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Ln 53 Documentation Auditor

Community
levnikolaevich
ln-53-documentation-auditor

Audits documentation and comments for trust, coverage, consistency and freshness; read-only.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-53-documentation-auditor
Stars
565
Forks
84
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by levnikolaevich on GitHub. Read the source before you install it.

Installation

Install the Ln 53 Documentation Auditor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/levnikolaevich/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/plugins/quality-assurance-suite/skills/ln-53-documentation-auditor .claude/skills/ln-53-documentation-auditor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 53 Documentation Auditor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ln 53 Documentation Auditor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ln 53 Documentation Auditor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Documentation Auditor

Goal: Audit documentation as a read-only evidence system: can a new contributor, operator, user, or coding agent find the right source, trust its claims, and act without hidden context? Review both standalone documents and code comments where they carry public or operational knowledge.

Execution contract: The checklist defines completion. Track each item internally as PENDING, PROVEN with evidence, CLEARED with evidence its condition is absent, or UNPROVEN with a gap; reading, delegation, or tool failure is not proof. Reconcile after each section. Before returning, resolve all PENDING, count only PROVEN and CLEARED, and apply verdict and approval rules to every gap. Preserve intent, scope, and existing authorization. Continue authorized work; ask only for consequential unresolved choices or required external approval. Scale depth to material risk without skipping checks. Preserve dependency and safety order; otherwise choose an appropriate verification method. Accept equivalent user or repository evidence; no other skill, named artifact, or complete lifecycle is required. Preserve source requirement and decision IDs. Bind reused evidence to relevant source versions, dirty changes, configuration, and environment; invalidate only affected claims. On continuation, reconcile task, authorization, current state, and unresolved evidence. For long work, return a compact continuation record or update an already authorized artifact; read-only skills do not persist it. Distinguish artifact readiness, verified behavior, and external-action authority. Prepare authorized work before required approval. If blocked by an instruction, cite its exact source and unresolved boundary; do not invent approval gates from caution.

Tool Routing

NeedPreferred toolUse it whenFallback
Document inventory and hierarchyNative file listing with narrow patternsEstablishing documentation surfaces, generated areas, and navigation structureRepository tree and known entry documents
Links, repeated terms, claims, and contradictionsNative text search, then focused readsFinding references, stale names, duplicated guidance, paths, commands, and configuration keysManual cross-document comparison
Code and configuration truthNative code search, manifests, schemas, and direct file readsA document claims that a path, option, endpoint, behavior, or default existsExecute a safe inspection command when text is insufficient
Freshness and ownershipGit log, blame, and diffDetermining when a claim changed, whether a document tracks active code, or who owns a conventionCurrent code and explicit ownership files
Commands and examplesShell in non-mutating or dry-run modeVerifying help text, command existence, config parsing, generated output, or example syntaxInspect command registration and tests; mark runtime proof unavailable
External factsOfficial documentation, specifications, and release notesA current external API, version, standard, or platform behavior affects correctnessPrimary-source web research; otherwise mark UNVERIFIED

Do not run commands that publish, migrate, deploy, write production state, or rewrite documentation. Generated caches are acceptable only when permitted and disclosed.

Evidence Rules

Claim typeRequired evidence
Repository path, symbol, command, configuration, endpoint, or defaultCurrent repository or safe command output
External API, version, standard, or compatibility statementOfficial source matching the relevant version
Count or coverage statementReproducible query with scope and exclusions
Historical rationaleCurrent decision record or history that still matches implementation
RecommendationA demonstrated reader failure, contradiction, maintenance cost, or operational risk

Absence of documentation is a finding only when a real audience needs the missing knowledge. Judge stale and missing guidance by the reader failure or harm they cause.

Checklist

1. Establish Scope and Audiences

  • Identify documentation entrypoints, public docs, maintainer docs, operational runbooks, generated references, examples, and code-comment surfaces in scope.
  • Identify intended audiences and their concrete tasks: understand, install, configure, operate, troubleshoot, extend, or verify the system.
  • Read applicable repository instructions and detect documentation ownership, generation commands, language policy, and source-of-truth conventions.
  • Separate authored documents from generated, vendored, archived, temporary, and example content before scoring defects.
  • Classify each target as entrypoint/index, reference, how-to, explanation, decision record, generated output, or example; prioritize canonical and claim-dense documents before navigation-only files.
  • Define which code, configuration, schemas, tests, and external contracts can verify documentation claims.
  • Keep the audit read-only and record any unavailable source, command, or environment as a limitation.

