multi-ai — multi-ai pipeline multi-ai, llm-review, community, multi-ai pipeline, ide skills, Claude planner, Codex reviewer, bash script automation, pipeline orchestrator setup, task automation workflow, Claude Code

v1.0.0

About this Skill

Perfect for AI Agents needing advanced task automation and pipeline orchestration capabilities, particularly those leveraging Claude and Codex. multi-ai is a pipeline orchestrator that utilizes Claude and Codex to automate tasks and improve workflow efficiency.

Features

Removes old .task/ directory using rm -rf command
Creates initial plan using Claude planner
Captures user request and writes to .task/user-request.txt
Supports coding standards and review criteria via standards.md
Utilizes Codex reviewer for code review

# Core Topics

cskwork cskwork
[0]
[0]
Updated: 3/22/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 8/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Locale and body language aligned
Review Score
8/11
Quality Score
36
Canonical Locale
en
Detected Body Locale
en

Perfect for AI Agents needing advanced task automation and pipeline orchestration capabilities, particularly those leveraging Claude and Codex. multi-ai is a pipeline orchestrator that utilizes Claude and Codex to automate tasks and improve workflow efficiency.

Core Value

Empowers agents to automate complex workflows and manage tasks efficiently through a pipeline orchestrator, utilizing technical capabilities like Claude for natural language processing and Codex for code generation, and supporting protocols such as task automation APIs.

Ideal Agent Persona

Perfect for AI Agents needing advanced task automation and pipeline orchestration capabilities, particularly those leveraging Claude and Codex.

Capabilities Granted for multi-ai

Automating development workflows using Claude and Codex
Orchestrating tasks for efficient project management
Streamlining AI model training and deployment pipelines

! Prerequisites & Limits

  • Requires access to Claude and Codex APIs
  • Dependent on specific AI agent capabilities and compatibility
  • May require additional setup for custom workflow integration

Why this page is reference-only

  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

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 AI Agents needing advanced task automation and pipeline orchestration capabilities, particularly those leveraging Claude and Codex. multi-ai is a pipeline orchestrator that utilizes Claude and Codex to automate tasks and improve workflow efficiency.

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: Automating development workflows using Claude and Codex, Orchestrating tasks for efficient project management, Streamlining AI model training and deployment pipelines.

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 Claude and Codex APIs. Dependent on specific AI agent capabilities and compatibility. May require additional setup for custom workflow integration.

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.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

multi-ai

Discover how to implement the multi-ai pipeline with Claude and Codex. Learn to automate tasks and improve your workflow with this step-by-step guide.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

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

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