Communitygithub.com

orch-add-feature

編排一個全新功能的從頭到尾開發流程——研究、規劃、TDD 實作、程式碼審查、與閘控提交——將每個階段委派給對應的 ECC 代理。適用於添加尚未存在的能力。

orch-add-feature 是什麼?

「orch-add-feature」是一個高階編排技能,用於驅動全新功能(非既有行為修正或變更)的端到端建置流程。它本身是「orch-pipeline」引擎的薄包裝器,共用了尺寸分類器、兩道閘門(Gate 1:計畫審核、Gate 2:預提交檢查)等核心機制。此技能會依序執行研究、規劃、TDD 實作、程式碼審查與提交等階段;對於小型或微不足道的功能,則會自動縮減至實作→審查→提交的三步驟快速路徑。支援 Claude Code、Cursor、Codex 等平台,開發者只要下達如「orch-add-feature: add OAuth2 login to nws-poller」的自然語言指令,系統就會自動探索方案、產生待辦清單,並在每個閘門等待開發者確認後才繼續。若功能涉及安全敏感路徑,系統還會自動引入「security-reviewer」代理進行安全評估。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

orch-add-feature 是做什麼的?

角色 · 動作 · 目標:orch · add · feature。共用 orch-pipeline 引擎的精簡包裝器(請參閱 orch-pipeline)。

使用時機

  • 使用者想要一個尚未存在的能力(例如:「新增」、「建置」、「實作」、「支援……」)。
  • 這是全新行為——不是修正 (orch-fix-defect),也不是改變既有行為 (orch-change-feature)。

操作設定

  • 預設規模下限: standard——除非很明顯是小功能,否則先執行研究與規劃。
  • 階段遮罩: 0 → 1 → 2 → 4 → 5 → 6(跳過 3 Scaffold;該階段僅限 MVP 流程使用)。
  • 第一個步驟(階段 4): 為新行為撰寫新的失敗測試,然後實作到通過測試。

運作方式

  1. 以上述設定執行 orch-pipeline 引擎。
  2. 先分類功能規模;小型/微不足道的功能會直接收斂到 4 → 5 → 6。
  3. 在閘門 1(計畫審核)與閘門 2(預提交)暫停等待確認。
  4. 若功能涉及安全觸發條件,則自動加上 security-reviewer 代理。

相關資訊:/feature-dev 是此流程的獨立版本。orch-add-feature 的差異在於它與家族其他技能共用 orch-pipeline 引擎——包括規模分類器與兩道閘門——因此能將微不足道的功能自動縮減到 4 → 5 → 6 的快速路徑上。

範例

orch-add-feature: add OAuth2 login to nws-poller
→ research existing auth libs → plan task_list  [GATE 1: approve]
→ TDD each task → code-review (+ security-reviewer: auth path)
→ commit  [GATE 2: confirm]

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.

相關技能