Code Wiki

Code Wiki Skill

Generate a comprehensive wiki for any codebase — overview, architecture, per-module deep-dives, Mermaid class and sequence diagrams. Inspired by Google CodeWiki, but works on local repos, private repos, and any language. Uses only existing available tools (Bash, Read, Grep, Write); no Docker, no external services, no extra dependencies.

This skill produces reference documentation (what/how). It does not produce strategic narrative (why — that's a different skill).

When to Use

• User says "document this codebase", "generate a wiki", "make architecture diagrams"
• Onboarding to an unfamiliar repo and wants a structured reference
• User points at a GitHub URL and asks for documentation
• Need a stable artifact (markdown + Mermaid) that renders on GitHub

Do NOT use this for:

• Single-file or single-function documentation — just answer directly
• API reference for one specific endpoint — use Read and answer inline
• Strategic "why does this exist" narrative — different skill, different purpose
• Codebases the user is actively developing in this session — just answer questions as they come

Prerequisites

• No env vars required.
• git on PATH for repo SHA tracking and remote clones.
• Optional: pygount for language-breakdown stats (see the codebase-inspection skill).

How to Run

Invoke through the Bash tool from the target repo's root, then use Read / Grep / Write to produce the wiki. Default output location is ~/.agent/wikis/<repo-name>/. Only write into the repo (docs/wiki/) when the user explicitly requests it.

Quick Reference

Procedure

1. Resolve the target

For a GitHub URL:

WIKI_TMP=$(mktemp -d)
git clone --depth 50 <url> "$WIKI_TMP/repo"
cd "$WIKI_TMP/repo"
REPO_SHA=$(git rev-parse HEAD)
REPO_NAME=$(basename <url> .git)

For a local path (or cwd if none given):

cd <path>
REPO_SHA=$(git rev-parse HEAD 2>/dev/null || echo "uncommitted")
REPO_NAME=$(basename "$PWD")

Then set the output dir:

OUTPUT_DIR="$HOME/.mindforge/wikis/$REPO_NAME"
mkdir -p "$OUTPUT_DIR/modules" "$OUTPUT_DIR/diagrams"

2. Scan repo structure

Use the Bash tool for the shell work, Read for manifests:

# Shallow tree first
ls -la

# Deeper tree, noise filtered
find . -type d \
  -not -path '*/\.*' \
  -not -path '*/node_modules*' \
  -not -path '*/venv*' \
  -not -path '*/__pycache__*' \
  -not -path '*/dist*' \
  -not -path '*/build*' \
  -not -path '*/target*' \
  -maxdepth 3 | sort

# Language breakdown (skip if pygount unavailable)
pygount --format=summary \
  --folders-to-skip=".git,node_modules,venv,.venv,__pycache__,.cache,dist,build,target" \
  . 2>/dev/null || true

Then Read the relevant manifests (package.json, pyproject.toml, setup.py, Cargo.toml, go.mod, pom.xml, build.gradle) and the project README. Use search_files target='files' to find them rather than guessing names.

3. Pick modules to document

Cap initial pass at 8–10 modules. Heuristics by language:

• Python: top-level packages (dirs with __init__.py), plus subsystem dirs
• JS/TS: src/<subdir>, top-level workspace dirs
• Rust: each crate in a workspace, or top-level src/<module> dirs
• Go: each top-level package directory
• Mixed/unfamiliar: top-level directories that contain source code (not config, not tests)

For very large repos, prioritize by:

1. Imported-from count (a module imported by many is core)
2. LOC (bigger modules usually warrant their own doc)
3. Mentions in README / top-level docs

State the module list to the user before generating per-module docs on big repos — gives them a chance to redirect.

4. Write README.md

Read the actual project README plus the top 2–3 entry-point files. Then Write:

# <Project Name>

<One paragraph: what it is and what it's for. Self-contained — don't assume the
reader has the source README.>

## Key Concepts

- **<Concept 1>** — <one line>
- **<Concept 2>** — <one line>

## Entry Points

- [`path/to/main.py`](<link>) — <what runs when you start it>
- [`path/to/cli.py`](<link>) — <CLI surface>

## High-Level Architecture

<2-3 sentences. Detail goes in architecture.md.>

See [architecture.md](architecture.md).

