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Doc Manager

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luongnv89
doc-manager

Generate or update docs to match the code, citing each claim to path:line and asking on ambiguity; runbook docs also get a check-only validation script. Don't use for API-reference autogen (JSDoc/Sphinx), landing pages, or CLAUDE.md/AGENTS.md.

Overview

Publisherluongnv89
Repositoryskills
Skill namedoc-manager
Stars
124
Forks
18
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by luongnv89 on GitHub. Read the source before you install it.

Installation

Install the Doc Manager 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/luongnv89/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/doc-manager .claude/skills/doc-manager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doc Manager 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 Doc Manager 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 Doc Manager 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.

Doc Manager

Keep a project's Markdown documentation true to the code. Every run ends with each doc updated, verified-current, or flagged — and every non-obvious claim traced to a source. Scope is Markdown only (README.md, docs/*.md, per-component READMEs); docstrings and comments are read as source-of-truth but not rewritten.

Prime directive — never invent. If the code does not show it and the user has not stated it, do not write it. When a fact is unclear or docs conflict with code, ask the user, then record the resolution in docs/DECISIONS.md. A guess is a defect here, not a convenience.

Repo Sync Before Edits (mandatory)

Before creating/updating/deleting any file in the repo, sync the current branch:

bash
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin && git pull --rebase origin "$branch"

If the tree is dirty: git stash push -u -m "pre-sync" → sync → git stash pop. If origin is missing, or rebase/stash conflicts occur, stop and ask before continuing.

Prerequisites

Validate before starting; if any fails, stop and surface it:

  • Git: git --version ≥ 2.30. Working tree clean or stashable.
  • Remote: git remote get-url origin resolves (needed for sync). If absent, ask before any pull/rebase.
  • Write access: analysis needs read only. Writing files needs the tree to be writable; if read-only, emit a diff/summary instead of writing.
  • Mermaid (optional): only if diagrams must be exported — command -v mmdc.

Branch selector

Work one doc at a time. For each doc in the inventory, pick the path:

SituationPath
A needed doc does not existA. Generate
A doc exists but may not match the codeB. Reconcile
A doc or a section of one covers deploy / release / setup / operational processalso run C. Runbook add-on

A single run usually mixes A and B across the inventory. C is additive — it layers onto whichever of A/B produced runbook content. Classify at the section level: a doc that mixes a runbook section with reference content gets C applied to the runbook section only, not the whole file.

Workflow

0. Feature branch

  1. If already on a task feature branch, skip.
  2. Detect convention: git branch -r | head -20 (feat/, feature/, …).
  3. Create feat/doc-manager (or the repo's convention).

1. Inventory + scope

Read the codebase to establish ground truth, then build the doc inventory.

  • Project facts: type (library/API/web/CLI/service), entry points, config, scripts, env vars, endpoints — each with its source location. This is your citation pool.
  • Existing docs: every README.md, docs/*.md, per-component README.
  • Inventory table — for each doc: path | purpose | status(unknown) | is-runbook?. Also list needed-but-missing docs implied by the code (e.g. code has a deploy/ dir but no docs/deployment.md).

Present the inventory to the user and confirm scope before editing. Do not silently expand beyond confirmed scope.

2. Per-doc pass

For each doc, run its path. Cite as you write — do not defer citation to a review pass.

A. Generate — create from code analysis. Include only what the code (or a user answer) supports. Structure by relevance to project type; skip categories that don't apply.

B. Reconcile — diff doc against code:

  • Claim matches code → keep; add a citation if missing, and re-verify any existing path:line still resolves to the claimed fact (line numbers rot when code above them shifts — repoint or FLAG stale cites).
  • Claim contradicts code → fix to match code, cite the code.
  • Claim unverifiable from code and not user-stated → flag inline (<!-- FLAG: unverified — {what} -->) and ask the user; never silently keep or delete.
  • Doc fully current (every cite re-verified) → mark verified-current, touch nothing.

Citation rule (checkable). Every non-obvious factual claim carries an inline source. Forms, in order of preference:

  • Single line — src/server.ts:12 (a specific value: port, default, flag).
  • Range / multi-source — src/retry.ts:8-24 or config.ts:4, env.ts:11 for an emergent fact that spans lines/files (e.g. "retries 3× with backoff", "config merges env > file > defaults"). This is a real citation, not a FLAG — FLAG is only for facts the code cannot confirm.
  • Whole file — src/router.ts when the fact is the file's overall behavior and no line is more authoritative.

The server listens on port 8080 (src/server.ts:12).

Cite unless trivially obvious. Default to citing; the burden is on treating a claim as obvious, not on citing it. Section intros and definitions of common terms are obvious. Anything a reader could get wrong — ports, commands, paths, env vars, versions, endpoints, defaults, behavior — is not. This coverage check is a judgment pass (grep can't verify it), so err toward over-citing: an uncited factual claim is the exception you must be able to justify.

Decisions log. Every ambiguity you ask about gets appended to docs/DECISIONS.md:

markdown
## YYYY-MM-DD
- Q: {the ambiguity}
- A ({who}): {resolution}
- Source: `path:line` (if code-derived)

Use the run date; never fabricate one. Get it from the environment context, not a guess.

