code-wiki – skill Lập trình cho agent AI
Tạo tài liệu wiki + sơ đồ Mermaid cho bất kỳ codebase nào.
Cài skill code-wiki bằng một lệnh
Chọn công cụ bạn đang dùng, copy lệnh và dán vào terminal. Skill sẽ nằm trong thư mục skills của công cụ đó và sẵn sàng dùng ở phiên làm việc tiếp theo.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/code-wiki-61395.tgz | tar xz -C ~/.claude/skills
Gọi trong Claude Code: /code-wiki – rồi mô tả công việc bạn muốn làm.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/code-wiki-61395.tgz | tar xz -C ~/.codex/skills
Gọi trong Codex: $code-wiki – rồi mô tả công việc bạn muốn làm.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/code-wiki-61395.tgz | tar xz -C ~/.hermes/skills
Gọi trong Hermes Agent: /code-wiki – rồi mô tả công việc bạn muốn làm.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/code-wiki-61395.tgz | tar xz -C ~/.deepcode/skills
Gọi trong DeepSeek: /code-wiki – rồi mô tả công việc bạn muốn làm.
Nội dung SKILL.md
Nội dung SKILL.md gốc bằng tiếng Anh.
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 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 || trueThen 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:
# <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 Diagramflowchart 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 TypesclassDiagram
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.>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<exact commands>
## First Run<minimum command to see the system do something useful>
## Common Workflows
### <Workflow 1>
<comman… (nội dung đầy đủ có trong gói tải về)
Skill liên quan
- esm – Sử dụng Biohub esm Python SDK để tạo protein ESM3, nhúng ESMC và gấp protein toàn nguyên…
- antigravity-maintainer-batch-release – Chạy các đợt quét bảo trì AAS được bảo vệ, gộp PR theo lô, review có thể tiếp tục, phân…
- anywrite – CLI đã biên dịch bao phủ toàn bộ 52 endpoint của Anytype local API — objects…
- api-analyzer – Kiểm tra xem một yêu cầu API có đúng hay không dựa trên đầu vào được cung cấp (method…
- caio-review – /c-level-agents:caio-review [plan] — Chief AI Officer kiểm tra nghiêm ngặt về đánh giá…
- codex – Giao việc lập trình cho OpenAI Codex CLI (tính năng, PR).
- api-contract-testing – Thiết kế và rà soát kiểm thử hợp đồng API giữa nhà cung cấp và người tiêu dùng. Dùng cho…
- api-design-principles – Nắm vững các nguyên tắc thiết kế API REST và GraphQL để xây dựng API trực quan, có khả…