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Generate wiki docs + Mermaid diagrams for any codebase.

Skill metadata

Reference: full SKILL.md

The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

Code Wiki Skill

Generate a full 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 Mibyan tools (terminal, read_file, search_files, write_file); 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_file 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 terminal tool from the target repo’s root, then use read_file / search_files / write_file to produce the wiki. Default output location is ~/.mibyan/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:
For a local path (or cwd if none given):
Then set the output dir:

2. Scan repo structure

Use the terminal tool for the shell work, read_file for manifests:
Then read_file 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_file the actual project README plus the top 2–3 entry-point files. Then write_file:
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

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_file those files (use offset / limit to read only what you need; prefer search_files for specific symbols).

7. Write diagrams/class-diagram.md

Pick the 5–10 most important classes/types. read_file them, then write:
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:
Don’t invent participants. Every box must correspond to a real component the reader can find in the code.

9. Write getting-started.md

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

12. Report to user

State exactly what was generated and where:
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 search_files for symbols + read_file 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_file 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 ~/.mibyan/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:
  2. All expected files exist —
  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.