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Codebase Wiki

OrganizationPopular
inkeep
codebase-wiki

How to work in a Codebase Wiki project (the `codebase-wiki` starter pack): an agent-authored, source-grounded wiki of the surrounding codebase. Read when the project has a `wiki/` knowledge base with `architecture/`, `modules/`, `flows/`, `concepts/`, and `guides/` sections plus `wiki/OVERVIEW.md`, or when asked to generate or refresh a wiki of this codebase. Carries the per-folder rules and freshness + log discipline, summarizes the audience/depth knobs and source-reference convention, and bundles the full generate/refresh procedure in `references/`. Complements the platform `open-knowledge` skill; does not replace it.

Overview

Publisherinkeep
Repositoryopen-knowledge
Skill namecodebase-wiki
Stars
4.2K
Forks
279
Bundled files
2
LicenseGPL-3.0
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Codebase Wiki 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/inkeep/open-knowledge.git /tmp/open-knowledge
mkdir -p .claude/skills
cp -r /tmp/open-knowledge/packages/server/assets/skills/packs/codebase-wiki .claude/skills/codebase-wiki
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codebase Wiki 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 Codebase Wiki 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 Codebase Wiki 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.

Codebase Wiki pack — how to work here

This project holds an agent-authored wiki of a codebase — DeepWiki, but living in the repo. A coding agent reads the source and writes a navigable, diagram-rich, source-grounded wiki as markdown under wiki/. It is version-controlled and diffable, private by default, human+agent co-editable, renders in OK's live preview, and doubles as durable grounding context for future agent sessions. There is no separate Q&A surface — Q&A is "the OK-grounded agent + search".

This skill is pack guidance. The platform /open-knowledge skill (read/write/preview/linking/grounding rules) still governs every markdown operation — this layers the wiki workflow on top.

The shape

wiki/
  OVERVIEW.md     hub: what it is, a big-picture architecture diagram, a nav map to every section.
                  Frontmatter carries `profile` (audience/depth) + `source_commit` (freshness anchor).
  log.md          append-only generation / refresh audit trail
  architecture/   system boundaries, layers, subsystems, cross-cutting concerns + diagrams
  modules/        one page per package / module: purpose, entry points, key files, deps
  flows/          key end-to-end flows as sequence / flow diagrams + narrative
  concepts/       glossary: atomic pages for domain terms / core abstractions
  guides/         task-oriented "how / where do I change X" (filled at depth >= standard)

Generating + refreshing

Don't free-hand it — read references/generate-and-refresh.md and follow the phased, STOP-gated procedure. It auto-detects mode: a stubbed OVERVIEW.md (empty source_commit) → generate (survey → overview → architecture → modules → flows → concepts → link-graph audit); a stamped source_commitrefresh (diff source_commit..HEAD, update only affected pages, re-stamp).

Two toolsets. Read source code with NATIVE tools (Read/Grep/Glob/Bash) — OK MCP does not index non-markdown source. Author and audit the wiki with OK MCP verbs (write/edit for pages, links/search for the graph). Never hand-write wiki markdown with native Write/Edit.

The two knobs

Two natural-language knobs, read from the user's request (e.g. "build the wiki, public and exhaustive") and recorded in OVERVIEW.md frontmatter (profile: <audience>/<depth>) so refreshes stay consistent:

  • audienceinternal (default) or public. public means polished prose, no secrets / internal infra / ticket numbers, and GitHub-URL source references.
  • depthtour | standard (default) | exhaustive. Scales coverage from OVERVIEW + architecture + top flows up through per-package module pages, concepts, and task guides.

references/generate-and-refresh.md is the authoritative source for exactly how each knob shapes the output — read it before generating.

Source-reference convention

  • Intra-wiki navigation → OK doc links — they build the backlink / hub / orphan graph, so link liberally; density is how the wiki stays navigable.
  • Code references → relative links + symbol code-spans (internal) or GitHub blob URLs (public). Source-file links stay out of the navigation graph (links tracks only .md/.mdx edges, so they never show as graph dead-links or orphans) — but a wrong-depth path still surfaces in the write/edit brokenLinks response (no-such-file, or unresolvable if it overshoots the content root), so count the ../ hops from the page's folder. Never invent paths — reference only files you actually read.

The full rules — the GitHub-URL / relative fallback, the #Lxx caveat, and the exact code-span shape — live in references/generate-and-refresh.md.

Per-folder rules

architecture/ — One page per architectural area (boundaries, layers, subsystems, cross-cutting concerns). Each: a mermaid system-context or component diagram, key components (with source refs), and the design decisions behind them. Uses the architecture-page template. At depth: tour, modules fold in here.

modules/ — One page per package / module: purpose, responsibilities, public API / entry points, key files (linked per the convention), dependencies, and flows it participates in. Uses the module-page template. Skipped at tour; sub-module depth scales with the knob.

flows/ — Key end-to-end sequences as mermaid sequence / flow diagrams + narrative. Uses the flow-page template; add a Failure modes section at exhaustive. Link every module and concept the flow crosses.

concepts/ — Atomic glossary pages (one term each): definition, why it matters, where it lives in the code. Uses the concept-page template. Keep small and densely cross-linked so each concept becomes a hub.

guides/ — Task-oriented "how / where do I change X" walkthroughs: goal, steps, relevant code, gotchas. Uses the guide-page template. Populated at standard, rich at exhaustive, thin/empty at tour.

Freshness discipline (MUST)

OVERVIEW.md frontmatter carries source_commit — the git HEAD the wiki was last generated/refreshed against. It is the freshness anchor: refresh mode diffs source_commit..HEAD to update only the affected pages, then re-stamps it. Always re-stamp source_commit after a generate or refresh run — a stale anchor silently breaks incremental refresh.

Log discipline (MUST)

wiki/log.md is an append-only audit trail. Append one dated entry per generation or refresh run — one per run, not per page. Reference touched pages as markdown links ([Server](./modules/server.md)) so they register in the backlink graph. Entry shape:

markdown
## YYYY-MM-DD: <generate | refresh>

- Profile: <audience>/<depth>
- source_commit: <short-sha> (was <prev-sha> on refresh)
- Coverage: <sections / packages written or updated>
- Pages: [Overview](./OVERVIEW.md), [Server](./modules/server.md), ...

Templates

Each folder ships a starter template (architecture-page, module-page, flow-page, concept-page, guide-page). Create with write({ document: { path, template: "<name>" } }). Templates carry only structure (headings + frontmatter scaffold); what each section is for is described above and in references/generate-and-refresh.md, not repeated inside document bodies.

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 Codebase Wiki AI skill do?

How to work in a Codebase Wiki project (the `codebase-wiki` starter pack): an agent-authored, source-grounded wiki of the surrounding codebase. Read when the project has a `wiki/` knowledge base with `architecture/`, `modules/`, `flows/`, `concepts/`, and `guides/` sections plus `wiki/OVERVIEW.md`, or when asked to generate or refresh a wiki of this codebase. Carries the per-folder rules and freshness + log discipline, summarizes the audience/depth knobs and source-reference convention, and bundles the full generate/refresh procedure in `references/`. Complements the platform `open-knowledg...

Why use Codebase Wiki on TypingMind?

Because you install it once and use it with any model. Codebase Wiki 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 Codebase Wiki in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/inkeep/open-knowledge/tree/main/packages/server/assets/skills/packs/codebase-wiki. 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 Codebase Wiki?

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 Codebase Wiki?

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

Is the Codebase Wiki AI skill free?

Yes. It is published on GitHub by inkeep under the GPL-3.0 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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