3. Runbook add-on (path C)

For any deploy/release/setup/operational doc, produce a check-only validation script and keep a troubleshooting log. Read references/runbook-validation.md for the script contract, template, and the fix→document loop. In short:

  • The validation script (scripts/validate-<name>.sh) is check/dry-run by default. It verifies preconditions and asserts expected state idempotently. Every destructive or outward-facing step is gated behind --run-destructive or a MANUAL: marker — never auto-run.
  • Script lives in the documented repo's scripts/, and the runbook section links to it. On a read-only tree, emit the script inline and run it once so you can report its --check outcome.
  • Run it (--check). Classify each failure: if the doc/check is wrong, fix it; if it is an operator env/tool/network prerequisite the agent cannot satisfy here, document it as a runbook prereq or MANUAL: step (do not weaken or drop the check just to force green). Append only real fix findings to docs/troubleshooting.md, cited.

4. Validate the run

  1. Citations: no non-obvious claim is unsourced or unresolved-FLAG. Grep for stray FLAG: markers — none may remain unaddressed.
  2. Links: every internal [text](path) resolves.
  3. Orphans: every docs/*.md is reachable from README.md or another doc within one hop.
  4. Inventory closed: every doc is updated, verified-current, or flagged (with the flag surfaced to the user). None left unknown.
  5. Runbook: each runbook section links a well-formed check-only validate-<name>.sh (run --check to confirm — on a writable tree it's committed; on a read-only tree it's emitted inline and run once). Agent-satisfiable local/static checks must pass; env/tool/network gaps that only an operator can close are documented as prereqs/MANUAL: rather than forced to exit 0. docs/troubleshooting.md reflects any real fix applied.
  6. Diagrams (if any): Mermaid renders without error (mmdc if available).

Present a change summary. Do not commit unless the user explicitly asks.

Expected output

  • Root README.md and docs/*.md reconciled to the code, each non-obvious claim cited to path:line.
  • docs/DECISIONS.md — append-only log of every ambiguity resolved with the user.
  • For runbook sections: scripts/validate-<name>.sh (check-only) linked from the section, plus docs/troubleshooting.md updated with fixes found during validation.
  • A change summary listing per-doc status and any open FLAGs.

Acceptance criteria

A run passes when all hold:

  • No invented facts. Every non-obvious claim in every touched doc is either cited to path:line or marked FLAG and raised with the user. Zero unresolved FLAG markers at close.
  • Inventory closed. Every doc in scope ends updated, verified-current, or flagged; none left unknown or known-stale-and-untouched.
  • Decisions logged. Every user-resolved ambiguity is appended to docs/DECISIONS.md with the resolution and (where applicable) source.
  • Runbook validated. Each deploy/process/setup section has a check-only validate-<name>.sh that is linked, well-formed, and gates every destructive step (run --check to confirm). Acceptance is not "exit 0 at all costs": agent-satisfiable local/static checks must pass; genuine operator prerequisites (missing env vars, tools, remote health outside this environment) may leave --check non-zero when documented as prereqs or MANUAL: steps — never invent a green path by dropping real checks. On a read-only tree the script is emitted inline and its --check outcome reported instead of committed. docs/troubleshooting.md records any real fix applied.
  • Links + orphans. Every internal link resolves; no docs/*.md is orphaned.
  • Branch discipline. No commits on main/master; all changes on a feature branch. No commit without an explicit user request.

Edge cases

  • No docs exist: all-Generate run. Start from README.md, add docs/ files the code justifies. Still cite everything.
  • Docs conflict with code: code wins. Fix the doc to match, cite the code, and log the conflict in DECISIONS.md. Do not delete the user's prose without asking.
  • Ambiguity with no code answer: ask the user; never guess. If unreachable, leave the claim as a FLAG and report it — do not fill the gap.
  • Monorepo: limit per-component READMEs to packages with public APIs or user-facing behavior; skip build output and generated packages.
  • Secrets: never document credentials, tokens, or internal-only endpoints beyond what code comments already expose.
  • Read-only repo: emit docs and the validation script as a diff/inline summary instead of writing files; still run the script once to report its --check outcome (including documented prereq failures).

Step Completion Reports

After each major step, emit:

◆ [Step Name] ([step N of M] — [context])
··································································
  [Check 1]:          √ pass
  [Check 2]:          × fail — [reason]
  [Criteria]:         √ N/M met
  ____________________________
  Result:             PASS | FAIL | PARTIAL

Use pass, × fail, for context. Per-phase checks:

  • Inventory + scopeGround-truth read, Inventory built, Scope confirmed
  • Per-doc passClaims cited, Conflicts flagged, Decisions logged
  • Runbook add-onValidate script check-only, Destructive steps gated, Troubleshooting updated
  • Validate the runNo unresolved FLAGs, Links resolve, Inventory closed, Runbook script well-formed

Guidelines

  • Protect the context budget. State the fact, cite it, move on. No filler, no restating the obvious, no marketing tone.
  • Adapt structure to project type — not every docs/ category applies.
  • Prefer code-derived facts over stale prose; keep existing accurate docs untouched (verified-current).
  • Maintain cross-references; remove content only when it's wrong or orphaned, and say so in the summary.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Doc Manager AI skill do?

Generate or update docs to match the code, citing each claim to path:line and asking on ambiguity; runbook docs also get a check-only validation script. Don't use for API-reference autogen (JSDoc/Sphinx), landing pages, or CLAUDE.md/AGENTS.md.

Why use Doc Manager on TypingMind?

Because you install it once and use it with any model. Doc Manager 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 Doc Manager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luongnv89/skills/tree/main/skills/doc-manager. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Doc Manager?

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 Doc Manager?

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

Is the Doc Manager AI skill free?

Yes. It is published on GitHub by luongnv89 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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