Multi-AI Pipeline Orchestrator

VerifiedCaution

Orchestrates a multi-AI pipeline with plan, review, implement, and review steps. Guides through creating a plan, refining it based on codebase research, running sequential reviews (Sonnet and Codex), implementing code, and conducting final reviews. Helps ensure code quality and approval before completion.

Sby Skills Guide Bot
DevelopmentIntermediate
6/2/2026
Claude CodeCodex
#multi-ai-pipeline#code-review#ai-orchestration#workflow-automation#plan-implement-review

Recommended for

Automated summary

This skill orchestrates a multi-AI pipeline that guides the user through a structured process of planning, sequential reviews, implementation, and code reviews using different AI models.

Strengths

  • Enforces a disciplined workflow with separate planning, implementation, and review stages.
  • Leverages multiple AI models (Sonnet, Codex) to improve quality and catch issues early.
  • Automates state management and sequential review cycles to ensure thoroughness.
  • Provides clear traceability via state files and outputs at each step.

Limitations

  • Requires the presence of specific skills (`/review-sonnet`, `/review-codex`, `/implement-sonnet`) to function fully.
  • May introduce overhead for simple tasks that could be done faster without multi-step reviews.
  • The rigid pipeline may not fit all workflows or team preferences.
When to use it

Use this skill when you need a reliable, multi-model review process for complex code changes that demand high quality and careful validation.

When not to use it

Avoid this skill for trivial or exploratory tasks where speed is more important than rigorous review, or when the required sub-skills are not available.

Security analysis

Caution
Quality score88/100

The skill includes a bash command to recursively force-remove the .task directory to clean up prior runs. While targeted, this could be destructive if the user has placed valuable files in that directory. No other risky patterns (no curl|sh, no exfiltration, no obfuscation) are present.

Findings
  • •Uses rm -rf to delete .task/ directory, which could result in data loss if the user has important files there unexpectedly.
How this analysis is made

Examples

New feature with multi-AI pipeline
I want to add a new user authentication feature using JWT. Run the multi-AI pipeline to plan, review, implement, and review it.
Refactor a module with quality reviews
Refactor the payment processing module to use a strategy pattern. Use the multi-AI pipeline to ensure thorough reviews and implementation.
Fix a bug with change validation
Fix the race condition in the order submission handler. Follow the multi-AI pipeline to plan and implement the fix with reviews.

name: multi-ai description: Start the multi-AI pipeline with a given request. Guides through plan -> review -> implement -> review workflow. allowed-tools: Read, Write, Edit, Bash, Glob, Grep

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:

rm -rf .task
mkdir -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:

{
  "id": "plan-YYYYMMDD-HHMMSS",
  "title": "Short descriptive title",
  "description": "What the user wants to achieve",
  "requirements": ["req1", "req2"],
  "created_at": "ISO8601",
  "created_by": "claude"
}

Step 4: Refine Plan

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

{
  "id": "plan-001",
  "title": "Feature title",
  "description": "What the user wants",
  "requirements": ["req1", "req2"],
  "technical_approach": "Detailed how-to",
  "files_to_modify": ["path/to/file.ts"],
  "files_to_create": ["path/to/new.ts"],
  "dependencies": [],
  "estimated_complexity": "low|medium|high",
  "potential_challenges": ["Challenge and mitigation"],
  "refined_by": "claude",
  "refined_at": "ISO8601"
}

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:

{
  "state": "complete",
  "plan_id": "plan-001",
  "completed_at": "ISO8601"
}

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

| File | Purpose | |------|---------| | .task/user-request.txt | Original user request | | .task/plan.json | Initial plan | | .task/plan-refined.json | Refined plan with technical details | | .task/impl-result.json | Implementation result | | .task/review-sonnet.json | Sonnet review output | | .task/review-codex.json | Codex review output | | .task/state.json | Pipeline state |

Reference Directory

| Path | Purpose | |------|---------| | skill/multi-ai/reference/standards.md | Review criteria and coding standards | | skill/multi-ai/reference/schemas/ | JSON schemas for structured output |

From the file to a first result

The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.

View the source and supporting files

This download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.

1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/multi-ai-pipeline-orchestrator-mm5t89z8/download' --output '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md.part' && mv '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md.part' '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

Tools declared by the author (check availability in your agent): Read, Write, Edit, Bash, Glob, Grep

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md' && test -s '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/multi-ai' && cp -R '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8' '.claude/skills/multi-ai'

Codex
test -f '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md' && test -s '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md' && mkdir -p '.agents/skills' && test ! -e '.agents/skills/multi-ai' && cp -R '.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8' '.agents/skills/multi-ai'

OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation
openskills install './.skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8'

openskills read 'multi-ai-pipeline-orchestrator-mm5t89z8'

Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.

For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.

openskills sync

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/multi-ai-pipeline-orchestrator-mm5t89z8/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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