# Doc Completeness Audit > Audit documentation completeness by mapping what a doc set should cover against what it actually covers. Produces a prioritized gap report by topic, not just by file. This skill should be used after shipping features, before releases, or when users report missing documentation. - Skill: `nickcrew/doc-completeness-audit` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds add nickcrew/doc-completeness-audit` - Raw SKILL.md: https://api.skillmd.com/api/skills/nickcrew/doc-completeness-audit/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: nickcrew (https://skillmd.com/u/nickcrew) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/nickcrew/doc-completeness-audit --- # Documentation Completeness Audit Determine whether a documentation set covers everything it should by building an inventory of what *needs* documenting and comparing it to what *exists*. The output is a prioritized gap report — not new documentation. ## When to Use - After shipping a feature — verify docs cover the new surface area - Before a release — ensure no undocumented public APIs, CLI flags, or config options - When users or new hires report "I couldn't find docs for X" - Periodic health check on doc coverage - After running `doc-maintenance` (structural) and `doc-claim-validator` (accuracy) to go wider ## Quick Reference | Resource | Purpose | Load when | |----------|---------|-----------| | `references/coverage-model.md` | Defines what "complete" means per doc type | Always (Phase 1) | --- ## Workflow Overview ``` Phase 1: Inventory → Build the "should exist" list from code and config Phase 2: Map → Match inventory items to existing documentation Phase 3: Classify → Score each gap by audience impact Phase 4: Report → Produce the prioritized gap report ``` --- ## Phase 1: Build the Inventory Construct a list of everything that should be documented. Use four sources, checking all of them: ### Source 1: Public Code Surface Run the bundled inventory script to extract documentable surface area deterministically: ```bash python3 skills/doc-completeness-audit/scripts/inventory.py --root . --json > inventory.json # Or human-readable: python3 skills/doc-completeness-audit/scripts/inventory.py --root . # Run specific detectors only: python3 skills/doc-completeness-audit/scripts/inventory.py --root . --detectors env_vars,cli_commands ``` The script scans source files across Python, JavaScript/TypeScript, Rust, Go, Ruby, Java, and shell, extracting six categories: | Detector | What it extracts | |----------|-----------------| | `env_vars` | Environment variable references (`os.environ`, `process.env`, `env::var`, etc.) | | `cli_commands` | CLI commands and flags (argparse, click, clap, cobra, commander) | | `config_keys` | Configuration key access in config-related files | | `http_endpoints` | HTTP route definitions (Flask, FastAPI, Express, Actix, Axum, net/http) | | `public_exports` | Public module exports (`__init__.py`, `export`, `pub fn`, Go capitalized funcs) | | `error_types` | Custom error/exception class definitions | | Event types, webhooks, callbacks | Every event name and payload shape | Dispatch an Explore agent to scan for these signals. Provide it with the project's primary language and entry points. ### Source 2: User-Facing Features Identify features a user interacts with: - TUI screens, views, keybindings - CLI workflows (multi-step operations) - Integration points (hooks, plugins, extensions) - Authentication/authorization flows - Error messages that imply user action ### Source 3: Operational Surface Identify what operators and maintainers need: - Installation and setup procedures - Upgrade and migration paths - Backup and restore procedures - Troubleshooting common errors - Environment requirements and dependencies - CI/CD integration points ### Source 4: Existing Docs Cross-References Check existing docs for promises of documentation that doesn't exist: - "See [link]" references to pages that don't exist - "Coming soon" or "TODO" markers - Table of contents entries without corresponding pages - Navigation entries without targets ### Source 5: Architectural / Operational / Migration Topic Discovery (sonnet) The first four sources catch *code-detectable* surface (env vars, CLI flags, endpoints, exported APIs, broken cross-references). They miss topics that exist as architectural patterns, user flows, ops procedures, or migration paths but don't surface as a single