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Deep Coding

作者 extraterrest · GitHub ↗ · v0.0.3 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install deep-coding
功能描述
Deep coding multi-agent development system. Use when the user wants to build software projects using the Orchestrator to Builder to Reviewer workflow, mentio...
使用说明 (SKILL.md)

System Dependencies

This skill requires the following system capabilities:

Dependency Purpose Required? Check
python3 Dashboard server (port 8765) Yes python3 --version
node / npm Project builds, Playwright For web projects node -v, npm -v
playwright E2E browser testing (Reviewers) Optional, for E2E npx playwright --version
ACP runtime Builder/Reviewer agent execution Optional, see below Platform-specific

No specific coding agent is required. The default configuration uses ACP + qoder, but you can use any available agent runtime. See First-Time Setup for configuration options.

Security Notes

⚠️ Dashboard server (server.py):

  • Binds to 127.0.0.1:8765 only — never expose to public network
  • Serves files from the project directory — verify no secrets (API keys, tokens) are present
  • Includes path traversal protection via safe_path() check

⚠️ Code execution:

  • Builders and Reviewers will execute and run arbitrary project code
  • For web projects: HTTP server serves project files locally
  • E2E tests use Playwright to open and interact with pages in a real browser
  • Only run on machines where executing generated code is acceptable
  • Use containers/VMs for untrusted projects

First-Time Setup

When a user installs this skill for the first time, guide them through the following steps:

Step 1: Create Project Workspace

mkdir -p my-projects/{requests/done,logs}
cp \x3Cskill-dir>/assets/server.py my-projects/
cp \x3Cskill-dir>/assets/dashboard.html my-projects/
cd my-projects

This creates the project root with all required directories and the Dashboard assets.

Step 2: Configure Orchestrator Agent

Create an Orchestrator agent in your openclaw.json (or equivalent config):

{
  "id": "orchestrator",
  "name": "Orchestrator",
  "workspace": "\x3Cyour-path>/my-projects"
}

Give the Orchestrator a heartbeat prompt that references references/orchestrator-rules.md.

Step 3: Configure Builder Agent(s)

Choose your preferred coding agent(s). Options:

Option Configuration Notes
ACP + qoder runtime: "acp", agentId: "qoder" Default, requires acpx plugin
ACP + claude runtime: "acp", agentId: "claude" Alternative ACP agent
ACP + codex runtime: "acp", agentId: "codex" OpenAI Codex
Subagent runtime runtime: "subagent" Built-in, no extra setup
PTY coding agents exec with PTY Claude Code, Codex CLI, etc.

The Orchestrator rules (references/orchestrator-rules.md) default to ACP + qoder, but you should update the agent ID to match your setup.

Recommended: Set up a 3-tier fallback chain

  1. Primary: Your preferred coding agent (e.g., qoder, claude)
  2. Fallback 1: Alternative ACP agent (e.g., claude if qoder is 429'd)
  3. Fallback 2: Built-in subagent runtime

Step 4: Allow Tool Access

Ensure your Orchestrator and Builder agents have access to:

  • read, write, edit — for file operations
  • exec — for running builds, tests, servers
  • sessions_spawn, sessions_send, sessions_list — for agent communication
  • subagents — for managing spawned agents

In openclaw.json:

{
  "tools": {
    "sessions": {
      "visibility": "all"
    },
    "agentToAgent": {
      "enabled": true,
      "allow": ["main", "orchestrator", "qoder-dev", "claude-dev"]
    }
  },
  "acp": {
    "enabled": true,
    "backend": "acpx",
    "defaultAgent": "qoder",
    "allowedAgents": ["qoder", "claude", "codex"]
  }
}

Step 5: Choose Your LLM

Set the default model for the Orchestrator and agents:

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "your-provider/your-model"
      }
    }
  }
}

For coding agents (qoder, claude, codex), they use their own model — no LLM config needed.

