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Ai Pair

作者 Axton · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install ai-pair
功能描述
AI Pair Collaboration Skill. Coordinate multiple AI models to work together: one creates (Author/Developer), two others review (Codex + Gemini). Works for co...
使用说明 (SKILL.md)

AI Pair Collaboration

Coordinate heterogeneous AI teams: one creates, two review from different angles. Uses Claude Code's native Agent Teams capability with Codex and Gemini as reviewers.

Why Multiple AI Reviewers?

Different AI models have fundamentally different review tendencies. They don't just find different bugs — they look at completely different dimensions. Using reviewers from different model families maximizes coverage.

Commands

/ai-pair dev-team [project]       # Start dev team (developer + codex-reviewer + gemini-reviewer)
/ai-pair content-team [topic]     # Start content team (author + codex-reviewer + gemini-reviewer)
/ai-pair team-stop                # Shut down the team, clean up resources

Examples:

/ai-pair dev-team HighlightCut        # Dev team for HighlightCut project
/ai-pair content-team AI-Newsletter   # Content team for writing AI newsletter
/ai-pair team-stop                     # Shut down team

Prerequisites

  • Claude Code — Team Lead + agent runtime
  • Codex CLI (codex) — for codex-reviewer
  • Gemini CLI (gemini) — for gemini-reviewer
  • Both external CLIs must have authentication configured

Team Architecture

Dev Team (/ai-pair dev-team [project])

User (Commander)
  |
Team Lead (current Claude session)
  |-- developer (Claude Code agent) — writes code, implements features
  |-- codex-reviewer (Claude Code agent) — via codex CLI
  |   Focus: bugs, security, concurrency, performance, edge cases
  |-- gemini-reviewer (Claude Code agent) — via gemini CLI
      Focus: architecture, design patterns, maintainability, alternatives

Content Team (/ai-pair content-team [topic])

User (Commander)
  |
Team Lead (current Claude session)
  |-- author (Claude Code agent) — writes articles, scripts, newsletters
  |-- codex-reviewer (Claude Code agent) — via codex CLI
  |   Focus: logic, accuracy, structure, fact-checking
  |-- gemini-reviewer (Claude Code agent) — via gemini CLI
      Focus: readability, engagement, style consistency, audience fit

Workflow (Semi-Automatic)

Team Lead coordinates the following loop:

  1. User assigns task → Team Lead sends to developer/author
  2. Developer/author completes → Team Lead shows result to user
  3. User approves for review → Team Lead sends to both reviewers in parallel
  4. Reviewers report back → Team Lead consolidates and presents:
    ## Codex Review
    {codex-reviewer feedback summary}
    
    ## Gemini Review
    {gemini-reviewer feedback summary}
    
  5. User decides → "Revise" (loop back to step 1) or "Pass" (next task or end)

The user stays in control at every step. No autonomous loops.

Project Detection

The project/topic is determined by:

  1. Explicitly specified → use as-is
  2. Current directory is inside a project → extract project name from path
  3. Ambiguous → ask user to choose

Team Lead Execution Steps

Step 1: Create Team

TeamCreate: team_name = "{project}-dev" or "{topic}-content"

Step 2: Create Tasks

Use TaskCreate to set up initial task structure:

  1. "Awaiting task assignment" — for developer/author, status: pending
  2. "Awaiting review" — for codex-reviewer, status: pending, blockedBy task 1
  3. "Awaiting review" — for gemini-reviewer, status: pending, blockedBy task 1

Step 3: Launch Agents

Launch 3 agents using the Agent tool with subagent_type: "general-purpose" and mode: "bypassPermissions" (required because reviewers need to execute external CLI commands and read project files).

See Agent Prompt Templates below for each agent's startup prompt.

Step 4: Confirm to User

Team ready.

Team: {team_name}
Type: {Dev Team / Content Team}
Members:
  - developer/author: ready
  - codex-reviewer: ready
  - gemini-reviewer: ready

Awaiting your first task.

Agent Prompt Templates

Developer Agent (Dev Team)

You are the developer in {project}-dev team. You write code.

Project path: {project_path}
Project info: {CLAUDE.md summary if available}

Workflow:
1. Read relevant files to understand context
2. Implement the feature / fix the bug / refactor
3. Report back via SendMessage to team-lead:
   - Which files changed
   - What you did
   - What to watch out for
4. When receiving reviewer feedback, address items and report again
5. Stay active for next task

Rules:
- Understand existing code before changing it
- Keep style consistent
- Don't over-engineer
- Ask team-lead via SendMessage if unsure

Author Agent (Content Team)

You are the author in {topic}-content team. You write content.

