AI pair programming

Orchestrates a dual-AI engineering loop where a Primary AI plans and implements, while a Review AI validates and reviews, with continuous feedback for optimal code quality. Supports custom AI pairing (Claude, Codex, Gemini, etc.)

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    nameai-pair-programming
    descriptionOrchestrates a dual-AI engineering loop where a Primary AI plans and implements, while a Review AI validates and reviews, with continuous feedback for optimal code quality. Supports custom AI pairing (Claude, Codex, Gemini, etc.)

    AI Pair Programming Skill

    Overview

    A flexible pair programming workflow that allows you to combine any two AI assistants for collaborative development. Configure your preferred AI pair based on their strengths.

    AI Configuration

    Before using this skill, configure your AI pair by setting the following roles:

    RoleDescriptionExamples
    Primary AIHandles architecture, planning, and code implementationClaude, Gemini, GPT-4
    Review AIValidates plans and reviews code for quality assuranceCodex, Claude, Gemini

    Example Configurations

    ConfigurationPrimary AIReview AIUse Case
    Claude + CodexClaudeCodexStrong planning + Fast validation
    Claude + GeminiClaudeGeminiMulti-model perspective
    Gemini + ClaudeGeminiClaudeLeverage Gemini's context + Claude's review
    GPT-4 + CodexGPT-4CodexGPT planning + Codex validation

    Setting Up Your AI Pair

    Replace {{PRIMARY_AI}} and {{REVIEW_AI}} placeholders throughout this skill with your chosen AI names.

    Example:

    • {{PRIMARY_AI}}Claude
    • {{REVIEW_AI}}Codex

    For command-line tools, replace {{REVIEW_AI_CLI}} with the appropriate CLI command:

    • Codex: codex exec --sandbox read-only
    • Claude: claude --print
    • Gemini: gemini
    • Other: Use your AI's CLI tool

    Note: CLI commands are examples. Please verify the correct CLI syntax for your specific AI tools and their current versions.

    Core Workflow Philosophy

    This skill implements a balanced engineering loop:

    • {{PRIMARY_AI}}: Architecture, planning, and execution
    • {{REVIEW_AI}}: Validation and code review
    • Continuous Review: Each AI reviews the other's work
    • Context Handoff: Always continue with whoever last cleaned up

    Note: Model + reasoning settings are assumed to be handled by each AI's existing config. Commands below use generic placeholders.

    Phase 1: Planning with {{PRIMARY_AI}}

    1. Start by creating a detailed plan for the task
    2. Break down the implementation into clear steps
    3. Document assumptions and potential issues
    4. Output the plan in a structured format

    Phase 2: Plan Validation with {{REVIEW_AI}}

    1. Send the plan to {{REVIEW_AI}} for validation:

    Check for:

    • Logic errors
    • Missing edge cases
    • Architecture flaws
    • Security concerns" | {{REVIEW_AI_CLI}}">
      echo "Review this implementation plan and identify any issues:
      [{{PRIMARY_AI}}'s plan here]
      
      Check for:
      - Logic errors
      - Missing edge cases
      - Architecture flaws
      - Security concerns" | {{REVIEW_AI_CLI}}
    1. Capture {{REVIEW_AI}}'s feedback

    Phase 3: Feedback Loop

    If {{REVIEW_AI}} finds issues:

    1. Summarize {{REVIEW_AI}}'s concerns to the user
    2. Refine the plan based on feedback
    3. Ask user (via AskUserQuestion): "Should I revise the plan and re-validate, or proceed with fixes?"
    4. Repeat Phase 2 if needed

    Phase 4: Execution

    Once the plan is validated:

    1. {{PRIMARY_AI}} implements the code using available tools (Edit, Write, Read, etc.)
    2. Break down implementation into manageable steps
    3. Execute each step carefully with proper error handling
    4. Document what was implemented

    Phase 5: Cross-Review After Changes

    After every change:

