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documentation-audit

This skill should be used when verifying documentation claims against codebase reality. Triggers on "audit docs", "verify documentation", "check docs", "docs accurate", "documentation drift", "before release", "after refactor", "docs don't match". Uses two-pass extraction with pattern expansion for comprehensive detection.

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aiskillstore/marketplace

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包含的文件（4）

SKILL.md3.5 KB
checklist.md3.2 KB
extraction-patterns.md3.8 KB
skill-report.json31.1 KB

SKILL.md（原文）

安装前可以查看交给 AI 代理的指令内容。

Documentation Audit

Systematically verify claims in documentation against the actual codebase using a two-pass approach.

Overview

Core principle: Low recall is worse than false positives—missed claims stay invisible.

Two-pass process:

Pass 1: Extract and verify claims directly from docs
Pass 2A: Expand patterns from false claims to find similar issues
Pass 2B: Compare codebase inventory vs documented items (gap detection)

Quick Start

Identify target docs (user-facing only, skip plans/, audits/)
Note current git commit for report header
Run Pass 1 extraction using parallel agents (one per doc)
Analyze false claims for patterns
Run Pass 2 expansion searches
Generate docs/audits/AUDIT_REPORT_YYYY-MM-DD.md

Claim Types

Type	Example	Verification
`file_ref`	`scripts/foo.py`	File exists?
`config_default`	"defaults to 'AI Radio'"	Check schema/code
`env_var`	`STATION_NAME`	In .env.example + code?
`cli_command`	`--normalize` flag	Script supports it?
`behavior`	"runs every 2 minutes"	Check timers/code

Verification confidence:

Tier 1 (auto): file_ref, config_default, env_var, cli_command
Tier 2 (semi-auto): symbol_ref, version_req
Tier 3 (human review): behavior, constraint

Pass 2 Pattern Expansion

After Pass 1, analyze false claims and search for similar patterns:

Dead script found: diagnose_track_selection.py
  → Search: all script references → Found 8 more dead scripts

Wrong interval: "every 10 seconds"
  → Search: "every \d+ (seconds?|minutes?)" → Found 3 more

Wrong service name: ai-radio-break-gen.service
  → Search: service/timer names → Found naming inconsistencies

Common patterns to always check:

Dead scripts: scripts/*.py references
Timer intervals: every \d+ (seconds?|minutes?)
Service names: ai-radio-*.service, *.timer
Config vars: RADIO_* environment variables
CLI flags: --flag patterns in bash blocks

Output Format

Generate docs/audits/AUDIT_REPORT_YYYY-MM-DD.md:

# Documentation Audit Report
Generated: YYYY-MM-DD | Commit: abc123

## Executive Summary
| Metric | Count |
|--------|-------|
| Documents scanned | 12 |
| Claims verified | ~180 |
| Verified TRUE | ~145 (81%) |
| **Verified FALSE** | **31 (17%)** |

## False Claims Requiring Fixes
### CONFIGURATION.md
| Line | Claim | Reality | Fix |
|------|-------|---------|-----|
| 135 | `claude-sonnet-4-5` | Actual: `claude-3-5-sonnet-latest` | Update |

## Pattern Summary
| Pattern | Count | Root Cause |
|---------|-------|------------|
| Dead scripts | 9 | Scripts deleted, docs not updated |

## Human Review Queue
- [ ] Line 436: behavior claim needs verification

Detailed References

For execution checklist and anti-patterns: checklist.md For claim extraction patterns: extraction-patterns.md

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academy-guide

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Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

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