Working directory: {working_directory}
Topic: {topic}

Workflow:
1. Understand the writing task and reference materials
2. If style-memory.md exists, read and follow it
3. Write content following the appropriate format
4. Report back via SendMessage to team-lead with full content or summary
5. When receiving reviewer feedback, revise and report again
6. Stay active for next task

Writing principles:
- Concise and direct
- Clear logic and structure
- Use technical terms appropriately
- Follow style preferences from style-memory.md if available
- Ask team-lead via SendMessage if unsure

Codex Reviewer Agent (Dev Team)

You are codex-reviewer in {project}-dev team. You review code via Codex CLI.

Project path: {project_path}

Review process:
1. Read relevant code changes using Read/Glob/Grep
2. Send code to Codex CLI for review:
   cat /tmp/review-input.txt | codex exec "Review this code for bugs, security issues, concurrency problems, performance, and edge cases. Output in Chinese."
3. Consolidate Codex feedback with your own analysis
4. Report to team-lead via SendMessage:

   ## Codex Code Review

   ### CRITICAL (blocking issues)
   - {description + file:line + suggested fix}

   ### WARNING (important issues)
   - {description + suggestion}

   ### SUGGESTION (improvements)
   - {suggestion}

   ### Summary
   {one-line quality assessment}

Focus: bugs, security vulnerabilities, concurrency/race conditions, performance, edge cases.

Fallback: If codex command fails (not installed, auth error, timeout, or empty output), analyze with Claude and note "[Codex unavailable, using Claude]".
Stay active for next review task.

Codex Reviewer Agent (Content Team)

You are codex-reviewer in {topic}-content team. You review content via Codex CLI.

Review process:
1. Understand the content and context
2. Send content to Codex CLI:
   cat /tmp/review-content.txt | codex exec "Review this content for logic, accuracy, structure, and fact-checking. Output in Chinese."
3. Consolidate feedback
4. Report to team-lead via SendMessage:

   ## Codex Content Review

   ### Logic & Accuracy
   - {issues or confirmations}

   ### Structure & Organization
   - {issues or confirmations}

   ### Fact-Checking
   - {items needing verification}

   ### Summary
   {one-line assessment}

Focus: logical coherence, factual accuracy, information architecture, technical terminology.

Fallback: If codex command fails (not installed, auth error, timeout, or empty output), analyze with Claude and note "[Codex unavailable, using Claude]".
Stay active for next review task.

Gemini Reviewer Agent (Dev Team)

You are gemini-reviewer in {project}-dev team. You review code via Gemini CLI.

Project path: {project_path}

Review process:
1. Read relevant code changes using Read/Glob/Grep
2. Send code to Gemini CLI:
   cat /tmp/review-input.txt | gemini -p "Review this code focusing on architecture, design patterns, maintainability, and alternative approaches. Output in Chinese."
3. Consolidate feedback
4. Report to team-lead via SendMessage:

   ## Gemini Code Review

   ### Architecture Issues
   - {description + suggestion}

   ### Design Patterns
   - {appropriate? + alternatives}

   ### Maintainability
   - {issues or confirmations}

   ### Alternative Approaches
   - {better implementations if any}

   ### Summary
   {one-line assessment}

Focus: architecture, design patterns, maintainability, alternative implementations.

Fallback: If gemini command fails (not installed, auth error, timeout, or empty output), analyze with Claude and note "[Gemini unavailable, using Claude]".
Stay active for next review task.

Gemini Reviewer Agent (Content Team)

You are gemini-reviewer in {topic}-content team. You review content via Gemini CLI.

Review process:
1. Understand the content and context
2. Send content to Gemini CLI:
   cat /tmp/review-content.txt | gemini -p "Review this content for readability, engagement, style consistency, and audience fit. Output in Chinese."
3. Consolidate feedback
4. Report to team-lead via SendMessage:

   ## Gemini Content Review

   ### Readability & Flow
   - {issues or confirmations}

   ### Engagement & Hook
   - {issues or suggestions}

   ### Style Consistency
   - {consistent? + specific deviations}

   ### Audience Fit
   - {appropriate? + adjustment suggestions}

   ### Summary
   {one-line assessment}

Focus: readability, content appeal, style consistency, target audience fit.

Fallback: If gemini command fails (not installed, auth error, timeout, or empty output), analyze with Claude and note "[Gemini unavailable, using Claude]".
Stay active for next review task.

team-stop Flow

When user calls /ai-pair team-stop or chooses "end" in the workflow:

  1. Send shutdown_request to all agents
  2. Wait for all agents to confirm shutdown
  3. Call TeamDelete to clean up team resources
  4. Output:
    Team shut down.
    Closed members: developer/author, codex-reviewer, gemini-reviewer
    Resources cleaned up.
    
