
Code Wiki
FreeGenerate comprehensive wiki documentation for any codebase.
Free · Opens the source repo
What Code Wiki does
The Code Wiki skill is designed to automate the generation of structured documentation for software projects. It creates an extensive wiki that includes an overview, architecture details, in-depth documentation for each module, and visual diagrams using Mermaid syntax. This skill is particularly useful for developers and teams looking to document their codebases efficiently, whether they are working with local, private, or open-source repositories. By leveraging existing Hermes tools, it operates without the need for Docker or external services, ensuring a lightweight and straightforward setup.
To use the Code Wiki skill, simply point it at a codebase, and it will systematically scan the project's structure, identify key modules, and generate markdown files that can be easily rendered on platforms like GitHub. The output includes a README file that summarizes the project, an architecture document that outlines the system's components and data flow, and individual module documentation that explains the purpose and functionality of each part of the codebase. Additionally, it creates class and sequence diagrams to visually represent relationships and workflows within the code.
This skill is ideal for onboarding new team members to a project, providing a clear and organized reference for unfamiliar codebases, or generating documentation for existing projects that lack comprehensive records. It serves as a stable artifact that can be stored in the repository, making it easy for anyone to access and understand the project's structure and functionality.
However, it is important to note that the Code Wiki skill is not intended for single-file documentation or for generating API references for specific endpoints. It focuses on broader documentation needs and should not be used for projects that are actively being developed in the same session, as this could lead to inconsistencies in the generated documentation.
When to use it
Use this skill when you need to generate comprehensive documentation for a codebase, especially during onboarding or when working with unfamiliar repositories.
When not to use it
Avoid using this skill for single-file documentation or when needing immediate answers for specific API endpoints. It is also not suitable for actively developed codebases during the same session.
What you can build with it
Onboarding New Developers
Use the Code Wiki skill to quickly generate documentation for a codebase, helping new team members understand the project's structure and functionality.
Documenting Legacy Code
When working with legacy code that lacks documentation, this skill can create a comprehensive wiki to provide clarity and context.
Visualizing Code Architecture
Generate architecture diagrams and module documentation to visualize system components and their interactions, aiding in team discussions and planning.
How to install Code Wiki
View source1. Install with the skills CLI
npx skills add nousresearch/hermes-agent/code-wiki --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by nousresearchCode 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 Hermes 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_fileand 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.
giton PATH for repo SHA tracking and remote clones.- Optional:
pygountfor language-breakdown stats (see thecodebase-inspectionskill).
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 ~/.hermes/wikis/<repo-name>/. Only write into the repo (docs/wiki/) when the user explicitly requests it.
Quick Reference
| Step | Action |
|---|---|
| 1 | Resolve target — local cwd, given path, or git clone --depth 50 <url> to a temp dir |
| 2 | Scan structure — ls, find -maxdepth 3, manifest files, README |
| 3 | Pick 8–10 modules to document |
| 4 | Write README.md (overview + module map) |
| 5 | Write architecture.md with Mermaid flowchart |
| 6 | Write per-module docs in modules/ |
| 7 | Write diagrams/class-diagram.md (Mermaid classDiagram) |
| 8 | Write diagrams/sequences.md (Mermaid sequenceDiagram, 2–4 workflows) |
| 9 | Write getting-started.md |
| 10 | Write api.md if applicable, else skip |
| 11 | Write .codewiki-state.json |
| 12 | Report paths to user |
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="$HOME/.hermes/wikis/$REPO_NAME"
mkdir -p "$OUTPUT_DIR/modules" "$OUTPUT_DIR/diagrams"
2. Scan repo structure
Use the terminal tool for the shell work, read_file 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_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:
- Imported-from count (a module imported by many is core)
- LOC (bigger modules usually warrant their own doc)
- 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:
# <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_file those files (use offset / limit to read only what you need; prefer search_files 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_file 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:HermesCLI.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__.pyexports, 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": "$(date -u +%Y-%m-%dT%H:%M:%SZ)",
"generator": "hermes-agent code-wiki skill v0.1.0",
"modules_documented": []
}
EOF
12. Report to user
State exactly what was generated and where:
Generated wiki at ~/.hermes/wikis/<repo-name>/:
README.md project overview, module map
architecture.md system architecture + flowchart
getting-started.md setup, first run, workflows
modules/<N files> per-module deep-dives
diagrams/architecture.md Mermaid flowchart
diagrams/class-diagram.md Mermaid class diagram
diagrams/sequences.md Mermaid sequence diagrams
If you cloned to a temp dir, remind the user it can be removed (rm -rf "$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
search_filesfor symbols +read_filewithoffset/limitover 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_filebefore 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
processfunction processes things by callingprocess_itemon 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
~/.hermes/wikis/. Only write into the repo when the user explicitly requests it. - Mermaid special chars need quotes:
A["Tool / Agent"]notA[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
```mermaiddoesn'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:
- 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, $total total fences (expect total = opens*2)" done - All expected files exist —
ls "$OUTPUT_DIR"/{README.md,architecture.md,getting-started.md,.codewiki-state.json} \ "$OUTPUT_DIR"/modules/ "$OUTPUT_DIR"/diagrams/ - Module count matches what you intended —
ls "$OUTPUT_DIR/modules" | wc -lshould equal the number of modules you committed to in Step 3. - No fabricated paths — sanity-check 2–3 source links resolve to real files.
Frequently asked questions about Code Wiki
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