Pandaone Guard
by hellob1889
README.md
<p align="center">
<img src="assets/banner-readme.svg" alt="Pandaone AI Agent — AI Agent 代码审计门禁" width="800"/>
</p>
# Pandaone
<p align="center">
<img src="assets/icon-github.svg" alt="Pandaone Logo" width="96" height="96"/>
</p>
<p align="center">
<img src="assets/terminal-write.png" alt="pandaone write — AI Agent reports to Pandaone first" width="900"/>
<br/>
<em>AI Agent 通过 <code>pandaone write</code> 提交审计 → Pandaone 签发带签名的合规凭证 → 写入完成</em>
</p>
## 🧑💼 Pandaone is your codebase's personal manager. / Pandaone 是你代码库的专属经理。
Every AI agent is an employee working in your company — your codebase. Without a personal manager, employees do what they want — edit files, push commits, break production — and you have no idea what they did, when, or why.
**Pandaone is the personal manager you hired to protect your code.** Every AI agent — Claude, Cursor, Trae, or any tool you adopt tomorrow — must report to Pandaone first. Nothing gets written, nothing gets committed, nothing gets deployed until Pandaone approves it with a signed, auditable receipt:
```
┌─────────────────────────────────────────────┐
│ [APPROVED] 2026-09-11 03:47 │
│ File: src/payment.py │
│ Agent: Claude Code (employee #3) │
│ Reason: "Fix rounding" │
│ Problem: "Charge $50,001 on $50,000 bill" │
│ Approach: "Round before transfer, not after"│
│ Commit: a3f7b2c │
└─────────────────────────────────────────────┘
```
Without a personal manager, your agents move money — er, code — without receipts:
```
Without Pandaone With Pandaone
────────────── ──────────────
$50,000 ??? $50,000 src/payment.py
Reason: "Fix rounding"
By: Claude Code
When: 2026-09-11 03:47
Commit: a3f7b2c
```
**The left column is how most AI-agent-driven codebases look today.**
**The right column is how yours will look with one command:**
```bash
pip install pandaone-guard
```
Your personal manager is on duty 24/7 — **before, during, and after every code change**. Every AI agent — Claude, Cursor, Trae — operates under one auditable protocol. **No exceptions. No bypass.**
<p align="center">
<img src="assets/quick-demo.svg" alt="Pandaone Quick Demo — 3-Step Workflow" width="1200"/>
</p>
> **AI Agent 代码审计门禁** — 让每一次代码改动都留下合规、可追溯的证据链。
[](https://pypi.org/project/pandaone-guard/)
[](https://pepy.tech/project/pandaone-guard)
[](https://pepy.tech/project/pandaone-guard)
[](https://pypi.org/project/pandaone-guard/)
[](https://github.com/hellob1889/Pandaone-AI-Agent/blob/main/LICENSE)
[](https://github.com/hellob1889/Pandaone-AI-Agent/actions/workflows/audit.yml)
[-success)](https://github.com/hellob1889/Pandaone-AI-Agent/releases/tag/v0.7.1)
[](https://github.com/hellob1889/Pandaone-AI-Agent/blob/main/src/pandaone/i18n.py)
[](https://github.com/hellob1889/Pandaone-AI-Agent/blob/main/.github/workflows/lint.yml)
[](https://pypi.org/project/pandaone-guard/)
[](https://github.com/hellob1889/Pandaone-AI-Agent/releases)
[](https://github.com/hellob1889/Pandaone-AI-Agent/actions/workflows/audit.yml)
[](https://github.com/hellob1889/Pandaone-AI-Agent/actions/workflows/lint.yml)
[](https://m8ven.ai/mcp/hellob1889/pandaone-ai-agent)
[English](#english) | [中文](#中文)
