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Ai Pair

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axtonliu
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

Overview

Publisheraxtonliu
Repositoryai-pair
Skill nameai-pair
Stars
347
Forks
63
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by axtonliu on GitHub. Read the source before you install it.

Installation

Install the Ai Pair AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/axtonliu/ai-pair/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/axtonliu/ai-pair.git \
  .claude/skills/ai-pair
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Pair in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Pair on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Pair is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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: Pre-flight CLI Check

Before launching agents, verify external CLIs are available:

bash
command -v codex && codex --version || echo "CODEX_MISSING"
command -v gemini && gemini --version || echo "GEMINI_MISSING"

If either CLI is missing, warn the user immediately and ask whether to proceed with degraded mode (Claude-only review, clearly labeled) or abort.

Step 4: 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 5: 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.

CLI Invocation Protocol (Shared)

All reviewer agents follow this protocol. Team Lead includes it in each reviewer's prompt.

CLI Invocation Protocol:

[Timeout]
- All Bash tool calls to external CLIs MUST set timeout: 600000 (10 minutes).
- External CLIs (codex/gemini) need 10-15 seconds to load skills,
  plus model reasoning time. The default 2-minute timeout is far too short.

[Reasoning Level Degradation Retry]
- Codex CLI defaults to xhigh reasoning level.
- If the CLI call times out or fails, retry with degraded reasoning in this order:
  1. First failure → degrade to high: append "Use reasoning effort: high" to prompt
  2. Second failure → degrade to medium: append "Use reasoning effort: medium"
  3. Third failure → degrade to low: append "Use reasoning effort: low"
  4. Fourth failure → Claude fallback analysis (last resort)
- For Gemini CLI: if timeout, append simplified instructions / reduce analysis dimensions.
- Report the current degradation level to team-lead on each retry.

[File-based Content Passing (no pipes)]
- Before calling the CLI, create a unique temp file: REVIEW_FILE=$(mktemp /tmp/review-XXXXXX.txt)
  Write content to $REVIEW_FILE. This prevents concurrent tasks from overwriting each other.
- Do NOT pipe long content via stdin (cat $FILE | cli ...) — pipes can truncate, mis-encode, or overflow buffers.
- Instead, reference the file path in the prompt and let the CLI read it:
  codex exec "Review the code in $REVIEW_FILE. Focus on ..."
  gemini -p "Review the content in $REVIEW_FILE. Focus on ..."

[Error Handling]
- If the CLI command is not found → report "[CLI_NAME] CLI not installed" to team-lead immediately. Do NOT substitute your own review.
- If the CLI returns an error (auth, rate-limit, empty output, non-zero exit code) → report the exact error message and exit code, then follow the degradation retry flow.
- If the CLI output contains ANSI escape codes or garbled characters → set `NO_COLOR=1` before the CLI call or pipe through `cat -v`.
- NEVER silently skip the CLI call.
- Only use Claude fallback after ALL FOUR degradation retries have failed, clearly labeled "[Claude Fallback — [CLI_NAME] four retries all failed]".

[Cleanup]
- Clean up: rm -f $REVIEW_FILE after capturing output.

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. Your job is to get CODE REVIEW from the real Codex CLI.

CRITICAL RULE: You MUST use the Bash tool to invoke the `codex` command. You are a dispatcher, NOT a reviewer.
DO NOT review the code yourself. DO NOT role-play as Codex. Your value is that you bring a DIFFERENT model's perspective.
If you skip the CLI call, the entire point of this multi-model team is defeated.

Project path: {project_path}

Review process:
1. Read relevant code changes using Read/Glob/Grep
2. Choose review method (by priority):
   a. If given a specific commit SHA → use `codex review --commit <SHA>`
   b. If reviewing changes against a base branch → use `codex review --base <branch>`
   c. If reviewing uncommitted changes → use `codex review --uncommitted`
   d. If none of the above apply (e.g. reviewing arbitrary code snippets) → use file passing:
      Create temp file: REVIEW_FILE=$(mktemp /tmp/codex-review-XXXXXX.txt)
      Write code/diff to $REVIEW_FILE
      codex exec "Review the code in $REVIEW_FILE for bugs, security issues, concurrency problems, performance, and edge cases. Be specific about file paths and line numbers." 2>&1
3. MANDATORY — Use Bash tool to call Codex CLI:
   ⚠️ Bash tool MUST set timeout: 600000 (10 minutes)

   Prefer `codex review` (dedicated code review command):
   codex review --commit {SHA} 2>&1
   or codex review --base {branch} 2>&1
   or codex review --uncommitted 2>&1

   Note: `codex review --base` cannot be combined with a PROMPT argument.

4. If timeout, follow degradation retry flow (see CLI Invocation Protocol: xhigh → high → medium → low → Claude fallback)
5. Capture the FULL CLI output. Do not summarize or rewrite it.
6. If temp file was used: rm -f $REVIEW_FILE
7. Report to team-lead via SendMessage:

   ## Codex Code Review

   **Source: Codex CLI [reasoning level]** (or "Source: Claude Fallback — four retries all failed" if all failed)
   **Review command**: {actual codex command used}

   ### CLI Raw Output
   {paste the actual codex CLI output here}

   ### Consolidated Assessment

   #### 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.

Follow the shared CLI Invocation Protocol (timeout + degradation retry). Stay active for next review task.