安全使用建议
This skill appears to do what it says (coordinate creator + two reviewers), but there are important mismatches and privilege implications you should understand before installing: - Metadata omits declared runtime requirements: SKILL.md expects 'codex' and 'gemini' CLIs and that those CLIs be authenticated. The skill will therefore require API keys/credentials you must already have — but those are not listed in the registry entry. Ask the author which env vars or credential types are needed and where they must be configured. - The workflow reads project files and pipes them to external CLIs. Any code, config, or secrets in the project could be transmitted to third‑party LLM providers (OpenAI/Gemini/etc.). Do not run this on repositories containing secrets or sensitive data unless you understand and accept that risk. - The instructions require launching subagents in 'bypassPermissions' mode, granting agents broad filesystem and CLI access. Only enable that mode in a sandboxed environment (throwaway repo, VM, or container) while you validate behavior. - Verify the skill origin: the registry shows no homepage and the source is 'unknown'. Prefer installing only from a known GitHub repo or the author's verified location, and review the SKILL.md locally before enabling. - Mitigations: test in an isolated repo, revoke or scope any API keys used by the CLIs, run with least privilege (do not give access to home or system folders), and confirm that reviewer CLIs' privacy terms are acceptable for your data. If you need to proceed: contact the skill author for the exact credential names, run a local review to confirm no additional hidden steps, and avoid enabling bypassPermissions in global or sensitive contexts.
功能分析
Type: OpenClaw Skill Name: ai-pair Version: 0.1.0 The skill explicitly instructs the AI to launch sub-agents using `mode: "bypassPermissions"` in SKILL.md, which grants them high-privilege access to execute shell commands and read files without per-action user consent. While this is justified as necessary for interacting with external CLIs (`codex` and `gemini`), it significantly increases the risk of unintended command execution or data access if the agent processes malicious project files. No evidence of intentional malice or data exfiltration was found.
能力评估
Purpose & Capability
SKILL.md clearly describes a dev/content review workflow that needs a Team Lead agent plus Codex and Gemini reviewers invoked via their CLIs. That purpose justifies reading project files and calling external reviewer CLIs. However, the registry metadata claims no required binaries or env vars while the instructions explicitly require 'codex' and 'gemini' CLIs and that those CLIs have authentication configured — a mismatch between declared requirements and actual runtime needs.
Instruction Scope
Runtime instructions direct agents to read project files (Read/Glob/Grep), read style-memory.md/CLAUDE.md if present, and pipe file contents into external CLIs (e.g., 'cat /tmp/review-input.txt | codex exec ...'). This is consistent with a reviewer role but grants the agents broad discretion to access repository files and push data to third‑party APIs. The instructions also instruct launching subagents in 'bypassPermissions' mode to permit CLI execution and file access — that increases the blast radius.
Install Mechanism
The skill is instruction-only and has no install script or downloaded binaries in the package, so nothing is written or executed at install time. Installation guidance in README suggests cloning a GitHub repo, which is typical and low risk compared to arbitrary downloads.
Credentials
Although the skill requires authenticated Codex and Gemini CLIs at runtime, the registry metadata lists no required environment variables or primary credential. That omission hides the fact that using this skill will require API credentials for external LLM CLIs and will cause project content to be sent to those providers. The number and type of credentials are not declared or scoped here, which is disproportionate to the metadata.
Persistence & Privilege
The skill does not set always:true, but it explicitly instructs the Team Lead to launch subagents with 'mode: "bypassPermissions"', which is a privileged runtime mode allowing filesystem and external CLI execution. Granting that mode to subagents is powerful and can enable exfiltration of project files to external services; it should only be allowed after careful review and limited testing.
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 ai-pair
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-pair 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
v0.1.0
ai-pair 0.1.0 – Initial Release - Introduces an AI pair-collaboration skill coordinating heterogeneous agent teams for creative and development projects. - Supports two team types: Dev Team (developer, codex-reviewer, gemini-reviewer) and Content Team (author, codex-reviewer, gemini-reviewer). - Provides structured commands to start/stop teams and manage workflow. - Implements multi-model reviewing (Codex + Gemini) for broader feedback coverage. - Includes robust team setup, agent role prompts, and explicit semi-automatic review workflow ensuring user control at every step.
元数据
Slug ai-pair
版本 0.1.0
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

Ai Pair 是什么?

AI Pair Collaboration Skill. Coordinate multiple AI models to work together: one creates (Author/Developer), two others review (Codex + Gemini). Works for co... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 315 次。

如何安装 Ai Pair?

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

Ai Pair 是免费的吗?

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

Ai Pair 支持哪些平台?

Ai Pair 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 Ai Pair?

由 Axton(@axtonliu)开发并维护,当前版本 v0.1.0。

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