---
## 中文 / Chinese
### 是什么？ / What is it?
Pandaone 是一个 **7 层防御体系**，强制 AI Agent（或任何开发者）写代码前必须经过审计：
任何受保护文件的修改都必须通过 `pandaone write` 命令，留下 reason / problem / approach 三段式审计记录。
**适用场景 / Use Cases**：
- 🤖 AI Agent 开发：防止 Agent 绕过审查直接改代码
- 🏢 企业合规：满足 SOC2 / ISO 27001 等代码变更审计要求
- 👥 团队协作：所有 PR 必须有审计记录才能合入
### 核心特性 / Core Features
- **17 种文本格式 + 24 种二进制格式保护**（`.py` / `.md` / `.json` / `.yml` / `.env` / `.png` / `.pdf` / ...）
- **7 层防御体系** / **7-Layer Defense System** (see [Defense Layers](#防御体系))
- **MCP Server 原生支持** — Claude / Cursor / Trae 等 AI IDE 直连
- **13 种审计日志导出格式**（Excel / Word / PDF / SQLite / ...）
- **纯 Python** — Windows / macOS / Linux 通吃，**无 C 扩展**
- **右键菜单集成** — 一行命令在 3 大平台安装原生右键菜单（HKCU / Quick Action / Nautilus + Dolphin）
- **i18n 国际化** — `zh-CN` / `en` 双语 CLI 自动检测 + `--lang` 强制 + 持久化偏好
### 快速开始 / Quick Start
#### 安装 / Installation
**方式 A：从 PyPI 安装（最简单）**
```bash
pip install pandaone-guard
# 一键装上右键菜单（Windows / macOS / Linux 自动检测）
pandaone install-context
```
**方式 B：从源码一键安装（推荐开发者使用，含最新 i18n + CI lint）**
```bash
git clone https://github.com/hellob1889/Pandaone-AI-Agent.git
cd pandaone
# Windows
powershell -ExecutionPolicy Bypass -File scripts/install.ps1
# macOS / Linux
./scripts/install.sh
```
**安装脚本自动做 / Install script auto-handles**：
1. 检测 Python ≥ 3.10
2. 探测 git（不在 PATH 时尝试常见安装路径）
3. 卸载 site-packages 里可能存在的老 pandaone 版本（避免版本冲突）
4. `pip install -e .` 本地源码 editable 安装
5. 验证 pandaone 可用（`python -m pandaone --version`）
6. 调用 `doctor.py --fix --persist-path` 自动修复剩余问题（依赖、setuptools、指纹、PATH 持久化到 HKCU）
#### 环境诊断（任何时候都能跑）/ Environment Diagnostics (run anytime)
```bash
python scripts/doctor.py # 人类可读
python scripts/doctor.py --json # CI 用（返回 pass/fail）
python scripts/doctor.py --quiet # 只显示 WARN / FAIL
python scripts/doctor.py --fix # 自动修复 13 类问题（仅当前会话生效）
python scripts/doctor.py --fix --persist-path # 持久化 PATH 到 HKCU（重启 shell 生效）
python scripts/doctor.py --fix-only # 只跑修复 + 重测，跳过详细诊断
```
检测 9 个项目 + **自动修复 13 类问题**（pip / git / pandaone 装在 site-packages / pandaone.exe PATH / 指纹污染 / setuptools / 5 个运行时依赖）。
**`--fix --persist-path` 会做什么**：
- 卸载 site-packages 老版本 → 重装本地源码
- 删除污染的 `~/.pandaone_fp.txt`
- 自动 `pip install` 缺失的运行时依赖
- Windows：用 `setx PATH "%PATH%;<Scripts>"` 持久化到 `HKCU\Environment\Path`（新开的 PowerShell 自动看到）
- macOS / Linux：追加 `export PATH="..."` 到 `~/.bashrc` / `~/.zshrc`
#### 初始化项目 / Initialize Project
```bash
cd /path/to/your-project
pandaone init --root .
# 自动：
# - 创建 .pandaone/ 目录
# - 生成 config.json（17 文本 + 24 二进制扩展名）
# - 创建 binary_snapshots.json（SHA256 字典）
# - 初始化 .gitignore 排除
```
#### 写代码（合规路径）/ Write Code (Compliant Path)
```bash
# 修改 Python 文件
pandaone write \
--file src/main.py \
--reason "修复 user_id 类型注解" \
--problem "原代码用 int，实际可能是 None" \
--approach "改为 Optional[int]" \
--old 'def get_user(user_id: int):' \
--new 'def get_user(user_id: Optional[int]):'
# 替换二进制文件（图片/文档/PDF 等）
pandaone write \
--file assets/logo.png \
--reason "更新品牌 logo" \
--problem "旧 logo 与新品牌色不匹配" \
--approach "用新版 logo 替换" \
--from-file /tmp/new_logo.png
```
#### 查看审计日志 / View Audit Log
<p align="center">
<img src="assets/terminal-log.png" alt="pandaone log — Audit history" width="900"/>
<br/>
<em>审计历史：每条变更带 reason / problem / approach 三段式凭证</em>
</p>
```bash
# 命令行查看
pandaone log --last 10
# 导出为 Excel
pandaone log --format xlsx --output audit_report.xlsx
# 导出为 PDF（含中文支持）
pandaone log --format pdf --output audit_report.pdf
# 全部 13 种格式：text / csv / tsv / json / yaml / md / html / xlsx / docx / pdf / sqlite / rst / asciidoc
```
#### 项目状态仪表盘 / Project Status Dashboard
```bash
pandaone status --root .
