컬렉션
컬렉션 orchestration

주목할 만한 에이전트 실행 오케스트레이션 플랫폼 | AI Agent Skills

Claude Code, Cursor, Windsurf용 orchestration platforms execution AI Agent Skills 모음입니다. 개발자 워크플로 자동화에 초점을 둡니다.

이 페이지는 실제 배포 파이프라인에서 복잡한 에이전트 실행을 조율하는 오케스트레이션 플랫폼을 다룹니다. 도구들은 의존성 순서, 병렬 작업, 재시도, 승인, 장기 실행 작업 가시성을 지원하여 프로덕션에서 다단계 자동화를 안전하게 운영할 수 있게 합니다. MCP 통합이 일부 가능할 수 있지만, 핵심 우선순위는 오케스트레이션 신뢰성입니다.

Primary Install Bridge

Pick One Skill, Then Take the Install Path

This collection should not trap users in comparison mode or pretend to install the whole collection. Its job is to narrow the shortlist to one skill, then send the next click into installation, validation, and rollout. Installation happens on the skill path.

Step 1 / Primary Path

Step 1: Open the installation docs

Move from agent orchestration comparison into the install flow, validation checklist, and first command path before comparing anything else.

Open the Install Path
Best-Fit Next Paths

The Next Click Should Keep Narrowing, Not Reset Back To A Generic Directory

Once the install path is clear, move into the solution, CLI, or editorial surface that best matches this collection. That keeps platform, framework, and operations demand narrowing into a more verifiable high-intent journey.

Editorial Proof & Trust Signals

Why This Collection Deserves Priority Attention

Reviewed on 2026-04-17 against orchestration fit, installation clarity, approval-aware execution, and production handoff value. This page is now positioned as an install-first orchestration entry point instead of a generic platform roundup.

We prioritize this page because orchestration-intent users usually need one platform they can install, validate, and prove inside a real multi-step workflow before standardizing anything broader.

Trust Signals

  • - Entries are chosen for practical orchestration value such as routing, approvals, retries, visibility, and long-running execution support.
  • - Selection favors tools with public docs and setup paths that teams can validate before pushing them into production workflows.
  • - The page is curated for operator-facing execution reliability, not for abstract platform adjacency or vague agent branding.

Grouping Logic

  • - Lead with tools that can own multi-step execution without hiding approval boundaries or retry behavior.
  • - Keep the shortlist compact enough for fast comparison while still covering orchestration, handoff, and production visibility.
  • - Use installation as the bridge from orchestration comparison into validated workflow execution.

Maintenance & Review

Last Reviewed
2026-04-17
Cadence
Re-check when install flow, maintainer posture, or orchestration relevance changes upstream; otherwise review monthly.
Maintained By
Killer-Skills editorial review within the recovery-focused authority queue.
Verification
Validate installability, approval clarity, execution fit, and maintainer trust before retaining or adding an entry.
Execution Examples

How These Skills Work Together In Practice

Validate one approval-gated workflow lane first

Use this page when you need one orchestration layer that can move a real workflow through routing, approvals, retries, and handoff without turning selection into another architecture debate.

  1. 1. Open the installation docs before comparing more orchestration platforms.
  2. 2. Choose one platform that best matches your approval boundaries and execution model.
  3. 3. Install it and verify the CLI write path, retry behavior, and first operator checkpoint.
  4. 4. Only after the base lane works, expand it to broader workflow automation.

Prove retry and handoff behavior before scaling

Treat the collection as an editorial filter when you need orchestration tools that can survive long-running jobs and operator checkpoints without hiding failure modes.

  1. 1. Check whether the platform has stable ownership and visible install guidance.
  2. 2. Review operator behavior so the team knows where retries, approvals, and logs actually live.
  3. 3. Use one validated workflow lane before standardizing orchestration across more systems.
  4. 4. Document the chosen orchestration baseline after the first clean production-style test.
After Installation

Supporting Moves After The Install Path Is Clear

backend-code-review

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langgenius

백엔드 코드 리뷰는 AI 에이전트 스킬을 사용하여 백엔드 코드를 리뷰하는 과정입니다

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인공지능

component-refactoring

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langgenius

The component-refactoring skill simplifies high-complexity React components, improving code quality and reducing maintenance efforts for developers. It utilizes patterns and workflows to refactor components, making them more efficient and easier to understand.

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인공지능

frontend-testing

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langgenius

Generate Vitest + React Testing Library tests for Dify frontend components, hooks, and utilities. Triggers on testing, spec files, coverage, Vitest, RTL, unit tests, integration tests, or write/review test requests.

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인공지능

orpc-contract-first

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langgenius

Guide for implementing oRPC contract-first API patterns in Dify frontend. Trigger when creating or updating contracts in web/contract, wiring router composition, integrating TanStack Query with typed contracts, migrating legacy service calls to oRPC, or deciding whether to call queryOptions directly vs extracting a helper or use-* hook in web/service.

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frontend-code-review

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langgenius

프론트엔드 코드 리뷰는 프론트엔드 코드를 자동으로 리뷰하는 과정

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frontend-query-mutation

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langgenius

프론트엔드 쿼리 및 뮤테이션 패턴은 TanStack Query와 oRPC를 사용하여 구현되는 Dify 계약 관리 솔루션입니다

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인공지능

guardian-evolution-system

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frankxai

Design and implement the Guardian AI companion evolution system - from Level 1 Spark to Level 50 Transcendent. XP mechanics, personality adaptation, and player progression.

0
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개발자

arcanea-react-best-practices

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frankxai

/Lumina-Intelligence, /Luminor-intelligence & /StarlightOrchestrator building Arcanea with swarm intelligence that overlays any AI. Ten Gates, Guardians & Godbeasts, context that compounds, and the creative civilization OS for creators who build universes.

3
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인공지능

ReasoningBank with AgentDB

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frankxai

Implement ReasoningBank adaptive learning with AgentDBs 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

3
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인공지능

brand-voice

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frankxai

Apply and enforce brand voice across all content creation. Manages voice attributes, tone adaptation by channel, style rules, and terminology. Use when writing content, reviewing drafts, or defining brand voice for a new project. Loads voice config from CREATOR.md or creator-memory/voice.md.

2
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인공지능
Related Authority Collections

If This Page Is Close, Keep Narrowing With Adjacent Authority Pages

Do not reset back to the generic directory. Move sideways through these adjacent high-intent collections to narrow the shortlist toward the install path that best matches your team.

Installation Questions

Answer These Three Questions Before You Install

This collection should not keep users browsing forever. These three questions explain how to shortlist, install, and validate the next step.

이 컬렉션들은 어떤 워크플로를 위해 만들어졌나요?

워크플로 자동화, 프로세스 자동화, 문서 작업, 데이터 워크플로 및 재사용 가능한 스킬 스택을 중심으로 구성되어 있습니다.

컬렉션과 메인 스킬 디렉터리의 차이점은?

스킬 디렉터리는 직접 검색과 필터링에 적합하고, 컬렉션은 완전한 워크플로를 위한 보완적 스킬 묶음을 찾는 데 적합합니다.

이 컬렉션들을 Claude Code나 Cursor에 설치할 수 있나요?

네. 이 컬렉션의 스킬들은 일반적으로 Claude Code, Cursor, Windsurf 및 기타 지원 환경에서 통합 설치 흐름으로 작동합니다.

Additional Recovery Paths

Use These Additional Paths If You Need One More Step To Narrow The Decision

These are the supporting surfaces for this collection after the install direction is clear and the primary next paths have already narrowed the decision.