ai-pair
AI Pair Collaboration Skill. Coordinate multiple AI models to work together: one creates (Author/Developer), two others review (Codex + Gemini). Works for code, articles, video scripts, and any creative task. Trigger: /ai-pair, ai pair, dev-team, content-team, team-stop
Best use case
ai-pair is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
AI Pair Collaboration Skill. Coordinate multiple AI models to work together: one creates (Author/Developer), two others review (Codex + Gemini). Works for code, articles, video scripts, and any creative task. Trigger: /ai-pair, ai pair, dev-team, content-team, team-stop
Teams using ai-pair should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-pair/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-pair Compares
| Feature / Agent | ai-pair | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
AI Pair Collaboration Skill. Coordinate multiple AI models to work together: one creates (Author/Developer), two others review (Codex + Gemini). Works for code, articles, video scripts, and any creative task. Trigger: /ai-pair, ai pair, dev-team, content-team, team-stop
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Cursor vs Codex for AI Workflows
Compare Cursor and Codex for AI coding workflows, repository assistance, debugging, refactoring, and reusable developer skills.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
AI Agent for YouTube Script Writing
Find AI agent skills for YouTube script writing, video research, content outlining, and repeatable channel production workflows.
SKILL.md Source
# 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
```bash
/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:
```bash
/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.
```Related Skills
Auto Repair Shop Operations
Complete operational reference for independent auto repair shops. Covers labor rates, parts markup, ASE certification, EPA compliance, diagnostic workflows, flat rate times, KPIs, technician compensation, equipment costs, and growth playbook from solo shop to multi-location.
agent-self-repair
General AI agent introspection debugging framework: auto capture errors, root cause analysis, automatic repair, fix verification, no manual intervention required
search-analyst-pair
Turn any research request into a structured, reviewable brief — fact collection, risk analysis, and recommendation in three deterministic steps.
---
name: article-factory-wechat
humanizer
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
find-skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
tavily-search
Use Tavily API for real-time web search and content extraction. Use when: user needs real-time web search results, research, or current information from the web. Requires Tavily API key.
baidu-search
Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics.
agent-autonomy-kit
Stop waiting for prompts. Keep working.
Meeting Prep
Never walk into a meeting unprepared again. Your agent researches all attendees before calendar events—pulling LinkedIn profiles, recent company news, mutual connections, and conversation starters. Generates a briefing doc with talking points, icebreakers, and context so you show up informed and confident. Triggered automatically before meetings or on-demand. Configure research depth, advance timing, and output format. Walking into meetings blind is amateur hour—missed connections, generic small talk, zero leverage. Use when setting up meeting intelligence, researching specific attendees, generating pre-meeting briefs, or automating your prep workflow.
self-improvement
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
botlearn-healthcheck
botlearn-healthcheck — BotLearn autonomous health inspector for OpenClaw instances across 5 domains (hardware, config, security, skills, autonomy); triggers on system check, health report, diagnostics, or scheduled heartbeat inspection.