← 返回 Skills 市场
liuchang8877

Agent Team Pipeline

作者 liuchang · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
222
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install agent-team-pipeline
功能描述
Use when needing to coordinate multiple AI agents in parallel for code development, testing, and review
使用说明 (SKILL.md)

Agent Team Pipeline

Overview

Coordinate OpenClaw as the brain dispatching multiple Codex instances in parallel: one for coding, one for testing, one for code review. Each agent works in an isolated git worktree to avoid conflicts.

When to Use

  • Need to implement a feature with separate code/test/review phases
  • Want parallel execution of independent tasks
  • Building a mini development team workflow

Not for:

  • Single simple tasks (use single agent)
  • Exploratory discussions requiring multi-round dialogue

Core Pattern

User → OpenClaw (brain) → Codex Coder → Codex Tester → Codex Reviewer
                        → Codex Tester (parallel)
                        → Codex Reviewer (parallel)

Setup

1. Create Isolated Worktrees

cd /Users/liuchang/.openclaw/workspace
git worktree add -b coder /Users/liuchang/agent-coder HEAD
git worktree add -b tester /Users/liuchang/agent-tester HEAD
git worktree add -b reviewer /Users/liuchang/agent-reviewer HEAD

2. Task Distribution Script

#!/bin/bash
CODER_DIR="/Users/liuchang/agent-coder"
TESTER_DIR="/Users/liuchang/agent-tester"
REVIEWER_DIR="/Users/liuchang/agent-reviewer"

case "$1" in
    coder)
        cd "$CODER_DIR" && codex exec "$2"
        ;;
    tester)
        cd "$TESTER_DIR" && codex exec "$2"
        ;;
    reviewer)
        cd "$REVIEWER_DIR" && codex exec "$2"
        ;;
esac

3. Workflow

  1. Coder receives task, writes code to agent-coder/
  2. Tester receives code, writes tests to agent-tester/
  3. Reviewer reviews code, provides feedback
  4. Loop: Coder addresses feedback → Reviewer confirms

Quick Reference

Role Worktree Command
Coder agent-coder codex exec "implement X"
Tester agent-tester codex exec "test X"
Reviewer agent-reviewer codex exec "review X"

Example

# Coder: Implement feature
codex exec "创建登录功能,使用 JWT"

# Tester: Write tests  
codex exec "为 ../agent-coder/login.py 写测试"

# Reviewer: Review code
codex exec "审查 ../agent-coder/login.py"

Common Mistakes

  • No worktree isolation → Use git worktree to prevent file conflicts
  • Sequential when parallel → Coder+Tester+Reviewer can run in parallel
  • Skipping review loop → Always verify fixes with reviewer before accepting

Real-World Impact

Successfully implemented hello.py example:

  • Coder created initial code
  • Tester wrote pytest file
  • Reviewer suggested main() + __main__ protection
  • Coder applied fixes
  • Reviewer confirmed compliance
安全使用建议
This skill appears to be a workflow pattern for running multiple agent roles, but it assumes host-side capabilities that it does not declare. Before installing or running it: (1) verify you have and trust the 'codex' CLI and git on your machine; (2) edit the SKILL.md/scripts to point at safe workspace paths (avoid hardcoded /Users/liuchang paths) or run in an isolated sandbox/VM; (3) inspect any 'codex exec' invocations because they execute arbitrary instructions and could run code that reads/writes files in your workspace; (4) if you intend to use this with real code, test the workflow in a disposable repository to ensure it doesn't touch sensitive data. If you need to proceed, update the skill metadata to declare required binaries (git, codex) so the capability and requirements match.
功能分析
Type: OpenClaw Skill Name: agent-team-pipeline Version: 1.0.0 The skill bundle describes a multi-agent orchestration workflow for code development, testing, and review using git worktrees and a 'codex' CLI tool. While it contains hardcoded absolute file paths specific to a user environment (/Users/liuchang/), which is a poor practice for portability, the logic is consistent with its stated purpose and lacks any indicators of malicious intent, data exfiltration, or unauthorized system access.
能力评估
Purpose & Capability
The skill's stated purpose (coordinate coder/tester/reviewer agents using isolated git worktrees) matches the instructions, but the SKILL.md assumes the presence of system tools (git, a 'codex' CLI) and uses absolute user paths. The registry metadata lists no required binaries or environment variables, which is inconsistent with the runtime steps that clearly need git and a 'codex' executable.
Instruction Scope
The instructions direct filesystem operations (git worktree creation under /Users/liuchang, cds into agent-specific directories) and invoke 'codex exec' with arbitrary task strings. They reference absolute paths in a specific user's home and relative paths that may access other worktrees (e.g., reviewing ../agent-coder/login.py). This grants the skill broad discretion to read/write and execute within the user's workspace and to run arbitrary commands via the 'codex' tool — actions beyond a pure orchestration description and not limited or sandboxed by the SKILL.md.
Install Mechanism
There is no install spec (instruction-only), which reduces installer risk. However, the runtime assumes external binaries (git, codex) are present and executable; the skill does not declare these requirements in the registry metadata. Because nothing is installed by the skill itself, disk-write risk from an installer is low, but runtime execution risk remains due to invoking host tools.
Credentials
The skill requires no declared environment variables or credentials, which is reasonable, but it hardcodes user-specific filesystem paths (/Users/liuchang/...) and implicitly requires access to the user's git repositories and working directories. The lack of declared binaries/env requirements while assuming access to system tools and a specific user's home is a proportionality mismatch and a usability/security concern.
Persistence & Privilege
The skill is not always-enabled and does not request elevated platform privileges. It does not include installation steps that would persistently modify other skills or system-wide settings. Autonomous invocation is allowed (platform default) but not combined with other high-risk indicators here.
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 agent-team-pipeline
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agent-team-pipeline 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
v1.0.0
- Initial release of agent-team-pipeline skill. - Enables coordination of multiple AI agents (coder, tester, reviewer) in parallel for code development workflows. - Uses isolated git worktrees for each agent to avoid conflicts. - Includes task distribution pattern and example setup/scripts. - Designed for multi-phase features, not simple or open-ended tasks.
元数据
Slug agent-team-pipeline
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Agent Team Pipeline 是什么?

Use when needing to coordinate multiple AI agents in parallel for code development, testing, and review. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 222 次。

如何安装 Agent Team Pipeline?

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

Agent Team Pipeline 是免费的吗?

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

Agent Team Pipeline 支持哪些平台?

Agent Team Pipeline 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 Agent Team Pipeline?

由 liuchang(@liuchang8877)开发并维护,当前版本 v1.0.0。

💬 留言讨论