Agent skill

AI Pair

by axtonliu in axtonliu/ai-pair

AI Pair Collaboration Skill. An agent skill from axtonliu/ai-pair.

MITAuto-check passedMedia & Creative

Install AI Pair

skills CLI
$ npx skills add axtonliu/ai-pair --skill ai-pair -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install axtonliu/ai-pair ai-pair --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-pair
GitHub stars
345
Token cost
~4.2k tokens
SKILL.md length
416 words
Files
6
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

AI Pair Collaboration Skill. An agent skill from axtonliu/ai-pair.

  • Works in 5 steps: Create Team → Create Tasks → Pre-flight CLI Check → …
  • Tasks that involve Video scripts and shorts
  • SKILL.md covers Why Multiple AI Reviewers?, Commands, Prerequisites and Team Architecture, plus 6 more sections
  • Calls codex and gemini

What it does

AI Pair is an agent skill from 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

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files (for example `README.md`, `examples/content-team.md` and `examples/dev-team.md`).

It sits in Media & Creative, covering Video scripts and shorts. The repository describes itself as: Coordinate multiple AI models (Claude + GPT + Gemini) as a heterogeneous team. One creates, two review. A Claude Code Skill. The licence is MIT.

When your agent uses it

  • Tasks that involve Video scripts and shorts

Example prompts

  • “/ai-pair”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Create Team
  2. Create Tasks
  3. Pre-flight CLI Check
  4. Launch Agents
  5. Confirm to User

What it can do on your machine

Read from SKILL.md and the folder at commit 60961fa. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • codex
    • gemini

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Pair loads about 4.2k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 416 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from axtonliu/ai-pair at commit 60961fa, republished under its MIT licence (© axtonliu). 416 words, ~4,195 tokens.

Download SKILL.mdSave it as .claude/skills/ai-pair/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
ai-pair
description
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
metadata.version
1.5.0

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
Show full SKILL.md (178 more words)Show less
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.

© axtonliu, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files in the repository root of axtonliu/ai-pair.

  • SKILL.md
  • .gitignore
  • LICENSE
  • README.md
  • examples/content-team.md
  • examples/dev-team.md

Open the folder on GitHubat commit 60961fa

Compare with similar skills

AI Pair next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Pair compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Pair this skillaxtonliu/ai-pair345—~4.2kAutomated safety check: PassMIT
Weekly Changelog Videoheygen-com/hyperframes59k—~3.3kAutomated safety check: PassApache-2.0
Viral Captions And Ctasvyralcontent/content-skills1331 repos~2.8kAutomated safety check: PassMIT
Video Scriptitwanger/toBeBetterJavaer18k—~2.2kAutomated safety check: PassNone
AI Design Teamjinggreen15/ai-design-team194—~614Automated safety check: PassNone
Shortform Idea Grillericosiu/ai-marketing-skills3.6k—~1.2kAutomated safety check: PassMIT

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Questions about AI Pair

What does AI Pair do?

AI Pair Collaboration Skill. An agent skill from axtonliu/ai-pair. AI Pair is an agent skill from axtonliu/ai-pair. AI Pair Collaboration Skill.

When should I use AI Pair?

AI Pair fits situations like: tasks that involve Video scripts and shorts.

How do I install AI Pair in Claude Code?

Run `npx skills add axtonliu/ai-pair --skill ai-pair -a claude-code`. Or copy the skill folder (the axtonliu/ai-pair repository) into .claude/skills/ai-pair in your project. Claude Code loads it when a task matches its description.

How do I install AI Pair in Codex?

Run `npx skills add axtonliu/ai-pair --skill ai-pair -a codex`. Or copy the skill folder (the axtonliu/ai-pair repository) into .agents/skills/ai-pair in your project. Codex loads it when a task matches its description.

Can I use AI Pair in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add axtonliu/ai-pair --skill ai-pair -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-pair, .gemini/skills/ai-pair, .github/skills/ai-pair and .opencode/skills/ai-pair in your project.

What does AI Pair need to run?

Going by SKILL.md and its folder, AI Pair needs the command-line tools its instructions call (codex and gemini).

Does AI Pair access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Pair safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Pair use?

AI Pair is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Pair use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Pair?

Skills that share tags, products or a category with AI Pair: Weekly Changelog Video (heygen-com/hyperframes, 59k stars), Viral Captions And Ctas (vyralcontent/content-skills, 133 stars), Video Script (itwanger/toBeBetterJavaer, 18k stars) and AI Design Team (jinggreen15/ai-design-team, 194 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Pair?

axtonliu (a GitHub user) maintains it in axtonliu/ai-pair, which has 345 GitHub stars. The repository was last updated on March 22, 2026.

Source: axtonliu/ai-pair on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.