multi-ai — community multi-ai, llm-review, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Advanced Pipeline Agents needing multi-AI workflow management capabilities. Claude (planner + coder) and codex (reviewer)

cskwork cskwork
[0]
[0]
Updated: 3/5/2026

Agent Capability Analysis

The multi-ai skill by cskwork is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Perfect for Advanced Pipeline Agents needing multi-AI workflow management capabilities.

Core Value

Empowers agents to manage complex workflows by integrating multiple AI services like Claude for planning and coding, and Codex for review, utilizing standards for coding and review criteria, and handling tasks through a structured pipeline.

Capabilities Granted for multi-ai

Orchestrating multi-AI workflows for task automation
Managing pipelines that require both planning and coding capabilities
Integrating Codex for reviewing and validating AI-generated content

! Prerequisites & Limits

  • Requires access to multiple AI services (e.g., Claude, Codex)
  • Needs a structured standards document for coding and review criteria
  • May require specific directory and file management (e.g., `.task/` directory)
Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

multi-ai

Install multi-ai, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

Multi-AI Pipeline Orchestrator

You are starting the multi-AI pipeline. Follow this process exactly.

Reference Documents

First, read the standards that guide all reviews:

  • skill/multi-ai/reference/standards.md - Coding standards and review criteria

Step 1: Clean Up Previous Task

Remove old .task/ directory if it exists:

bash
1rm -rf .task 2mkdir -p .task

Step 2: Capture User Request

Write the user's request to .task/user-request.txt.

Step 3: Create Initial Plan

Write .task/plan.json:

json
1{ 2 "id": "plan-YYYYMMDD-HHMMSS", 3 "title": "Short descriptive title", 4 "description": "What the user wants to achieve", 5 "requirements": ["req1", "req2"], 6 "created_at": "ISO8601", 7 "created_by": "claude" 8}

Step 4: Refine Plan

Research the codebase and create .task/plan-refined.json:

json
1{ 2 "id": "plan-001", 3 "title": "Feature title", 4 "description": "What the user wants", 5 "requirements": ["req1", "req2"], 6 "technical_approach": "Detailed how-to", 7 "files_to_modify": ["path/to/file.ts"], 8 "files_to_create": ["path/to/new.ts"], 9 "dependencies": [], 10 "estimated_complexity": "low|medium|high", 11 "potential_challenges": ["Challenge and mitigation"], 12 "refined_by": "claude", 13 "refined_at": "ISO8601" 14}

Step 5: Sequential Plan Reviews

Run reviews in sequence. Fix issues after each before continuing:

  1. Invoke /review-sonnet

    • Read .task/review-sonnet.json result
    • If needs_changes: fix issues in plan, update .task/plan-refined.json
  2. Invoke /review-codex

    • Read .task/review-codex.json result
    • If needs_changes: fix issues and restart from step 5.1
    • If approved: continue to implementation

Step 6: Implement

Invoke /implement-sonnet

This skill will:

  • Read the approved plan from .task/plan-refined.json
  • Implement the code
  • Add tests
  • Output to .task/impl-result.json

Step 7: Sequential Code Reviews

Run reviews in sequence. Fix issues after each before continuing:

  1. Invoke /review-sonnet

    • Read .task/review-sonnet.json result
    • If needs_changes: fix code issues
  2. Invoke /review-codex

    • Read .task/review-codex.json result
    • If needs_changes: fix issues and restart from step 7.1
    • If approved: continue to completion

Step 8: Complete

Write .task/state.json:

json
1{ 2 "state": "complete", 3 "plan_id": "plan-001", 4 "completed_at": "ISO8601" 5}

Report success to the user with:

  • Summary of what was implemented
  • Files changed
  • Tests added

Important Rules

  • Follow this process exactly - no shortcuts
  • Fix ALL issues raised by reviewers before continuing
  • If codex rejects, restart the review cycle from sonnet
  • Keep the user informed of progress at each major step

State Files Reference

FilePurpose
.task/user-request.txtOriginal user request
.task/plan.jsonInitial plan
.task/plan-refined.jsonRefined plan with technical details
.task/impl-result.jsonImplementation result
.task/review-sonnet.jsonSonnet review output
.task/review-codex.jsonCodex review output
.task/state.jsonPipeline state

Reference Directory

PathPurpose
skill/multi-ai/reference/standards.mdReview criteria and coding standards
skill/multi-ai/reference/schemas/JSON schemas for structured output

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is multi-ai?

Perfect for Advanced Pipeline Agents needing multi-AI workflow management capabilities. Claude (planner + coder) and codex (reviewer)

How do I install multi-ai?

Run the command: npx killer-skills add cskwork/llm-review/multi-ai. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for multi-ai?

Key use cases include: Orchestrating multi-AI workflows for task automation, Managing pipelines that require both planning and coding capabilities, Integrating Codex for reviewing and validating AI-generated content.

Which IDEs are compatible with multi-ai?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for multi-ai?

Requires access to multiple AI services (e.g., Claude, Codex). Needs a structured standards document for coding and review criteria. May require specific directory and file management (e.g., `.task/` directory).

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add cskwork/llm-review/multi-ai. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use multi-ai immediately in the current project.

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