```
<p align="center">
<img src="assets/terminal-status.png" alt="pandaone status — Project dashboard" width="900"/>
<br/>
<em>终端版：项目状态 + 最近审计表（CLI 一行命令）</em>
</p>
显示 / Displays：项目路径、配置摘要、L1 锁定状态、二进制快照、最近审计、版本指纹等。
Project path, configuration summary, L1 lock state, binary snapshots, recent audits, version fingerprints, etc.
**🆕 Web 实时仪表盘 / Web Real-time Dashboard**:
```bash
pandaone serve # 浏览器打开 http://localhost:8765
```
<p align="center">
<img src="assets/dashboard.png" alt="Pandaone Web Dashboard — 15 audit records, 3 agents" width="900"/>
<br/>
<em>Web 版：15 条审计记录 · 11 APPROVED / 4 REJECTED · claude-code / cursor / trae 三方协作实时推送</em>
</p>
### 防御体系（7 层） / Defense Layers (7 Layers)
| 层 / Layer | 组件 / Component | 作用 / Function | 被绕过后的兜底 / Fallback if Bypassed |
|---|---|---|---|
| **L1** | file chmod | 文件级只读锁（attrib +r / chmod -w）<br/>File-level read-only lock | L2 watchdog |
| **L2** | watchdog | 实时文件监控 + git checkout 回滚<br/>Real-time file monitor + git checkout rollback | L3 hook |
| **L3** | pre-commit hook | 严格校验每个 staged 文件的 APPROVED 记录<br/>Strict validation of APPROVED record for every staged file | L4 / L5 |
| **L4** | 启动读 README<br/>Read README on startup | CLI 启动时加载项目元数据<br/>CLI loads project metadata at startup | L5 |
| **L5** | 自指纹<br/>Self-fingerprint | CLI 自身 SHA256 检测篡改<br/>CLI's own SHA256 detects tampering | L6 |
| **L6** | 二进制 SHA256 snapshot<br/>Binary SHA256 snapshot | 检测 .png/.pdf 等二进制篡改<br/>Detects tampering of .png/.pdf binaries | L7 |
| **L7** | GitHub Actions CI | PR 合入前审计验证（最终兜底）<br/>Audit verification before PR merge (final fallback) | 人工 review<br/>Manual review |
### MCP Server（AI Agent 直连 / AI Agent Native）
```json
// claude_desktop_config.json 或 Cursor MCP 配置
// claude_desktop_config.json or Cursor MCP configuration
{
"mcpServers": {
"pandaone": {
"command": "pandaone-mcp",
"env": {}
}
}
}
```
暴露 **11 个工具**：`pandaone_init` / `pandaone_lock` / `pandaone_unlock` / `pandaone_write` / `pandaone_log` / `pandaone_status` / `pandaone_install_hook` / `pandaone_watch` / `pandaone_install_git` / `pandaone_fingerprint_update` / `pandaone_ci`
**所有 11 个工具都已声明完整的 4 个 MCP annotations**（`readOnlyHint` / `destructiveHint` / `idempotentHint` / `openWorldHint`）—— **OpenAI 目录合规**（v0.7.14 修复）。
#### 在 ChatGPT 中使用 / Use with ChatGPT
ChatGPT Desktop app 的 Developer Mode（开发者模式）原生支持 MCP server。把 pandaone 加进去：
**步骤**：
1. 打开 ChatGPT Desktop app
2. `Settings` → `Beta features` → **开启 Developer Mode**
3. `Settings` → `Connectors` → `Create new connector`
4. 填入以下 JSON（**字段名一字不差**）：
```json
{
"name": "pandaone-guard",
"command": "pandaone-mcp",
"args": [],
"env": {}
}
```
5. 第一次对话前，ChatGPT 会要求安装 pandaone-guard。如未自动装，在终端手动跑：
```bash
# macOS / Linux
pip3 install pandaone-guard
# Windows (PowerShell)
pip install pandaone-guard
```
**进阶**（自定义 venv / Python 路径 / 中文界面）：
```json
{
"name": "pandaone-guard",
"command": "/path/to/venv/bin/python",
"args": ["-m", "pandaone_mcp"],
"env": {
"PANDAX_LANG": "zh"
}
}
```
**环境变量说明** / Environment variables：
| 变量 | 类型 | 默认 | 说明 |
|---|---|---|---|
| `PANDAX_FP_PASSWORD` | 🔐 secret | `"0000"` | 仅 `pandaone_fingerprint_update` / `pandaone_watch` 守护进程鉴权需要；本地设置，不上传 |