Step 6: Verify Setup

cd my-projects
python3 server.py
# Open http://localhost:8765 — should show empty dashboard

Harness Deep Coding System

Multi-agent development: Orchestrator decomposes → Builders code → Reviewers verify → E2E test → deliver.

Roles

User-Facing Agent (you)

  • Gather requirements through conversation
  • Create request JSON at projects/requests/TIMESTAMP.json (use actual timestamp)
  • Notify Orchestrator via sessions_send to agent:orchestrator:main
  • Report progress every heartbeat when project is active

Orchestrator

  • Decomposes project into 2-4 modules + mandatory integration-test
  • Creates project-state.json with module states
  • Spawns Builders and Reviewers via sessions_spawn
  • Monitors progress via heartbeat, handles failures
  • Runs E2E smoke test after bugfix/feature accepted

Builder

  • Codes independently per module
  • Uses configured agent runtime (ACP subagent, or fallback)
  • Writes to logs/builder-MODULE.log (APPEND, UTC+8)

Reviewer

  • MUST actually test the application, not just read code
  • For web projects: serve via HTTP, verify in browser
  • Writes detailed review results to review_history
  • Writes to logs/reviewer-MODULE.log (APPEND, UTC+8)

User-Facing Workflow

1. Gather Requirements

  • What to build, key features, constraints, tech stack
  • Break into 2-4 logical modules (data → core → render → UI)
  • Auto-add final integration-test module depending on ALL others

2. Create Request

{
  "name": "Project Name",
  "description": "What it does",
  "owner": "user name",
  "tags": ["web", "game"]
}

Path: \x3Cproject-root>/requests/TIMESTAMP.json (use actual timestamp)

3. Notify Orchestrator

Send to agent:orchestrator:main:

  • Request file path
  • Instructions to decompose into modules
  • Create project-state.json
  • Spawn Builder for first module
  • Use per-agent logs, APPEND mode, UTC+8
  • Run E2E smoke test after acceptance

4. Progress Reporting

Read project-state.json every heartbeat:

  • Report completion % and module states
  • Announce 100% completion

Project Structure

All paths are relative to your project root directory:

\x3Cproject-root>/
├── projects-registry.json          ← All projects overview
├── server.py                       ← Dashboard server (port 8765)
├── dashboard.html                  ← Dashboard UI
├── requests/
│   └── done/                       ← Processed requests
├── logs/                           ← Agent activity logs
├── PROJECT-SLUG/
│   ├── project-state.json           ← Module states, review history
│   ├── logs/
│   │   ├── orchestrator.log         ← Orchestrator decisions
│   │   ├── builder-MODULE.log       ← Each Builder writes own file
│   │   └── reviewer-MODULE.log      ← Each Reviewer writes own file
│   └── SOURCE CODE (generated files)

See references/architecture.md for full project structure, module lifecycle, and dashboard details.

Module Lifecycle

pending → in_progress → ready_for_review → in_review → accepted
                        ↑                    |
                        └── needs_revision ──┘

Critical Rules

Rule Description
One action per heartbeat Never do multiple spawns in one cycle
Spawn Reviewer immediately Never leave ready_for_review more than one cycle
Reviewer writes results Must write to review_history array, never just change state
E2E smoke test Mandatory for bugfixes and new features before delivery
No archive copies DO NOT copy project-state.json to archive/

Common Issues

Issue Fix
429 rate limit Wait, then re-spawn. Do NOT self-accept
Missing E2E Bugfix/feature accepted → must spawn E2E Reviewer
Reviewer not spawned Check sessions_list, spawn if missing
Builder timeout Check if files exist, accept if complete
Archive duplicates Orchestrator should NOT copy to archive/

Dashboard

Dashboard is included in assets/server.py and assets/dashboard.html.

Usage:

  1. Copy assets/server.py and assets/dashboard.html to your project root directory
  2. Run: python3 server.py
  3. Open: http://localhost:8765

Security: The server binds to 127.0.0.1 only and includes path traversal protection.