greppable symbol. Examples: - Architectural patterns the system implements (CQRS, event sourcing, saga) — should be documented but won't show up in inventory.py - User flows implicit across UI surfaces — "how to share a project" may span multiple components and isn't a single CLI command - Migration paths between versions — typically tribal knowledge until someone needs them - Operational runbooks (incidents, rollbacks, capacity events) - Recovery procedures and disaster scenarios Dispatch one `general-purpose` + `sonnet` agent for topic discovery: ``` subagent_type: "general-purpose" model: "sonnet" description: "Architectural/operational topic discovery" ``` Prompt: read README, top-level docs, and a sample of code (architecture files, integration boundaries, deployment configs, major feature directories). Identify topics that *should* be documented but aren't captured by the code-surface inventory. For each topic, name: - `topic` — what needs documenting (one phrase) - `evidence` — what in the codebase implies this topic exists (path:line citations) - `audience` — who would read this (operators, contributors, advanced users) - `type` — reference, tutorial, guide, explanation, runbook - `confidence` — high (clear evidence), medium (inferred), low (speculative) Append the agent's output to the inventory list before Phase 2. **Output:** A structured inventory list. Each item has: - `topic` — what needs documenting - `source` — where the requirement was discovered (code path, config key, user flow, sonnet inference) - `audience` — who needs this (end user, developer, operator) - `type` — what kind of doc it needs (reference, tutorial, guide, explanation, runbook) - `confidence` — high (deterministic) | medium | low (sonnet-inferred speculative) --- ## Phase 2: Map to Existing Documentation (per-docfile sonnet dispatch) For each inventory item, determine whether it's documented and how well. "Adequate coverage" requires reading surrounding context — a grep hit doesn't tell you whether the topic is truly explained vs. just mentioned in passing. Orchestrator-side execution would require reading every doc *N* times (once per inventory item), which strains the context window. ### Dispatch strategy Two-phase mapping: 1. **Bulk grep pass (orchestrator)** — for each inventory item, grep docs for the topic name. Build a candidate match map: which docs mention each topic. 2. **Per-docfile sonnet pass** — for each docfile that surfaced as a candidate match for any inventory item, dispatch one `general-purpose` + `sonnet` agent. The agent receives the doc + the list of inventory items that grep'd to this doc, and judges each as Documented / Shallow / Misplaced. This keeps total agent calls ≈ N candidate docfiles (not N inventory items × M docs). For a typical project with 100 inventory items and 50 docs, the candidate map usually has 30–50 docs needing review. ### Per-docfile prompt template ``` subagent_type: "general-purpose" model: "sonnet" description: "Coverage mapping for " ``` Prompt: ``` Read the doc at . The following inventory items grep-matched this doc — judge each: For each item, classify as one of: - Documented: dedicated section or page provides adequate coverage - Shallow: mentioned but insufficient (missing examples, edge cases, parameter listings; flag-in-table without explanation) - Misplaced: covered, but in the wrong doc type for the audience (API reference embedded in a tutorial; user-facing topic in dev-only docs) - No real match: grep matched but the doc doesn't actually cover the topic (incidental mention, different concept with the same word) Output as YAML: doc_path: items_reviewed: N classifications: - item: classification: Documented | Shallow | Misplaced | No real match section: evidence: gap: ``` ### Items with no candidate match Inventory items that grep'd 0 docs go directly to the "Missing" bucket without a sonnet review. The orchestrator handles these in Phase 3. ### Why per-docfile rather than per-item Per-item dispatch (one sonnet call per inventory item, reading every candidate doc fresh) blows up at any meaningful scale (100 items × 5 candidates = 500 calls). Per-docfile lets the agent see all related items in one pass and cross-reference within the doc — also higher precision than fragmented per-item judgments. --- ## Phase 3: Classify Gaps by Impact Not all gaps are equal. Score each gap using audience impact: ### Priority Framework | Priority | Criteria | Example | |----------|----------|---------| | **P0** | User cannot accomplish a core task without this | No installation guide, undocumented required config | | **P1** | User can work around it but wastes significant time | CLI flag exists but undocumented, error message without troubleshooting | | **P2** | Missing docs for secondary features or advanced use cases | Plugin API undocumented, advanced config options missing | | **P3** | Missing docs for edge cases or rarely used features | Obscure env var, deprecated feature migration path | | **P4** | Nice to have — explanatory content, design rationale | Architecture decision records, "why" behind defaults | ### Audience Weighting Apply a multiplier based on audience: | Audience | Weight | Rationale | |----------|--------|-----------| | New users / onboarding | 1.5x | First impressions; high abandonment risk | | Daily users | 1.0x | Core audience | | Advanced users / contributors | 0.8x | Can read source when docs fail | | Internal operators | 0.7x | Can ask the team | A P2 gap for new users (P2 × 1.5 = 3.0) outranks a P1 gap for internal operators (P1 × 0.7 = 2.1). --- ## Phase 4: Produce the Gap Report ### Report Format ```markdown # Documentation Completeness Audit **Audit date:** YYYY-MM-DD **Scope:** [directories or doc sets audited] **Inventory items:** N total **Coverage:** N documented / N shallow / N missing / N misplaced --- ## Summary [2-3 sentences: overall completeness assessment] Coverage by audience: | Audience | Documented | Shallow | Missing | Coverage % | |----------|-----------|---------|---------|------------| | New users | N | N | N | N% | | Daily users | N | N | N | N% | | Contributors | N | N | N | N% | | Operators | N | N | N | N% | --- ## P0 Gaps — Blocking | # | Topic | Audience | Source | Current State | What's Needed | |---|-------|----------|--------|---------------|---------------| | 1 | [topic] | [who] | [code path] | Missing | [what to write] | ## P1 Gaps — High Impact | # | Topic | Audience | Source | Current State | What's Needed | |---|-------|----------|--------|---------------|---------------| ## P2 Gaps — Moderate Impact | # | Topic | Audience | Source | Current State | What's Needed | |---|-------|----------|--------|---------------|---------------| ## P3-P4 Gaps — Low Priority | # | Topic | Audience | Priority | Current State | |---|-------|----------|----------|---------------| --- ## Shallow Coverage Details For each Shallow item, explain what's insufficient: ### [Topic] **Current doc:** [path and section] **Problem:** [what's missing — examples, edge cases, complete reference, etc.] **Recommended action:** [specific improvement] --- ## Misplaced Documentation | Topic | Current Location | Recommended Location | Why | |-------|-----------------|---------------------|-----| --- ## Well-Documented (No Action Needed) [List topics with adequate coverage, grouped by audience, so the report shows the full picture and not just the gaps] ``` --- ## Integration with Other Doc Skills This skill fits into the documentation health pipeline: ``` doc-maintenance → Structural health (links, orphans, folders) doc-claim-validator → Semantic accuracy (do claims match code?) doc-completeness-audit → Topic coverage (is everything documented?) doc-quality-review → Prose quality (is it well-written?) doc-architecture-review → Information architecture (is it findable?) ``` Route gap remediation to the appropriate producer: - Reference gaps → `reference-documentation` - Tutorial gaps → `tutorial-design` - Explanation gaps → `documentation-production` --- ## Anti-Patterns - Do not count files as coverage — a file can exist and say nothing useful - Do not manufacture gaps to look thorough — if coverage is good, say so - Do not audit archived docs (`docs/archive/`) — they are historical - Do not require documentation for internal implementation details — only public surface - Do not treat every function as needing its own doc page — aggregate by topic - Do not conflate "not documented" with "needs documenting" — some things are correctly undocumented (internal helpers, deprecated code scheduled for removal) --- ## Bundled Resources ### Scripts - `scripts/inventory.py` — Extract documentable surface area from any codebase (env vars, CLI commands, config keys, HTTP endpoints, public exports, error types) ### References - `references/coverage-model.md` — Defines coverage expectations per doc type and audience