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Doc Consolidate

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jmagly
doc-consolidate

Crawl repository for scattered docs and consolidate into categorized reference index in .aiwg/docs/

Overview

Publisherjmagly
Repositoryaiwg
Skill namedoc-consolidate
Stars
225
Forks
34
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 jmagly on GitHub. Read the source before you install it.

Installation

Install the Doc Consolidate 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.
    https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/doc-consolidate
  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/jmagly/aiwg.git /tmp/aiwg
mkdir -p .claude/skills
cp -r /tmp/aiwg/agentic/code/addons/aiwg-utils/skills/doc-consolidate .claude/skills/doc-consolidate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doc Consolidate 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 Consolidate 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 Consolidate 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 Consolidate

You are the Doc Consolidate Orchestrator — crawling a repository for documentation scattered across directories and building a consolidated reference index in .aiwg/docs/ for planning, ops, semantic memory, and release-associated documentation.

Core Philosophy

Repos accumulate docs everywhere: README files in every package, deployment guides in ops/, release notes at root, API docs in docs/api/, troubleshooting buried in wiki-style folders. This skill answers: "what docs exist, where are they, and what are they for?"

No file duplication. Copying docs creates parallel drift. Instead, build a reference manifest and lightweight stubs that point to originals.

Natural Language Triggers

Users may say:

  • "consolidate docs"
  • "doc consolidate"
  • "find all docs"
  • "catalog docs"
  • "inventory docs"
  • "doc inventory"
  • "where are all the docs"
  • "what docs do we have"
  • "gather documentation"
  • "index all documentation"
  • "consolidate documentation for planning"

Parameters

--dry-run (optional)

Preview all discovered docs and their classifications without writing any files. Produces the same report as a live run but with no mutations.

--scope <path> (optional)

Limit discovery to a subtree. Useful for large monorepos.

bash
/doc-consolidate --scope docs/
/doc-consolidate --scope packages/auth/

--incremental (optional)

Only process files changed since the last successful run. Reads timestamp from .aiwg/reports/doc-consolidate-last-run.json. Falls back to full scan if no prior run exists.

--prefix <dir> (optional)

Target a different project directory instead of the current working directory.

Categories

CategoryWhat belongs herePath heuristics
releaseChangelogs, release notes, version announcementsCHANGELOG*, docs/releases/*, *release-note*
userUser guides, tutorials, quickstarts, how-tosREADME*, docs/getting-started*, docs/quickstart*, docs/guide*
apiAPI references, SDK docs, integration guidesdocs/api/*, *-reference.md, *-api.md, *cli-reference*
deploymentDeploy guides, runbooks, infra docsdocs/deploy*, ops/*, *runbook*, *infrastructure*
planningRoadmaps, RFCs, proposals, ADRs*roadmap*, *rfc*, *proposal*, ADR-*, docs/planning/*
communicationsAnnouncements, blog posts, marketing*announcement*, *blog*, *press*, *marketing*
helpTroubleshooting, FAQ, support docs*troubleshoot*, FAQ*, *support*, *error-reference*
developmentContributing guides, dev setup, coding standardsCONTRIBUTING*, docs/development/*, docs/contributing*, *coding-standard*

Each doc gets a primary category and optional tags for secondary categorization.

Output Files

FilePurpose
.aiwg/docs/_manifest.yamlMaster index: path, category, title, summary, confidence, tags
.aiwg/docs/{category}/Stub files referencing originals via @-mentions
.aiwg/reports/doc-consolidate-{timestamp}.mdRun report with stats and low-confidence flags
.aiwg/reports/doc-consolidate-last-run.jsonIncremental state (timestamp, file list, checksums)

Execution Flow

Phase 1: Discovery

  1. Parse flags: --dry-run, --scope, --incremental, --prefix
  2. If --incremental: load .aiwg/reports/doc-consolidate-last-run.json
  3. Walk repository recursively using Glob, collecting doc-like files:

Include patterns:

**/*.md
**/*.txt (only in docs/, doc/, documentation/ directories)
**/*.rst
**/*.adoc
**/README*
**/CHANGELOG*
**/CONTRIBUTING*
**/LICENSE*

Exclude patterns:

node_modules/**
.git/**
vendor/**
dist/**
build/**
.aiwg/docs/**       (output directory — avoid self-reference)
**/*.min.*
**/package-lock.json
  1. For each file, extract:

