본문으로 건너뛰기
cccskills
무료GitHub에 공개

swarm-orchestrator

Use when the user types [SWARM] to activate a multi-agent parallel workflow. Dispatches specialized subagents to work on independent tasks simultaneously, with progress tracking and result aggregation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ProjectZeroDays/FreeAI_AI_Inference_Workstation스타 5(GitHub 5 + cccskills 0)

실행 점수 미검증

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

[SWARM] Orchestrator

Activate a fleet of specialized agents to execute tasks in parallel. This skill handles task decomposition, agent dispatch, progress tracking, and result consolidation.

Activation

User types [SWARM] followed by a task description or goal.

[SWARM] implement login page with JWT auth
[SWARM] refactor all API endpoints to use async/await
[SWARM] add comprehensive tests for auth module

Execution Protocol

1. Analyze & Decompose

Break the user's request into independent subtasks that can execute in parallel.

Rules:

  • Each subtask must have a clear input/output boundary
  • No subtask should block another (true parallelism)
  • Limit to 4-8 concurrent agents (more creates coordination overhead)
  • Group dependent work into sequential phases

Decomposition example:

Request: "Add user authentication"

Phase 1 (parallel):
  ├── Agent-A: Backend auth API endpoints
  ├── Agent-B: Database schema migrations
  └── Agent-C: Auth middleware module

Phase 2 (parallel, after Phase 1):
  ├── Agent-D: Login page frontend
  ├── Agent-E: Session management
  └── Agent-F: Tests for auth flows

Phase 3 (sequential):
  └── Orchestrator: Integration verification

2. Dispatch Subagents

Spawn subagents with clear instructions:

// Use actor tool with subagent_type: "general"
{
  "operation": {
    "action": "run",
    "subagent_type": "general",
    "description": "Implement auth API",
    "prompt": "Task: Build JWT authentication API endpoints.

Work directory: <current working directory>

Requirements:
1. POST /api/auth/login — accept email/password, return JWT
2. POST /api/auth/refresh — refresh expired tokens
3. POST /api/auth/logout — invalidate refresh token
4. All endpoints need @login_required decorator

Output artifacts:
- /src/auth/api.py — API routes
- /src/auth/middleware.py — JWT middleware
- /tests/test_auth.py — test coverage

Verification:
- Run: python -m pytest tests/test_auth.py -v
- All tests must pass before reporting completion

Handoff format when done:
Status: success | failed
Summary: <one-line description>
Files touched: <paths>
Tests: <count> passing
"
  }
}

3. Track Progress

Monitor all running agents. Log status updates:

[HH:MM:SS] SWARM ACTIVATED — Goal: "implement login page"
[HH:MM:SS] Phase 1: Dispatching 3 agents (auth-api, db-migrate, middleware)
[HH:MM:SS] Agent auth-api: In Progress
[HH:MM:SS] Agent db-migrate: In Progress  
[HH:MM:SS] Agent middleware: In Progress
[HH:MM:23] Agent db-migrate: Done — 4 migrations applied
[HH:MM:45] Agent middleware: Done — JWT middleware complete
[HH:MM:12] Agent auth-api: Done — 6 endpoints implemented
[HH:MM:13] Phase 2: Dispatching 3 agents (login-ui, session, tests)
...
[HH:MM:58] SWARM COMPLETE — All phases finished successfully

4. Aggregate Results

Collect outputs from all agents. Produce a final summary:

=== SWARM EXECUTION REPORT ===
Goal: implement login page with JWT auth
Duration: 4 minutes 23 seconds
Agents: 6 dispatched across 2 phases

Phase 1 Results:
  ✅ auth-api — 6 endpoints, 12 tests passing
  ✅ db-migrate — 4 migrations applied
  ✅ middleware — JWT validation complete

Phase 2 Results:
  ✅ login-ui — React component + form validation
  ✅ session — Redis session store implemented
  ✅ tests — 28 integration tests added