| `PANDAX_LANG` | config | `"zh"` | 界面语言（`zh` / `en`）|
| `PANDAONE_SKIP_GIT_CHECK` | config | unset | 设 `1` 跳过 git 检测（无 git 环境用）|
| `NO_COLOR` | config | unset | 设 `1` 禁用彩色输出 |
**只有一个 secret**：`PANDAX_FP_PASSWORD`。本地配置，永不上传到任何远程。
### 真实场景测试 / Real-World Validation
完整实战验证报告：[实战验证报告.md](实战验证报告.md)
包含 / Includes：
- 7 层防御每层实战证据 / 7-layer defense, each layer with real-world evidence
- L3 hook 严重 bug 的发现 + 修复
- 隐藏文件锁定 bug 的发现 + 修复
- 13 种导出格式实测 / 13 export formats tested
- MCP 协议完整工作流
### 详细示例集 / Detailed Examples
- [EXAMPLES.md](EXAMPLES.md) — 10 个真实工作流 + 终端输出
- [examples/](examples/) — 5 个可运行的 Python demo 脚本
- [docs/](docs/) — 中英双语文档站（mkdocs）
- [docs/index.html](docs/index.html) — 交互式 HTML 文档首页（带终端动画）
### 命令清单 / Command List
| 命令 / Command | 用途 / Purpose |
|---|---|
| `pandaone init` | 初始化项目 |
| `pandaone lock` / `unlock` | 锁定 / 解锁所有受保护文件 |
| `pandaone write` | **核心**：审计写入（支持文本/二进制） |
| `pandaone log` | 查询审计历史（13 种导出格式） |
| `pandaone status` | 项目状态仪表盘 |
| `pandaone install-hook` | 安装 L3 pre-commit hook |
| `pandaone watch` | 启动 L2 watchdog 守护进程 |
| `pandaone serve` | **🆕 Web 实时仪表盘**（浏览器打开 `localhost:8765`,watchdog + SSE <100ms 推送）|
| `pandaone install-git` | 自动安装 git |
| `pandaone ci` | L7 CI 验证（git diff vs audit log）|
| `pandaone-mcp` | 启动 MCP server（stdio JSON-RPC） |
### 开发与测试 / Development & Testing
```bash
git clone https://github.com/hellob1889/Pandaone-AI-Agent
cd pandaone
pip install -e .[dev]
pytest tests/ -v
```
当前测试数：**342 passed, 1 skipped**（覆盖 i18n / write / status / lock / ci / watchdog / export / init / e2e / serve / desktop-icon / gitignore）
### 路线图 / Roadmap
| 版本 / Version | 状态 / Status | 关键能力 / Key Capability |
|---|---|---|
| v0.1.0 | ✅ | Phase 1-3 MVP（5 层防御） |
| v0.2.0 | ✅ | 多格式导出（7 种） |
| v0.3.0 | ✅ | 17 种文本保护 |
| v0.4.0 | ✅ | 24 种二进制 SHA256 |
| v0.5.0 | ✅ | MCP Server |
| v0.6.0 | ✅ | GitHub Actions CI |
| v0.6.1 | ✅ | L3 hook 强化（实战验证） |
| **v0.6.2** | ✅ | 隐藏文件锁定 bug 修复 |
| **v0.7.0** | ✅ | Phase 9 OS 右键菜单 + Phase 10 i18n（zh-CN/en）+ doctor.py 环境自检 + auto-fix 13 类 + PATH 持久化 |
| **v0.7.1** | ✅ | **28 个 bug 全修 + 4 大新功能**：P0 安全（#8/#12×2/#22/#23）+ P1（#2/#5/#15/#29）+ P2（#21/#6/#20/#9-#10）+ P3（#13/#4/#26/#39）+ UX（#14/#17/#48/#25/#28）+ 工程化（#版本漂移 / #README 分组标签 / #CRLF 根因）+ **🆕 文件夹熊猫锁图标**（desktop.ini + ICO）+ **🆕 Web 实时仪表盘**（watchdog + SSE <100ms）+ **🆕 Git 兼容**（init 自动写 .gitignore）+ **🆕 右键菜单真实可用验证**，298 测试通过 |
| **v0.7.2** | ✅ | **CI 工程化修复**：publish.yml Tests job 在干净 ubuntu-latest 容器 25s exit 1（setuptools pin 缺失）→ pin `setuptools==80.10.2` + `--no-build-isolation`，新增 pytest log artifact 上传。340 测试通过 |
| **v0.7.3** | ✅ | **agent 身份 + diff 捕获 + 面板 UI**：`--agent` 参数追踪调用方（Claude / Cursor / Trae / user:name）+ `--verbose` 完整 diff + 彩色面板格式（状态 / 文件 / commit / 行数 / 原因 / 问题 / 方法 / 差异）+ status 加 agent 分组统计 + HTML 导出加卡片布局 + i18n 新增 16 个键。342 测试通过 |
完整历史：[CHANGELOG.md](CHANGELOG.md)
### 许可 / License
MIT License — 详见 [LICENSE](LICENSE)
---
## English
### What is it?
Pandaone is a **7-layer defense system** that forces every code change through an audit gate.
Any modification to a protected file (17 text + 24 binary formats) must go through `pandaone write`,