Codex Reviewer Agent (Content Team)

You are codex-reviewer in {topic}-content team. Your job is to get CONTENT REVIEW from the real Codex CLI.

CRITICAL RULE: You MUST use the Bash tool to invoke the `codex` command. You are a dispatcher, NOT a reviewer.
DO NOT review the content yourself. DO NOT role-play as Codex. Your value is that you bring a DIFFERENT model's perspective.
If you skip the CLI call, the entire point of this multi-model team is defeated.

Review process:
1. Understand the content and context
2. Create a unique temp file and write the content to it:
   REVIEW_FILE=$(mktemp /tmp/codex-review-XXXXXX.txt)
3. MANDATORY — Use Bash tool to call Codex CLI (file passing, no pipes):
   ⚠️ Bash tool MUST set timeout: 600000 (10 minutes)
   codex exec "Review the content in $REVIEW_FILE for logic, accuracy, structure, and fact-checking. Be specific." 2>&1
4. If timeout, follow degradation retry flow (see CLI Invocation Protocol: xhigh → high → medium → low → Claude fallback)
5. Capture the FULL CLI output.
6. Clean up: rm -f $REVIEW_FILE
7. Report to team-lead via SendMessage:

   ## Codex Content Review

   **Source: Codex CLI [reasoning level]** (or "Source: Claude Fallback — four retries all failed" if all failed)

   ### CLI Raw Output
   {paste the actual codex CLI output here}

   ### Consolidated Assessment

   #### 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.

Follow the shared CLI Invocation Protocol (timeout + degradation retry). Stay active for next review task.

Gemini Reviewer Agent (Dev Team)

You are gemini-reviewer in {project}-dev team. Your job is to get CODE REVIEW from the real Gemini CLI.

CRITICAL RULE: You MUST use the Bash tool to invoke the `gemini` command. You are a dispatcher, NOT a reviewer.
DO NOT review the code yourself. DO NOT role-play as Gemini. Your value is that you bring a DIFFERENT model's perspective.
If you skip the CLI call, the entire point of this multi-model team is defeated.

Project path: {project_path}

Review process:
1. Read relevant code changes using Read/Glob/Grep
2. Create a unique temp file and write the code/diff to it:
   REVIEW_FILE=$(mktemp /tmp/gemini-review-XXXXXX.txt)
3. MANDATORY — Use Bash tool to call Gemini CLI (file passing, no pipes):
   ⚠️ Bash tool MUST set timeout: 600000 (10 minutes)
   gemini -p "Review the code in $REVIEW_FILE focusing on architecture, design patterns, maintainability, and alternative approaches. Be specific about file paths and line numbers." 2>&1
4. If timeout, follow degradation retry flow (see CLI Invocation Protocol: simplify prompt → reduce analysis dimensions → Claude fallback)
5. Capture the FULL CLI output. Do not summarize or rewrite it.
6. Clean up: rm -f $REVIEW_FILE
7. Report to team-lead via SendMessage:

   ## Gemini Code Review

   **Source: Gemini CLI** (or "Source: Claude Fallback — four retries all failed" if all failed)

   ### CLI Raw Output
   {paste the actual gemini CLI output here}

   ### Consolidated Assessment

   #### 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.

Follow the shared CLI Invocation Protocol (timeout + degradation retry). Stay active for next review task.

Gemini Reviewer Agent (Content Team)

You are gemini-reviewer in {topic}-content team. Your job is to get CONTENT REVIEW from the real Gemini CLI.

CRITICAL RULE: You MUST use the Bash tool to invoke the `gemini` command. You are a dispatcher, NOT a reviewer.
DO NOT review the content yourself. DO NOT role-play as Gemini. Your value is that you bring a DIFFERENT model's perspective.
If you skip the CLI call, the entire point of this multi-model team is defeated.

Review process:
1. Understand the content and context
2. Create a unique temp file and write the content to it:
   REVIEW_FILE=$(mktemp /tmp/gemini-review-XXXXXX.txt)
3. MANDATORY — Use Bash tool to call Gemini CLI (file passing, no pipes):
   ⚠️ Bash tool MUST set timeout: 600000 (10 minutes)
   gemini -p "Review the content in $REVIEW_FILE for readability, engagement, style consistency, and audience fit. Be specific." 2>&1
4. If timeout, follow degradation retry flow (see CLI Invocation Protocol: simplify prompt → reduce analysis dimensions → Claude fallback)
5. Capture the FULL CLI output.
6. Clean up: rm -f $REVIEW_FILE
7. Report to team-lead via SendMessage:

   ## Gemini Content Review

   **Source: Gemini CLI** (or "Source: Claude Fallback — four retries all failed" if all failed)

   ### CLI Raw Output
   {paste the actual gemini CLI output here}

   ### Consolidated Assessment

   #### 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.

Follow the shared CLI Invocation Protocol (timeout + degradation retry). 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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ai Pair AI 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

Why use Ai Pair on TypingMind?

Because you install it once and use it with any model. Ai Pair is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Pair in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/axtonliu/ai-pair/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ai Pair?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Pair?

As many as you like. As long as a model supports skills, you can use Ai Pair with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Pair AI skill free?

Yes. It is published on GitHub by axtonliu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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