## Module Map

| Module | Purpose |
|---|---|
| [`<module>`](modules/<module>.md) | <one-line purpose> |

## Getting Started

See [getting-started.md](getting-started.md).

For link targets in local mode use relative paths. For cloned repos use https://github.com/<owner>/<repo>/blob/<sha>/<path> so links survive future commits.

5. Write architecture.md

# Architecture

<2-3 paragraphs: shape of the system. What talks to what. Where data enters,
where it exits, where state lives.>

## Components

- **<Component>** — <1-2 sentences>. See [`modules/<module>.md`](modules/<module>.md).

## System Diagram

```mermaid
flowchart TD
    User([User]) --> Entry[Entry Point]
    Entry --> Core[Core Engine]
    Core --> StorageA[(Database)]
    Core --> ExternalAPI{{External API}}
```

## Data Flow

1. **<Step>** — [`<file>`](<link>)
2. **<Step>** — [`<file>`](<link>)

## Key Design Decisions

- <Anything load-bearing the reader should know>

Mermaid shape semantics:

• [] = component
• [()] = database / storage
• {{}} = external service
• (()) = entry point or terminal
• --> = sync call, -.-> = async/event

Cap at ~20 nodes per diagram. Split into sub-diagrams if larger.

6. Write per-module docs in modules/

For each selected module, inspect its layout with ls, identify 3–5 most important files (by size, by being named core.py / main.py / __init__.py, by being imported a lot), then Read those files (use offset / limit to read only what you need; prefer Grep for specific symbols).

# Module: `<module>`

<1-2 sentence purpose.>

## Responsibilities

- <bullet>
- <bullet>

## Key Files

- [`<module>/<file>`](<link>) — <what it does>

## Public API

<Functions/classes/constants other code uses. Group related items. Show
signatures, not full implementations.>

## Internal Structure

<How the module is organized internally. State management.>

## Dependencies

- **Used by:** <other modules>
- **Uses:** <other modules + external libs>

## Notable Patterns / Gotchas

- <Anything non-obvious>

7. Write diagrams/class-diagram.md

Pick the 5–10 most important classes/types. Read them, then write:

# Class Diagram

## Core Types

```mermaid
classDiagram
    class Agent {
        +string name
        +list~Tool~ tools
        +chat(message) string
    }
    class Tool {
        <<interface>>
        +name string
        +execute(args) any
    }
    Agent --> Tool : uses
    Tool <|-- TerminalTool
    Tool <|-- WebTool
```

## Notes

<Anything the diagram can't express — lifecycle, threading, etc.>

For languages without classes (Go, C, Rust): use the diagram for struct relationships, or skip class-diagram.md and explain it in prose in architecture.md. Don't force-fit.

8. Write diagrams/sequences.md

Pick 2–4 of the most important workflows. Trace each call path through the code (read entry point, follow function calls), then:

# Sequence Diagrams

## Workflow: <Name>

<1 sentence describing what this does and when it runs.>

```mermaid
sequenceDiagram
    participant User
    participant CLI
    participant Agent
    participant LLM
    User->>CLI: types message
    CLI->>Agent: chat(message)
    Agent->>LLM: API call
    LLM-->>Agent: response + tool_calls
    Agent->>Agent: execute tools
    Agent-->>CLI: final response
```

### Walkthrough

1. **User input** — [`cli.py:run_session`](<link>)
2. **Message dispatch** — [`run_agent.py:AIAgent.chat`](<link>)

Don't invent participants. Every box must correspond to a real component the reader can find in the code.

9. Write getting-started.md

# Getting Started

## Prerequisites

<From manifest files + README. Be specific — versions if pinned.>

## Installation

```bash
<exact commands>
```

## First Run

```bash
<minimum command to see the system do something useful>
```

## Common Workflows

### <Workflow 1>
<commands>

## Configuration

- `<config-file>` — <what it controls>
- Env var `<VAR>` — <what it controls>

## Where to Go Next

- Architecture: [architecture.md](architecture.md)
- Module reference: [README.md#module-map](README.md#module-map)

10. Write api.md (skip if not applicable)

Only write this if the project is a library or API server. If it is:

• Find the public API surface (__init__.py exports, OpenAPI specs, route handlers, exported types)
• Document each public entry with signature, parameters, return type, one-line description
• Group by category