2. Check Structure and Discoverability

  • Verify that the root entry document explains purpose, supported use, prerequisites, installation path, and navigation appropriate to its audience.
  • Check hierarchy, headings, table of contents, local navigation, cross-links, anchors, and predictable placement of related material.
  • Verify local paths and anchors, including case sensitivity, and inspect external redirects for a wrong or unavailable destination; a working intentional redirect is not a defect.
  • Identify orphan documents, duplicate entrypoints, circular navigation, deep chains, and important content reachable only by repository search.
  • Check that concepts have one canonical owner and that secondary documents link rather than fork rules, thresholds, or procedures.
  • Verify that generated documentation names its generator and does not invite hand editing that will be overwritten.
  • Check whether large documents can be split or compressed without hiding the sequence or context needed to act safely.

3. Check Relevance, Coverage, and Consistency

  • Compare each document's title, scope statement, and intended audience with its actual content.
  • Judge content by document kind: indexes route without duplicating detail, references optimize exact lookup, how-to guides are safely sequenced, explanations build the right mental model, and decision records preserve context, decision, alternatives, and consequences.
  • Flag filler, repeated summaries, speculative narration, generic generated prose, and ceremonial sections that do not help the intended audience decide or act.
  • Prefer durable intent, contracts, constraints, and operations over copied implementation or business logic; retain only minimal verified code or command examples needed to act safely.
  • Identify off-topic content, scope creep, unexplained prerequisites, hidden assumptions, and sections that no longer support an active task.
  • Check coverage of public behavior, configuration, deployment, migrations, failure recovery, security-sensitive operation, and extension points where applicable.
  • Trace requirements and architecture statements across documents; report contradictions and ambiguous ownership rather than choosing silently.
  • Find obsolete future-tense plans, completed TODOs, removed features, legacy compatibility guidance, old directory layouts, and stale screenshots or examples.
  • Check terminology, entity names, identifiers, capitalization, and lifecycle states for consistency with code and across documents.
  • Verify that warnings, prerequisites, destructive actions, rollback steps, and failure outcomes appear at the point where readers need them.
  • In always-loaded agent instructions, identify redundant formatting prose already enforced by tooling and path-specific detail that can move to supported scoped files. Preserve discovery, non-obvious requirements, and host compatibility.

4. Verify Facts and Examples

  • Extract material claims about paths, files, symbols, versions, counts, commands, flags, environment variables, configuration keys, ports, endpoints, schemas, and defaults.
  • Build a normalized claim ledger before verification and group repeated claims by subject so one repository check can expose both stale copies and cross-document contradictions.
  • Verify each claim against current repository evidence or a safe command; record the exact source and scope of the check.
  • Resolve installed versions from manifests and lockfiles before consulting external documentation or claiming current support.
  • Use official sources for external APIs, deprecations, security standards, platform limits, and compatibility statements.
  • Execute examples only in a safe local or disposable context; otherwise inspect parsing, tests, and expected output and mark them statically verified.
  • Check examples for missing imports, placeholders presented as real values, invalid paths, unsafe secrets, obsolete syntax, and output that no longer matches behavior.
  • Interpret examples, templates, future plans, and external references in their declared context. Verify conditional guidance for supported prerequisites even when currently disabled; do not judge a hypothetical example as deployed behavior.
  • Verify numeric counts with a reproducible query and documented exclusions; avoid preserving aggregate counts that will drift without automation.
  • Recommend moving change-prone versions, paths, defaults, inventories, generated output, and status snapshots to authoritative code, configuration, or generated sources where practical; otherwise identify the source, scope, and owner or generation/update trigger needed to prevent silent staleness.
  • Apply the research-to-action gate: external guidance becomes a finding only when it proves a concrete in-repository defect or risk.