Final Verification:
  ✅ All tests passing (40/40)
  ✅ No lint errors
  ✅ Git diff: 8 files changed, +612/-45 lines

Status: COMPLETE

Agent Types

Match agent specialization to task type:

Agent TypeBest ForModel
generalComplex multi-step work, any domainHigh-reasoning
exploreCode search, understanding, analysisAny
developerCode generation, refactoring, bug fixesMid-to-high tier

Common Patterns

Parallel Implementation

[SWARM] implement CRUD for User model

→ Dispatch 4 agents:
  1. Backend routes (POST/GET/PUT/DELETE)
  2. Database schema + migrations
  3. Frontend list/create/edit views
  4. Test suite

Research + Build

[SWARM] add real-time notifications

→ Phase 1 (research):
  1. Explore current codebase for event patterns
  2. Design notification schema

→ Phase 2 (build, parallel):
  1. Implement WebSocket handler
  2. Build notification model + API
  3. Create frontend notification panel

Refactor Campaign

[SWARM] migrate to TypeScript

→ Dispatch 4 agents:
  1. Config setup (tsconfig, path aliases)
  2. Core types (models, APIs, utils)
  3. Migrate existing JS modules
  4. Update build pipeline + tests

Error Handling

Agent Failure

[HH:MM:SS] Agent auth-api: FAILED — SyntaxError in api.py:45

Recovery options:
1. Retry with clarified instructions
2. Fix the error directly if simple
3. Skip and continue with other agents (if non-critical)
4. Stop entire swarm (if blocking dependency)

Timeout

  • Set explicit timeouts: "timeout_ms": 300000 (5 minutes)
  • Agents that timeout are marked failed — timeout
  • Orchestration continues with remaining agents

Resource Limits

  • Max 8 concurrent agents per swarm
  • If queue exceeds limit, batch: run 4, wait, run next 4

When NOT to Use [SWARM]

  • Single focused task — Just do it directly
  • Sequential dependencies — One thing must finish before next starts
  • Trivial changes — Under 30 minutes of work
  • Ambiguous goals — Clarify requirements first, then swarm

Use [SWARM] when you have multiple independent workstreams that can execute in parallel to save time.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

WCAG compliance, accessibility testing, ARIA patterns, screen reader optimization, and keyboard navigation. Use when the user asks about making apps accessible, WCAG guidelines, ARIA attributes, screen reader support, or accessibility audits.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ProjectZeroDays/FreeAI_AI_Inference_Workstation52026년 9월 4일 업데이트

ace-music

무료

Generate AI music using ACE-Step 1.5 via ACE Music's free API. Use when the user asks to create, generate, or compose music, songs, beats, instrumentals, or audio tracks. Supports lyrics, style prompts, covers, and repainting. Free API, no cost.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ProjectZeroDays/FreeAI_AI_Inference_Workstation52026년 9월 4일 업데이트

A continuously adaptive skill suite that empowers Clawdbot to act as a versatile coder, business analyst, project manager, web developer, data analyst, and NAS metadata scraper. It intelligently discovers free resources, adapts to user context, and ensures reliable, proven guidance across multiple domains.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ProjectZeroDays/FreeAI_AI_Inference_Workstation52026년 9월 4일 업데이트

High-fidelity intelligence gathering and data assimilation. Focuses on synthesizing fragmented data into a comprehensive target operational picture.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ProjectZeroDays/FreeAI_AI_Inference_Workstation52026년 9월 4일 업데이트

Use when Codex needs proactive task queues, heartbeat routines, scheduled follow-up, or continuous work loops instead of waiting for prompts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ProjectZeroDays/FreeAI_AI_Inference_Workstation52026년 9월 4일 업데이트

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ProjectZeroDays/FreeAI_AI_Inference_Workstation52026년 9월 4일 업데이트

ProjectZeroDays의 스킬 모두 보기

이 스킬의 문제 신고