which records reason / problem / approach as immutable audit evidence.
### Use cases
- 🤖 AI Agent development: prevent agents from bypassing review
- 🏢 Enterprise compliance: meet SOC2 / ISO 27001 code change audit requirements
- 👥 Team collaboration: every PR must have audit records before merging
### Install
**Option A: from PyPI (easiest)**
```bash
pip install pandaone-guard
```
**Option B: from source (recommended for developers, includes latest i18n + CI lint)**
```bash
git clone https://github.com/hellob1889/Pandaone-AI-Agent.git
cd pandaone
# Windows
powershell -ExecutionPolicy Bypass -File scripts/install.ps1
# macOS / Linux
./scripts/install.sh
```
**The install script automatically**:
1. Checks Python >= 3.10
2. Locates git (scans common install paths if not in PATH)
3. Removes stale pandaone from site-packages (prevents version conflicts)
4. `pip install -e .` local source editable install
5. Verifies pandaone works (`python -m pandaone --version`)
6. Runs `doctor.py --fix --persist-path` (auto-fix remaining issues: deps / setuptools / fingerprint / persist PATH to HKCU)
### Environment diagnostics (run anytime)
```bash
python scripts/doctor.py # human-readable
python scripts/doctor.py --json # CI mode (returns pass/fail)
python scripts/doctor.py --quiet # only WARN / FAIL
python scripts/doctor.py --fix # auto-fix 13 issue classes (session only)
python scripts/doctor.py --fix --persist-path # persist PATH to HKCU (new shell)
python scripts/doctor.py --fix-only # just fix + re-check, skip detailed diagnosis
```
Checks 9 items + **auto-fixes 13 issue classes** (pip / git / pandaone-in-site-packages / pandaone.exe PATH / fingerprint pollution / setuptools / 5 runtime dependencies).
**What `--fix --persist-path` does**:
- Uninstall stale site-packages version, reinstall local source
- Remove polluted `~/.pandaone_fp.txt`
- Auto `pip install` for missing runtime deps
- Windows: use `setx PATH "%PATH%;<Scripts>"` to persist to `HKCU\Environment\Path` (new PowerShell shells see it automatically)
- macOS / Linux: append `export PATH="..."` to `~/.bashrc` / `~/.zshrc`
### Quick start
```bash
cd /path/to/your-project
pandaone init --root .
pandaone write \
--file src/main.py \
--reason "Fix user_id type annotation" \
--problem "Original used int, could be None" \
--approach "Change to Optional[int]" \
--old 'def get_user(user_id: int):' \
--new 'def get_user(user_id: Optional[int]):'
```
### 7 Defense Layers
| Layer | Component | Purpose |
|---|---|---|
| L1 | file chmod | File-level read-only lock |
| L2 | watchdog | Real-time file monitoring + git checkout rollback |
| L3 | pre-commit hook | Strict per-file APPROVED validation |
| L4 | startup README | CLI loads project metadata on startup |
| L5 | self-fingerprint | CLI's own SHA256 detects tampering |
| L6 | binary SHA256 snapshot | Detect .png/.pdf binary tampering |
| L7 | GitHub Actions CI | Pre-merge audit verification (final backstop) |
### MCP Server
```json
{
"mcpServers": {
"pandaone": {
"command": "pandaone-mcp",
"env": {}
}
}
}
```
Exposes 11 tools for AI agents (Claude / Cursor / Trae).