11. Write the state file

cat > "$OUTPUT_DIR/.codewiki-state.json" <<EOF
{
  "repo_name": "$REPO_NAME",
  "source_path": "$PWD",
  "source_sha": "$REPO_SHA",
  "generated_at": "$(date -u +%Y-%m-%dT%H:%M:%SZ)",
  "generator": "
  "modules_documented": []
}
EOF

12. Report to user

State exactly what was generated and where:

Generated wiki at ~/.agent/wikis/<repo-name>/:
  README.md                   project overview, module map
  architecture.md             system architecture + flowchart
  getting-started.md          setup, first run, workflows
  modules/<N files>           per-module deep-dives
  diagrams/architecture.md    Mermaid flowchart
  diagrams/class-diagram.md   Mermaid class diagram
  diagrams/sequences.md       Mermaid sequence diagrams

If you cloned to a temp dir, remind the user it can be removed (rm -rf "$WIKI_TMP") after they've reviewed the wiki.

Scope Control

Generating a full wiki for a 500K-LOC monorepo is wildly token-expensive. Default to bounded scope:

• Initial scan: max depth 3 directories
• Per-module docs: cap at 10 modules unless user expands scope
• Per-file reads: prefer Grep for symbols + Read with offset/limit over full reads
• Skip vendored code (vendor/, third_party/, generated code, _pb2.py, .min.js)

If the user says "do the whole thing exhaustively", believe them — but ballpark the cost first: "this repo has ~340 source files, comprehensive coverage will be expensive — confirm?"

Re-Run / Update

If .codewiki-state.json already exists at the target path:

• Read it for previous SHA and module list
• If source SHA matches: ask user if they want to regenerate or skip
• If SHA differs: offer to regenerate only modules with changed files (git diff --name-only <old-sha> HEAD)

Full incremental-regeneration is a future enhancement — for now, regenerating the whole thing is acceptable.

Pitfalls

• Fabricating components. Every diagram node and claimed function call must be in the source. Read before writing. The single biggest failure mode for auto-generated docs is plausible-sounding fabrication.
• Generic AI prose. "This module is responsible for..." is content-free. Say what the module actually does in domain-specific terms.
• Restating code as prose. A module doc that says "the process function processes things by calling process_item on each item" is worse than just linking to the function.
• Mermaid > 50 nodes. They don't render legibly. Split them.
• Documenting tests, generated code, or vendored deps as if they were product code. Skip them.
• In-repo output without asking. Default is ~/.agent/wikis/. Only write into the repo when the user explicitly requests it.
• Mermaid special chars need quotes: A["Tool / Agent"] not A[Tool / Agent]. <br> for line breaks inside a node.
• Nested code fences in SKILL.md. When writing a markdown example that contains a Mermaid block, use 4-backtick outer fences so the 3-backtick inner ```mermaid doesn't close the outer. (This SKILL.md does it.)
• classDiagram generics render as ~T~ (e.g. List~Tool~), not <T>.
• GitHub Mermaid theme is fixed — don't include %%{init: ...}%% blocks; they're stripped on render.

Verification

After writing, verify:

1. Mermaid blocks balance — opens equal closes per file:

   for f in "$OUTPUT_DIR"/diagrams/*.md "$OUTPUT_DIR"/architecture.md; do
     opens=$(grep -c '^```mermaid' "$f")
     total=$(grep -c '^```' "$f")
     echo "$f: $opens mermaid blocks, $total total fences (expect total = opens*2)"
   done
   

2. All expected files exist —

   ls "$OUTPUT_DIR"/{README.md,architecture.md,getting-started.md,.codewiki-state.json} \
      "$OUTPUT_DIR"/modules/ "$OUTPUT_DIR"/diagrams/
   

3. Module count matches what you intended — ls "$OUTPUT_DIR/modules" | wc -l should equal the number of modules you committed to in Step 3.
4. No fabricated paths — sanity-check 2–3 source links resolve to real files.

Mandatory actions when this skill is active

Before applying this skill:

• Read the task requirements fully before acting
• Confirm you understand the goal and constraints
• Check for existing work or prior context in the codebase

While working:

• Follow the methodology described above step by step
• Document any decisions or findings as you go

After completing:

• Self-check: does the output satisfy the original requirement?
• Verify no regressions or unintended side effects