5. Review Comments and Produce the Report

  • Check comments and docstrings for useful rationale, invariants, contracts, side effects, failure behavior, and non-obvious constraints.
  • For documented deliberate simplifications with material limits, verify supported bounds, an observable revisit trigger, and what to reconsider. Report missing bounds or triggers, exceeded limits, and overdue reviews with evidence and impact; require no special marker and do not label undocumented routine code as debt.
  • Match docstrings for public or non-obvious interfaces against the current signature, parameters, return value, raised errors, side effects, nullability, and lifecycle obligations.
  • Flag syntax narration, obsolete history, dead code, false promises, and information better expressed by names or types; preserve historical rationale that still explains a constraint or prevents a known regression.
  • Check public interfaces for documentation required by the language or ecosystem and for examples that match the actual contract.
  • Filter intentional audience simplification, framework convention, generated prose, and accepted legacy constraints before reporting.
  • Classify every in-scope document or comment surface as KEEP, ADD, UPDATE, DELETE, or MERGE; recommend deletion or consolidation only when canonical coverage preserves every required audience task and contract.
  • Apply the materiality gate: require concrete reader error, blocked task, unsafe action, contradiction, or recurring maintenance cost at evidenced scale. Reject taste, theoretical purity, generic practice, hypothetical scale, and reasonable alternatives; require the outcome or constraint, not a preferred implementation.
  • External correction evidence: Ground external corrections in version-matched official contracts, using primary engineering sources for unresolved tradeoffs. Cite the supported mechanism; local evidence suffices for local defects.
  • For each accepted finding, identify the owning documentation surface, affected reader outcome, and evidence that a correction would restore trust; do not turn recommendations into authorized edits.
  • Classify findings as P0-P3 based on the harm caused by wrong action, inability to operate, maintenance drift, or minor friction.
  • Use BLOCKED when a safety-critical claim, required audience journey, or authoritative source cannot be verified without a credible fallback; use FAIL when evidence proves unsafe guidance, an inoperable required journey, or an unresolved P0/P1; use CONCERNS only for material non-blocking trust gaps, and PASS only when required claims and journeys are verified with no material finding.

Self-Check

  • Reconcile before returning. Check item-level evidence, requirement coverage, contradictions, scope, verdict, and applicable cleanup. Correct the report or authorized artifacts. Reuse valid evidence; do not automatically rescan the repository or rerun successful commands. Repeat checks only for relevant changes, failures, or unresolved evidence. Disclose remaining gaps.

Output Contract

Report in the user's language, in this order; retain all five fields and state each fact once. Small results may use one line per field; omit empty tables and do not copy linked artifacts:

  1. Result: Skill-specific verdict and supported outcome.
  2. Scope: Reviewed/changed scope, exclusions, baseline, and material assumptions.
  3. Evidence: Skill-specific fields below; distinguish facts, inferences, and unverified claims. Link artifacts; use tables when useful.
  4. Verification: Checks/results, unavailable evidence, and applicable cleanup/external state.
  5. Completion: Checklist: X/Y complete; Incomplete: None or each UNPROVEN item's reason, outcome impact, and exact next action; residual risks and required decisions.

Skill-specific evidence: Documentation audiences and journeys; structure, coverage, accuracy, durability/SSOT, and comment trust. Record KEEP / ADD / UPDATE / DELETE / MERGE per in-scope surface. Findings need priority, document location, verifying evidence, affected audience, material impact, unacceptable tradeoff, and minimal correction or canonical owner; allow equivalent structures. Distinguish unverified claims from proven defects.

Frequently asked questions

What does the Ln 53 Documentation Auditor AI skill do?

Audits documentation and comments for trust, coverage, consistency and freshness; read-only.

Why use Ln 53 Documentation Auditor on TypingMind?

Because you install it once and use it with any model. Ln 53 Documentation Auditor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ln 53 Documentation Auditor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/levnikolaevich/claude-code-skills/tree/master/plugins/quality-assurance-suite/skills/ln-53-documentation-auditor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 53 Documentation Auditor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ln 53 Documentation Auditor?

As many as you like. As long as a model supports skills, you can use Ln 53 Documentation Auditor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 53 Documentation Auditor AI skill free?

Yes. It is published on GitHub by levnikolaevich under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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