Awesome-omni-skill multi-ai

Start the multi-AI pipeline with a given request. Guides through plan -> review -> implement -> review workflow.

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/multi-ai-cskwork" ~/.claude/skills/diegosouzapw-awesome-omni-skill-multi-ai-212df6 && rm -rf "$T"

manifest: skills/data-ai/multi-ai-cskwork/SKILL.md

source content

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:

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

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