never generates supporting materials ad-hoc; asset usage is clearly specified

Quality Categories Reference

Detailed checklist items and Anthropic skill examples: ./references/skill-completeness-checklist.md

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月9日 更新

SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into an executable SAM plan.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月9日 更新

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月9日 更新

Browser automation for AI agents using the agent-browser CLI and Playwright. Use when navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, logging into sites, or automating any browser task. Triggers on "open a website", "fill out a form", "click a button", "scrape data", "test this web app", "automate browser actions", or any programmatic web interaction request.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月9日 更新

Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月9日 更新

Create or adapt Claude Code agent definitions. Use when creating an agent, changing subagent configuration, selecting agent scope, or designing a specialized delegation role.

日本語の概要は準備中です。原文の説明を表示しています。

Jamie-BitFlight/claude_skills672026年10月9日 更新

Jamie-BitFlight のスキルをすべて見る

このスキルの問題を報告する