DeepTask

AI 自动拆解需求与任务管理工具。实现 AI 自动拆解需求 → 人工审核 → AI 执行任务 → UT 验证 → **Git Commit** 的完整闭环。使用 SQLite 数据库管理项目、会话、子任务、MUF（最小功能单元）、单元测试。**核心特性：每完成一个 MUF 并通过 UT 后自动执行 git com...

Security Scan

The skill's code and instructions largely match its stated purpose (automating MUF → UT → git commit flow), but it autonomously writes and executes code and tests and will run git in whatever workspace you point it at — this presents operational risks that are not surfaced in the description.

Purpose & Capability

Name/description promise (AI task decomposition, running unit tests, and auto git commits) matches the included scripts. The CLI, ai_worker, and db manager implement project/session/MUF lifecycle, run tooling (python/node/moon), run unit tests, write code files, initialize git, commit and verify commits — all coherent with the declared purpose.

Instruction Scope

Runtime instructions and code explicitly direct the agent to write code files into a workspace (default ~/.openclaw/workspace), run unit tests, execute subprocesses (python/node/moon/git), initialize git, and commit changes with structured messages. Executing AI-generated code and stored test_code from the DB means the skill will execute arbitrary code on the host. The SKILL.md/CLI do not require any explicit prior user confirmation for commits beyond the normal flow, and they operate on whatever project_dir/workspace is provided — raising risk of unintended commits or execution in sensitive folders.

Install Mechanism

No install spec; this is instruction+code only. No downloads or external installers are used. The code is bundled in the skill and will run via Python when invoked by the user/agent.

Credentials

The skill requests no environment variables or external credentials, which is proportionate. However it requires filesystem access to the user's workspace and will call system binaries (git, python3, node, moon) via subprocess. Because it writes files and executes them, filesystem and execution privileges are effectively required; those are normal for this tool but may be surprising to users who expect a safer/sandboxed operation.

Persistence & Privilege

The skill is not marked always:true and does not request elevated platform privileges. It stores state in a local SQLite DB under ~/.openclaw/deeptask.db and uses a workspace under ~/.openclaw/workspace — both are self-contained and expected for this application.

What to consider before installing

What to consider before installing/using DeepTask: - The tool will create and write code files and run unit tests (it executes code on your machine). That means arbitrary code (AI-generated or stored tests) can run commands on your host — only use it where you trust the inputs (e.g., an isolated VM/container or an empty, disposable workspace). - It will initialize git and automatically commit changes to whatever project directory you configure (default ~/.openclaw/workspace/project_<ID>). Ensure the workspace is not pointed at important repositories or directories containing secrets to avoid accidental commits of sensitive data. - Review the bundled scripts (ai_worker.py, cli.py, db_manager.py) before running. Pay attention to how test_code or code_content from the DB is executed. If you need to run it, prefer a sandbox (container, VM) and disable network or restrict permissions where possible. - Recommended mitigations: set workspace to an isolated directory, back up important repos first, inspect/approve generated code before letting the skill run cycles that auto-commit, and consider running under a user account with limited access. If you need higher assurance, request the full untruncated code paths for review of the unit-test execution functions (run_unit_test/_run_python_test/_run_moon_test) to audit how tests are invoked and whether they can spawn unbounded shell commands.

DeepTask Skill

AI 自动拆解需求 → 人工审核 → AI 执行 → UT 验证 → Git Commit 的闭环流程管理工具。

核心目标

• 任务分解粒度可控：项目 → 会话 → 子任务 → MUF（最小功能单元）
• 明确状态机：每个环节有清晰的状态流转
• 人工审核精准介入：仅在关键节点（会话级）触发审核
• 失败自动熔断：7 次失败或 5 小时超时自动暂停
• 环境预检机制：执行前先检查工具链/环境是否可用
• Hello World 验证：新语言先写测试代码验证语法
• ⭐ Git Commit 追踪：每完成一个 MUF 并通过 UT 后自动执行 git commit

---

⭐ Git Commit 机制（核心特性）

Commit 触发条件

MUF 完成 → UT 验证通过 → 执行 git commit

Commit 信息格式

git commit -m "SE_ID:SE-1, ST_ID:ST-1, MUF_ID:MUF-1, UT_ID:UT-1"