    • path — relative to project root
    • title — first H1 heading, or filename if no heading
    • summary — first non-empty paragraph (max 200 chars)
    • size — file size in bytes
    • lastModified — from git log or file mtime
  2. If --incremental: filter to files with mtime newer than last run, or use git diff --name-only --since={lastRun} for precision

  3. Report discovery results:

Discovery complete: {N} doc-like files found
  Scope: {scope or "full repo"}
  New since last run: {M} (if incremental)

Phase 2: Classification

Classify each discovered file into a category using a two-pass strategy:

Pass 1: Path heuristics (fast, deterministic)

Apply pattern matching on the file path. Rules evaluated in order, first match wins:

yaml
release:
  - path matches: CHANGELOG*, */CHANGELOG*
  - path matches: docs/releases/*, */releases/*
  - path matches: *release-note*, *release_note*
  - filename matches: RELEASES*, HISTORY*

user:
  - path matches: README*, */README*
  - path matches: docs/getting-started*, docs/quickstart*
  - path matches: docs/guide*, docs/tutorial*
  - path matches: docs/usage*, docs/install*

api:
  - path matches: docs/api/*, */api-docs/*
  - path matches: *-reference.md, *-api.md
  - path matches: *cli-reference*, *sdk-*
  - path matches: docs/integrations/*

deployment:
  - path matches: docs/deploy*, */deploy/*
  - path matches: ops/*, */ops/*
  - path matches: *runbook*, *infrastructure*
  - path matches: *docker*, *kubernetes*, *k8s*
  - path matches: docs/install/non-interactive*

planning:
  - path matches: *roadmap*, docs/roadmap*
  - path matches: *rfc*, docs/rfc/*
  - path matches: *proposal*, docs/proposals/*
  - path matches: ADR-*, */ADR-*, *adr-*
  - path matches: docs/planning/*

communications:
  - path matches: *announcement*, */announcements/*
  - path matches: docs/releases/*-announcement*
  - path matches: *blog*, *press*, *marketing*
  - path matches: *newsletter*

help:
  - path matches: *troubleshoot*, */troubleshooting/*
  - path matches: FAQ*, */FAQ*
  - path matches: *support*, docs/support/*
  - path matches: *error-reference*, *known-issues*

development:
  - path matches: CONTRIBUTING*, */CONTRIBUTING*
  - path matches: docs/development/*, docs/contributing/*
  - path matches: *coding-standard*, *style-guide*
  - path matches: docs/architecture/*, *dev-guide*

Confidence: high for path match.

Pass 2: Content analysis (for unmatched files)

For files that didn't match any path heuristic, read the first 500 characters and classify by keyword presence:

KeywordsCategory
version, release, changelog, breaking change, migrationrelease
getting started, tutorial, how to, step by step, quickstartuser
endpoint, API, request, response, parameter, authenticationapi
deploy, server, infrastructure, docker, kubernetes, productiondeployment
roadmap, milestone, planned, RFC, proposal, decisionplanning
announcement, launch, update, communitycommunications
troubleshoot, FAQ, error, fix, solution, workaroundhelp
contributing, development, build, test, lint, setupdevelopment

Confidence: medium for single-keyword match, low for no clear match (defaults to user).

Output: Each file gets { path, category, confidence, title, summary, tags }

Report classification results:

Classification complete:
  release: {N}    user: {N}    api: {N}
  deployment: {N} planning: {N} communications: {N}
  help: {N}       development: {N}
  Low confidence: {N} (review recommended)

Phase 3: Manifest Generation

If --dry-run: skip writing, proceed to report only.

Write .aiwg/docs/_manifest.yaml:

yaml
version: 1
generated: "2026-04-06T04:00:00Z"
project: /path/to/repo
total: 47
categories:
  release:
    - path: CHANGELOG.md
      title: Changelog
      summary: "All notable changes to this project..."
      confidence: high
      tags: [release, history]
      lastModified: "2026-04-05"
      size: 24500
    - path: docs/releases/v2026.4.0-announcement.md
      title: "v2026.4.0 Release"
      summary: "AIWG v2026.4.0 brings the web dashboard..."
      confidence: high
      tags: [release, communications]
      lastModified: "2026-04-04"
      size: 8200
  user:
    - path: README.md
      title: AIWG
      summary: "Framework for improving AI-generated content..."
      confidence: high
      tags: [user, overview]
      lastModified: "2026-04-05"
      size: 12000
  # ... remaining categories
stats:
  total: 47
  by_category:
    release: 5
    user: 12
    api: 8
    deployment: 3
    planning: 4
    communications: 5
    help: 3
    development: 7
  by_confidence:
    high: 38
    medium: 6
    low: 3

Phase 4: Stub Generation + Report

If --dry-run: skip stub creation, write report only.

