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orch-add-feature

새로운 기능을 처음부터 끝까지 구축하는 작업을 조율합니다. 리서치, 계획, TDD 구현, 리뷰, 게이트 커밋까지 각 단계를 적절한 ECC 에이전트에 위임합니다. 아직 존재하지 않는 기능을 추가할 때 사용하세요.

orch-add-feature란 무엇인가요?

`orch-add-feature`는 완전히 새로운 기능을 개발하는 엔드-투-엔드 워크플로우를 조율하는 스킬입니다. 이 스킬은 공유 엔진 `orch-pipeline`을 기반으로 하여, 리서치, 계획, TDD(Test-Driven Development) 기반 구현, 코드 리뷰, 그리고 게이트 커밋(gated commit) 단계를 순차적으로 수행합니다. 주로 사용자가 '추가', '빌드', '구현' 등 이전에 존재하지 않았던 동작을 요청할 때 적합하며, 버그 수정(`orch-fix-defect`)이나 기존 기능 변경(`orch-change-feature`)과는 구분됩니다. 기본적으로 표준 크기로 가정하여 리서치와 계획 단계를 수행하지만, 명백히 작은 기능인 경우 4→5→6단계로 축소됩니다. 특히 보안 트리거를 건드리는 기능이면 `security-reviewer`를 자동으로 추가하여 보안 검토를 강화합니다. Claude Code, Cursor 등 주요 AI 에이전트 플랫폼에서 활용할 수 있으며, `/feature-dev`와 달리 `orch-pipeline` 엔진을 공유하여 크기 분류와 게이트 검증을 일관성 있게 처리합니다.

지원 대상✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/orch-add-feature

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

orch-add-feature은(는) 무엇을 하나요?

액터 · 액션 · 타겟: orch · add · feature. 공유 엔진 orch-pipeline을 감싸는 얇은 래퍼입니다.

사용 시기

  • 사용자가 아직 존재하지 않는 기능을 원할 때 ("추가", "빌드", "구현", "지원 …").
  • 완전히 새로운 동작일 때 — 버그 수정(orch-fix-defect)이나 기존 동작 변경(orch-change-feature)이 아닌 경우.

작업 설정

  • 기본 크기 기준: 표준 — 명확히 작지 않은 경우 리서치 + 계획 실행.
  • 단계 마스크: 0 → 1 → 2 → 4 → 5 → 6 (3번 스캐폴드(Scaffold)는 건너뜀; 이는 MVP 전용).
  • 첫 번째 동작(단계 4): 새로운 동작에 대한 새로운 실패 테스트를 작성한 후, 통과하도록 구현합니다.

작동 방식

  1. 위 설정으로 orch-pipeline 엔진을 실행합니다.
  2. 먼저 크기를 분류합니다. 작거나 사소한 기능은 4 → 5 → 6 단계로 축소됩니다.
  3. 게이트 1(계획 승인)과 게이트 2(커밋 전)에서 중단합니다.
  4. 기능이 보안 트리거에 닿으면 security-reviewer를 추가합니다.

관련: /feature-dev는 이 흐름의 독립형 버전입니다. orch-add-feature는 orch-pipeline 엔진(크기 분류기와 두 게이트)을 나머지 제품군과 공유하여 사소한 기능을 4 → 5 → 6으로 적절히 조정한다는 점에서 다릅니다.

예시

orch-add-feature: add OAuth2 login to nws-poller
→ 리서치 기존 auth 라이브러리 → 계획 task_list  [게이트 1: 승인]
→ 각 작업 TDD → 코드 리뷰 (+ security-reviewer: auth 경로)
→ 커밋  [게이트 2: 확인]

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

관련 스킬