通过1Password CLI,将保存的密码和API密钥用于命令及配置模板,完成安装、认证、秘密值读取,并通过环境变量向命令传递凭据。
- 安装并配置1Password CLI
- 使用已保存的API密钥运行命令
- 向配置模板填入秘密值
阅读代码并整理为Markdown维基,说明整体结构、主要模块和入门步骤,生成Mermaid架构图与流程图,并附源码链接,供读者追溯查阅。
Generate wiki docs + Mermaid diagrams for any codebase.
检查代码,将项目概述、整体架构、主要模块说明和入门步骤整理成Markdown维基。使用以文本描述图表的Mermaid格式,生成架构图、主要类型关系图,以及展示调用顺序的时序图。说明附有源码链接;对于库或API服务器,还会编写公开API的参考资料。
适合了解初次接触的代码库,或为新加入的成员留下参考资料。本地仓库和私有仓库都可作为文档编写对象。首次整理以八至十个主要模块为重点。
使用Hermes现有工具,获取远程仓库和记录提交信息需要git。无需Docker、外部服务或API密钥。默认保存位置为~/.hermes/wikis/,仅在明确提出要求时才将文件写入仓库。说明开发背景或战略意图不属于此技能的用途。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月20日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
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).
Do NOT use this for:
read_file and answer inlinegit on PATH for repo SHA tracking and remote clones.pygount for language-breakdown stats (see the codebase-inspection skill).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.
| 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 |
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"
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.
Cap initial pass at 8–10 modules. Heuristics by language:
__init__.py), plus subsystem dirssrc/<subdir>, top-level workspace dirssrc/<module> dirsFor very large repos, prioritize by:
State the module list to the user before generating per-module docs on big repos — gives them a chance to redirect.
README.mdread_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.
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/eventCap at ~20 nodes per diagram. Split into sub-diagrams if larger.
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>
diagrams/class-diagram.mdPick 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.
diagrams/sequences.mdPick 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.
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)
api.md (skip if not applicable)Only write this if the project is a library or API server. If it is:
__init__.py exports, OpenAPI specs, route handlers, exported types)cat > "$OUTPUT_DIR/.codewiki-state.json" <<EOF
{
"repo_name": "$REPO_NAME",
"source_path": "$PWD",
"source_sha": "$REPO_SHA",
"generated_at": "$(date -u +%Y-%m-%dT%H:%M:%SZ)",
"generator": "hermes-agent code-wiki skill v0.1.0",
"modules_documented": []
}
EOF
State exactly what was generated and where:
Generated wiki at ~/.hermes/wikis/<repo-name>/:
README.md project overview, module map
architecture.md system architecture + flowchart
getting-started.md setup, first run, workflows
modules/<N files> per-module deep-dives
diagrams/architecture.md Mermaid flowchart
diagrams/class-diagram.md Mermaid class diagram
diagrams/sequences.md Mermaid sequence diagrams
If you cloned to a temp dir, remind the user it can be removed (rm -rf "$WIKI_TMP") after they've reviewed the wiki.
Generating a full wiki for a 500K-LOC monorepo is wildly token-expensive. Default to bounded scope:
search_files for symbols + read_file with offset/limit over full readsvendor/, 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?"
If .codewiki-state.json already exists at the target path:
git diff --name-only <old-sha> HEAD)Full incremental-regeneration is a future enhancement — for now, regenerating the whole thing is acceptable.
read_file before writing. The single biggest failure mode for auto-generated docs is plausible-sounding fabrication.process function processes things by calling process_item on each item" is worse than just linking to the function.~/.hermes/wikis/. Only write into the repo when the user explicitly requests it.A["Tool / Agent"] not A[Tool / Agent]. <br> for line breaks inside a node.```mermaid doesn't close the outer. (This SKILL.md does it.)~T~ (e.g. List~Tool~), not <T>.%%{init: ...}%% blocks; they're stripped on render.After writing, verify:
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, $total total fences (expect total = opens*2)"
done
ls "$OUTPUT_DIR"/{README.md,architecture.md,getting-started.md,.codewiki-state.json} \
"$OUTPUT_DIR"/modules/ "$OUTPUT_DIR"/diagrams/
ls "$OUTPUT_DIR/modules" | wc -l should equal the number of modules you committed to in Step 3.还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
通过1Password CLI,将保存的密码和API密钥用于命令及配置模板,完成安装、认证、秘密值读取,并通过环境变量向命令传递凭据。
创建用公式联动利润表、资产负债表和现金流量表的 Excel 模型,分阶段填入历史实际数据与预测假设,比较不同情景,并检查三张报表之间的一致性。
以少量修改让现有PyTorch训练代码适用于多GPU或多台机器,指导混合精度、节省内存的DeepSpeed与FSDP配置,以及训练启动方法。
帮助配置Hermes,通过Actual Computer在自己的设备上运行AI模型。支持加密中继与本地连接,涵盖模型选择、响应测试,以及请求报错或空响应的排查。
扮演不擅长技术、容易不满的用户测试应用,发现难懂的操作和导致放弃使用的障碍。将实际可改进的问题与个人抱怨分开,整理截图、修复建议和任务单。
从中立立场解决两个智能体分别修改代码产生的Git冲突,对照双方差异和意图,记录每处冲突的取舍理由,并通过构建或测试验证合并结果。