Features: project list, completion status, module states, agent activity timeline.

安全使用建议
This skill appears to be what it says: a local multi-agent development harness. Key risks to consider before installing: 1) The included server.py will serve files from the project directory — verify there are no API keys or secrets in your project before running the server, and run it bound to localhost only as instructed. 2) Builders/Reviewers are required to execute generated project code and run tests (including Playwright); for untrusted projects, run the system inside isolated VMs or containers. 3) The dashboard HTML pulls JS from public CDNs (Tailwind, Vue, markdown-it); if your environment restricts outbound network calls or you need a hardened supply chain, consider hosting those assets locally. 4) Only grant the platform the minimal agent/tool permissions needed and limit allowed agent IDs to trusted ones. If you want a firmer assessment, provide the full, untruncated server.py content to verify the safe_path() implementation and confirm there are no hidden network callbacks or backdoors in the code.
功能分析
Type: OpenClaw Skill Name: deep-coding Version: 0.0.3 The 'deep-coding' skill bundle is a multi-agent orchestration system designed for complex software development tasks. It utilizes an Orchestrator agent to manage Builders and Reviewers, providing a local Python-based dashboard (server.py and dashboard.html) for progress monitoring. While the system requires high-risk capabilities such as arbitrary code execution (exec) and broad file access to function, these are clearly aligned with its stated purpose and are accompanied by explicit security warnings in SKILL.md. The dashboard server implements path traversal protection and binds only to the local interface (127.0.0.1). No evidence of malicious intent, data exfiltration, or harmful prompt injection was found.
能力评估
Purpose & Capability
Name/description (multi-agent deep-coding harness) matches what the skill asks for: orchestration rules, spawn builders/reviewers, file layout, and a local dashboard server. Declared runtime needs (python3, node, Playwright, agent runtimes like ACP/qoder) are appropriate for building, serving, and E2E testing code.
Instruction Scope
SKILL.md instructs the agent to create a project workspace, copy and run server.py (serves project files), spawn builders/reviewers, and require those agents have read/write/exec and sessions permissions. It explicitly requires builders/reviewers to execute generated project code and run E2E tests. This is expected for the tool's purpose but increases risk: running untrusted project code and serving project directories can expose secrets if present — the skill does warn about this.
Install Mechanism
Instruction-only skill with no install spec and only a small Python server script and HTML dashboard. No downloads from unknown URLs, no archives to extract. Low install risk.
Credentials
The skill does not declare required environment variables or credentials. It does recommend configuring runtime choices (BUILDER_RUNTIME, BUILDER_AGENT) and platform tool permissions; those are relevant to the orchestrator's operation and proportional to the stated capabilities.
Persistence & Privilege
Skill is not forced-always, does not request system-wide configuration changes in the manifest, and does not declare persistent elevated privileges. It does require the platform to grant agent tool permissions (read/write/exec/sessions/subagents) to function, which is appropriate for multi-agent orchestration but should be limited to trusted agents.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install deep-coding
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /deep-coding 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.0.3
Security fixes, relative paths, flexible Builder config, first-time setup guide
v0.0.2
Removed absolute paths, added Dashboard assets
v0.0.1
Initial release: Orchestrator → Builder → Reviewer multi-agent development system with Dashboard
v1.0.0
Initial release: Orchestrator → Builder → Reviewer multi-agent development system with Dashboard
元数据
Slug deep-coding
版本 0.0.3
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 4
常见问题

Deep Coding 是什么?

Deep coding multi-agent development system. Use when the user wants to build software projects using the Orchestrator to Builder to Reviewer workflow, mentio... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 175 次。

如何安装 Deep Coding?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install deep-coding」即可一键安装,无需额外配置。

Deep Coding 是免费的吗?

是的,Deep Coding 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Deep Coding 支持哪些平台?

Deep Coding 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Deep Coding?

由 extraterrest(@extraterrest)开发并维护,当前版本 v0.0.3。

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