完整流程

# ai_worker.py 中的 execute_muf 函数

def execute_muf(muf_id, project_dir, tool_name, se_id, st_id, code_content):
    # 1. 更新 MUF 状态为 in_progress
    db.update_status("mufs", muf_id, "in_progress")
    
    # 2. 实现 MUF 代码（写入文件，包含追踪注释）
    # 代码文件头部：# SE_ID:SE-1, ST_ID:ST-1, MUF_ID:MUF-1, UT_ID:UT-1
    
    # 3. 运行 UT 验证
    ut_passed = run_unit_test(ut_id, project_dir, tool_name)
    
    if not ut_passed:
        # UT 失败，标记为 failed，不执行 commit
        db.update_status("mufs", muf_id, "failed")
        return False
    
    # 4. ⭐ UT 通过，执行 git commit
    git_commit(project_dir, se_id, st_id, muf_id, ut_id)
    # commit 信息："SE_ID:SE-1, ST_ID:ST-1, MUF_ID:MUF-1, UT_ID:UT-1"
    
    # 5. 验证 git 历史
    verify_git_history(project_dir, se_id, st_id, muf_id, ut_id)
    
    # 6. 更新 MUF 状态为 completed
    db.update_status("mufs", muf_id, "completed")
    
    return True

验证 Commit

# 查看项目 git 历史
git log --oneline

# 查找特定 MUF 的 commit
git log --grep "SE_ID:SE-1" --grep "MUF_ID:MUF-1"

# 示例输出：
# a1b2c3d SE_ID:SE-1, ST_ID:ST-1, MUF_ID:MUF-1, UT_ID:UT-1

---

快速开始

1. 初始化数据库

python ~/.openclaw/skills/deeptask/scripts/cli.py init

2. 创建项目

python ~/.openclaw/skills/deeptask/scripts/cli.py create_project \
  --name "用户管理系统" \
  --desc "支持注册/登录/权限管理" \
  --summary "核心功能：RBAC 权限模型"

3. 执行完整周期（含 Git Commit）

python ~/.openclaw/skills/deeptask/scripts/ai_worker.py \
  --project DT-1 \
  --tool python3 \
  --workspace ~/.openclaw/workspace \
  --cycle

输出示例：

=== 执行 MUF: MUF-1 ===
  SE: SE-1, ST: ST-1, UT: UT-1
  📝 实现 MUF 代码...
  ✅ 代码已写入：project_DT-1/muf_1.py
  🧪 运行 UT 验证...
  ✅ UT 验证通过
  📦 执行 git commit...
  ✅ Git commit 成功：SE_ID:SE-1, ST_ID:ST-1, MUF_ID:MUF-1, UT_ID:UT-1
  ✅ Git commit 已验证
  ✅ MUF 完成

4. 查看 Git 历史

cd ~/.openclaw/workspace/project_DT-1
git log --oneline

5. 人工审核

# 批准会话
python ~/.openclaw/skills/deeptask/scripts/cli.py review \
  --session SE-1 \
  --status approved \
  --reviewer "张三" \
  --comments "Git commit 验证通过，代码质量良好"

---

执行流程（增强版）

0. 环境预检

# 检查工具链
env_ok, env_msg = check_environment("python3")
if not env_ok:
    trigger_fuse("环境不可用")

1. Hello World 验证

# 验证语法
hw_ok, hw_msg = hello_world_test("python3")
if not hw_ok:
    trigger_fuse("语法验证失败")

2. 初始化 Git

init_git(project_dir)

3. AI 拆解项目

项目 → 会话 → 子任务 → MUF

4. 执行 MUF（含 UT 验证和 Git Commit）⭐

MUF 实现 → UT 验证 → ✅ 通过 → Git Commit → 状态更新
                      ❌ 失败 → 标记 failed → 不 commit

5. 更新进度

检查子任务/会话完成情况，自动更新状态

6. 检查熔断

失败 MUF 自动记录审查请求

---

命令行工具

可用命令

命令	说明
init	初始化数据库
create_project	创建新项目
list_projects	列出所有项目
show_project	显示项目详情
review	人工审核
status	显示系统状态
check_env	检查环境
reset_project	重置项目状态
git_log	查看 Git Commit 历史 ⭐ NEW