Stub generation:

For each category with entries, ensure .aiwg/docs/{category}/ exists. For each doc in the category, write a stub file:

markdown
<!-- Auto-generated by doc-consolidate. Do not edit — edit the source file instead. -->
# {title}

> {summary}

**Source**: @{path}
**Category**: {category}
**Confidence**: {confidence}
**Last modified**: {lastModified}
**Tags**: {tags joined by comma}

Stub filename: sanitized version of the source path (e.g., docs/cli-reference.md becomes docs--cli-reference.md).

Run report: Write .aiwg/reports/doc-consolidate-{timestamp}.md:

markdown
# Doc Consolidate Report — {timestamp}

## Summary
- **Total docs discovered**: {N}
- **Categories**: {breakdown}
- **Low confidence**: {N} (listed below for review)
- **Mode**: {dry-run | live}
- **Scope**: {scope or full repo}

## By Category

### release ({N})
| File | Title | Confidence |
|------|-------|------------|
| CHANGELOG.md | Changelog | high |
| ... | ... | ... |

### user ({N})
...

## Low Confidence (Review Recommended)
| File | Assigned Category | Why |
|------|-------------------|-----|
| docs/misc/notes.md | user | No heuristic match, keyword: "how to" |

## Coverage Gaps
- No docs found in: {categories with 0 entries}

Incremental state: Write .aiwg/reports/doc-consolidate-last-run.json:

json
{
  "timestamp": "2026-04-06T04:00:00Z",
  "totalFiles": 47,
  "byCategory": { "release": 5, "user": 12, ... },
  "files": {
    "CHANGELOG.md": { "checksum": "sha256:abc...", "category": "release" },
    "README.md": { "checksum": "sha256:def...", "category": "user" }
  }
}

Integration Points

SystemHow
doc-syncAfter consolidation, doc-sync can read _manifest.yaml to locate all docs for drift detection instead of re-scanning
build-artifact-indexStubs in .aiwg/docs/ appear in aiwg index query results
ralph-memoryWrite a memory entry with consolidation stats so future agent loops know doc coverage without re-scanning
fortemiManifest entries can be ingested as structured notes for semantic search across session history
intake-from-codebaseCan consume prior consolidation results from _manifest.yaml instead of re-analyzing docs

Examples

Full consolidation

bash
/doc-consolidate

Preview without writing

bash
/doc-consolidate --dry-run

Scope to docs directory

bash
/doc-consolidate --scope docs/

Incremental update

bash
/doc-consolidate --incremental

Target a different project

bash
/doc-consolidate --prefix /path/to/other-project

Guided consolidation

User: "consolidate all the docs, focus on release and deployment docs"
→ Orchestrator runs full consolidation, highlights release and deployment categories in report

Safety and Guardrails

  1. Never modify source docs — only write to .aiwg/docs/ and .aiwg/reports/
  2. Stubs are disposable — safe to delete .aiwg/docs/ and re-run
  3. Low-confidence files flagged — never silently misclassify; report for human review
  4. Dry-run first — recommend --dry-run before first live run on a new repo
  5. Incremental is safe — merges with existing manifest, doesn't drop previous entries
  6. No git operations — does not commit, push, or modify git state

Completion Criteria

yaml
completion:
  required:
    - manifest_written: true          # .aiwg/docs/_manifest.yaml exists and is valid YAML
    - stubs_generated: true           # .aiwg/docs/{category}/ directories populated
    - report_written: true            # .aiwg/reports/doc-consolidate-{ts}.md
    - incremental_state_saved: true   # .aiwg/reports/doc-consolidate-last-run.json
  optional:
    - low_confidence_zero: false      # Not required — low confidence files are flagged, not blocked
    - all_categories_populated: false # Some repos won't have all 8 categories

Frequently asked questions

What does the Doc Consolidate AI skill do?

Crawl repository for scattered docs and consolidate into categorized reference index in .aiwg/docs/

Why use Doc Consolidate on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/doc-consolidate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Doc Consolidate?

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 Consolidate?

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

Is the Doc Consolidate AI skill free?

Yes. It is published on GitHub by jmagly 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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