**All 11 tools declare the full set of 4 MCP annotations** (`readOnlyHint` / `destructiveHint` / `idempotentHint` / `openWorldHint`) — **OpenAI directory compliant** (fixed in v0.7.14).
#### Use with ChatGPT
ChatGPT Desktop app's Developer Mode natively supports MCP servers. To add pandaone:
**Steps**:
1. Open ChatGPT Desktop app
2. `Settings` → `Beta features` → **enable Developer Mode**
3. `Settings` → `Connectors` → `Create new connector`
4. Paste this JSON (field names must match exactly):
```json
{
"name": "pandaone-guard",
"command": "pandaone-mcp",
"args": [],
"env": {}
}
```
5. ChatGPT will offer to install `pandaone-guard` on first use. If it doesn't auto-install, run manually:
```bash
# macOS / Linux
pip3 install pandaone-guard
# Windows (PowerShell)
pip install pandaone-guard
```
**Advanced** (custom venv / Python path / English interface):
```json
{
"name": "pandaone-guard",
"command": "/path/to/venv/bin/python",
"args": ["-m", "pandaone_mcp"],
"env": {
"PANDAX_LANG": "en"
}
}
```
**Environment variables**:
| Variable | Type | Default | Purpose |
|---|---|---|---|
| `PANDAX_FP_PASSWORD` | 🔐 secret | `"0000"` | Only needed for `pandaone_fingerprint_update` / `pandaone_watch` daemon auth; set locally, never uploaded |
| `PANDAX_LANG` | config | `"zh"` | UI language (`zh` / `en`) |
| `PANDAONE_SKIP_GIT_CHECK` | config | unset | Set to `1` to skip git detection (for git-less environments) |
| `NO_COLOR` | config | unset | Set to `1` to disable colored output |
**Only one secret**: `PANDAX_FP_PASSWORD`. Local config only, never sent to any remote.
### License
MIT — see [LICENSE](LICENSE)
### Links
- [PyPI Package](https://pypi.org/project/pandaone-guard/)
- [GitHub Repository](https://github.com/hellob1889/Pandaone-AI-Agent)
- [Issue Tracker](https://github.com/hellob1889/Pandaone-AI-Agent/issues)
- [Documentation](https://github.com/hellob1889/Pandaone-AI-Agent/blob/main/README.md)
- [Changelog](https://github.com/hellob1889/Pandaone-AI-Agent/blob/main/CHANGELOG.md)
- [实战验证报告 (Validation Report)](实战验证报告.md)TDQS
A3.5/5.0
Scored across 11 tools
Disambiguation5/5
每个工具都有清晰不同的目的：日志查看、hook安装、watch守护、git便携安装、指纹更新、CI验证、锁定/解锁、初始化、审计写入和状态查看。即使lock/unlock是配对的，描述也明确区分了动作方向，不会产生混淆。
Naming Consistency4/5
所有工具都统一使用pandaone_前缀，且大部分采用动词+宾语模式（如install_hook、install_git、write、lock）。但pandaone_log和pandaone_ci是名词形式，fingerprint_update的词语顺序略有倒置，与其他动词开头的命名不完全一致，但整体可读性强，模式基本可预测。
Tool Count5/5
11个工具对于文件审计和保护系统来说非常合适，每个工具都覆盖一个明确的职责，没有冗余或缺失，数量处于理想范围内。
Completeness4/5
从初始化、审计写入、锁定/解锁、日志查询、状态查看、watch守护、CI验证到hook和git安装，核心工作流覆盖完整。但缺少如系统卸载或重置等运维工具，以及更细粒度的文件历史查看，但通过日志过滤基本可以满足，整体没有明显死胡同。
Maintenance
ActivityMaintained
ResponsivenessNo issues