    1. Send {{PRIMARY_AI}}'s implementation to {{REVIEW_AI}} for review:
      • Bug detection
      • Performance issues
      • Best practices validation
      • Security vulnerabilities
    2. {{PRIMARY_AI}} analyzes {{REVIEW_AI}}'s feedback and decides:
      • Apply fixes immediately if issues are critical
      • Discuss with user if architectural changes needed
      • Document decisions made

    Phase 6: Iterative Improvement

    1. After {{REVIEW_AI}} review, {{PRIMARY_AI}} applies necessary fixes
    2. For significant changes, send back to {{REVIEW_AI}} for re-validation
    3. Continue the loop until code quality standards are met
    4. For AI tools that support session continuation, use the resume flag:
    echo "Review the updated implementation" | {{REVIEW_AI_CLI}} --resume

    Note: Session resumption support varies by AI tool. For tools without resume support, start a new session with full context.

    Recovery When Issues Are Found

    When {{REVIEW_AI}} identifies problems:

    1. {{PRIMARY_AI}} analyzes the root cause
    2. Implements fixes using available tools
    3. Sends updated code back to {{REVIEW_AI}} for verification
    4. Repeats until validation passes

    When implementation errors occur:

    1. {{PRIMARY_AI}} reviews the error/issue
    2. Adjusts implementation strategy
    3. Re-validates with {{REVIEW_AI}} before proceeding

    Best Practices

    • Always validate plans before execution
    • Never skip cross-review after changes
    • Maintain clear handoff between AIs
    • Document who did what for context
    • Use resume to preserve session state (check your AI tool's documentation for support)

    Command Reference

    PhaseCommand PatternPurpose
    Validate planecho "plan" | {{REVIEW_AI_CLI}}Check logic before coding
    Implement{{PRIMARY_AI}} uses Edit/Write/Read tools{{PRIMARY_AI}} implements the validated plan
    Review codeecho "review changes" | {{REVIEW_AI_CLI}}{{REVIEW_AI}} validates {{PRIMARY_AI}}'s implementation
    Continue reviewecho "next step" | {{REVIEW_AI_CLI}} --resumeContinue validation session (if supported)
    Apply fixes{{PRIMARY_AI}} uses Edit/Write tools{{PRIMARY_AI}} fixes issues found by {{REVIEW_AI}}
    Re-validateecho "verify fixes" | {{REVIEW_AI_CLI}}{{REVIEW_AI}} re-checks after fixes

    Note: The --resume flag is supported by some AI tools (e.g., Codex). For tools without resume support, start a new session with the necessary context.

    Error Handling

    1. Stop on non-zero exit codes from {{REVIEW_AI}}
    2. Summarize {{REVIEW_AI}} feedback and ask for direction via AskUserQuestion
    3. Before implementing changes, confirm approach with user if:
      • Significant architectural changes needed
      • Multiple files will be affected
      • Breaking changes are required
    4. When {{REVIEW_AI}} warnings appear, {{PRIMARY_AI}} evaluates severity and decides next steps

    The Perfect Loop

    Plan ({{PRIMARY_AI}}) → Validate Plan ({{REVIEW_AI}}) → Feedback →
    Implement ({{PRIMARY_AI}}) → Review Code ({{REVIEW_AI}}) →
    Fix Issues ({{PRIMARY_AI}}) → Re-validate ({{REVIEW_AI}}) → Repeat until perfect
    

    This creates a self-correcting, high-quality engineering system where:

    • {{PRIMARY_AI}} handles all code implementation and modifications
    • {{REVIEW_AI}} provides validation, review, and quality assurance

    Quick Start Examples

    Example 1: Claude + Codex (Original Configuration)

    PRIMARY_AI = Claude
    REVIEW_AI = Codex
    REVIEW_AI_CLI = codex exec --sandbox read-only
    

    Example 2: Claude + Gemini

    PRIMARY_AI = Claude
    REVIEW_AI = Gemini
    REVIEW_AI_CLI = gemini
    

    Example 3: Gemini + Claude

    PRIMARY_AI = Gemini
    REVIEW_AI = Claude
    REVIEW_AI_CLI = claude --print
    
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