Git Log 命令示例

# 查看项目 DT-1 的 git 历史
python cli.py git_log --project DT-1

# 查看特定 MUF 的 commit
python cli.py git_log --muf MUF-1

# 验证 commit 是否包含正确的追踪信息
python cli.py git_log --project DT-1 --verify

---

数据库结构

核心表

1. projects - 项目表
2. sessions - 会话表
3. subtasks - 子任务表
4. mufs - 最小功能单元表
5. unit_tests - 单元测试表
6. review_records - 人工审查记录表
7. git_commits - Git Commit 记录 ⭐ NEW

Git Commits 表结构

字段	类型	说明
id	INTEGER	自增 ID
se_id	TEXT	会话 ID
st_id	TEXT	子任务 ID
muf_id	TEXT	MUF ID
ut_id	TEXT	UT ID
commit_hash	TEXT	Git commit hash
commit_msg	TEXT	Commit 信息
committed_at	DATETIME	Commit 时间

---

检查清单（每次任务必做）

执行前检查

• 工具链是否安装？ (python3 --version)
• Hello World 是否通过？
• Git 是否初始化？

执行后验证

• MUF 代码已写入？
• UT 验证通过？
• Git commit 已执行？ ⭐
• Commit 信息包含 SE_ID/ST_ID/MUF_ID/UT_ID？ ⭐
• Git 历史可查询？ ⭐

---

使用场景

场景 1: 完整项目周期

# 1. 创建项目
python cli.py create_project --name "电商系统" --desc "在线购物平台"

# 2. 执行完整周期（含 Git Commit）
python ai_worker.py --project DT-1 --tool python3 --cycle

# 3. 查看 Git 历史
cd workspace/project_DT-1 && git log --oneline

场景 2: 验证 Git Commit

# 1. 执行 MUF
python ai_worker.py --project DT-1 --tool python3 --cycle

# 2. 验证 commit
git log --grep "MUF_ID:MUF-1"

# 3. 输出应包含：
# abc123 SE_ID:SE-1, ST_ID:ST-1, MUF_ID:MUF-1, UT_ID:UT-1

场景 3: 审计追踪

# 查找特定会话的所有 commit
git log --grep "SE_ID:SE-1" --oneline

# 统计每个 MUF 的 commit
git log --grep "MUF_ID" --oneline | wc -l

---

注意事项

1. Git Commit 仅在 UT 通过后执行 - UT 失败不 commit
2. Commit 信息格式固定 - 必须包含 SE_ID, ST_ID, MUF_ID, UT_ID
3. 代码文件包含追踪注释 - 文件头部应有 # SE_ID:xxx, ST_ID:xxx...
4. Git 仓库自动初始化 - 首次执行时自动 git init
5. Commit 失败不阻断流程 - Git 失败记录警告，继续执行

---

故障排查

Git Commit 未执行

# 1. 检查 UT 是否通过
python cli.py status  # 查看 UT 状态

# 2. 检查是否有代码更改
cd project_dir && git status

# 3. 手动执行 commit
git add -A && git commit -m "SE_ID:SE-1, ST_ID:ST-1, MUF_ID:MUF-1, UT_ID:UT-1"

Commit 信息格式错误

# 正确格式
git commit -m "SE_ID:SE-1, ST_ID:ST-1, MUF_ID:MUF-1, UT_ID:UT-1"

# 错误格式（缺少字段）
git commit -m "完成 MUF-1"  # ❌

Git 历史验证失败

# 验证 commit 是否存在
git log --grep "SE_ID:SE-1" --grep "MUF_ID:MUF-1"

# 如果找不到，检查 ai_worker.py 日志

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版本历史

• v1.0.0 - 初始版本
• v1.1.0 - 增加环境预检机制
• v1.1.0 - 增加 Hello World 验证
• v1.2.0 - 增加 Git Commit 追踪机制 ⭐
• v1.2.0 - 增加 git_log 命令
• v1.2.0 - 增加 commit 验证功能