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orch-add-feature(编排添加新功能)

通过将调研、规划、TDD实现、审查和有门控的提交各阶段委派给对应的ECC智能体,从头到尾地编排构建一个全新的功能。在需要添加尚不存在的功能时使用。

orch-add-feature(编排添加新功能) 是什么?

orch-add-feature 是一个 AI 智能体技能,专为从头开始构建全新功能而设计。它作为 `orch-pipeline` 引擎的精简包装,将复杂的功能添加流程分解为调研、规划、TDD(测试驱动开发)实现、代码审查和门控提交等多个阶段,每个阶段委派给最合适的 ECC 智能体。 该技能能够自动根据功能规模进行自适应:默认以“标准”规模启动(除非明显很小),对于微小或琐碎的功能,它会跳过不必要的阶段(如脚手架),直接进入实现—审查—提交的快速通道。它内置两个门控检查点(Gate 1:规划审批;Gate 2:预提交),确保质量和安全性。如果新功能涉及安全相关代码,系统会自动引入安全审查员。 与独立的 `/feature-dev` 变体不同,`orch-add-feature` 通过共享 `orch-pipeline` 引擎与家族中其他技能(如修复缺陷、变更功能)保持一致,从而能够利用统一的规模分类器和门控机制,为不同类型的工作提供一致且高效的执行路径。

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

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打开一个已预加载此 Agent Skill 的新对话。

文档

orch-add-feature(编排添加新功能) 是做什么的?

行为者 · 动作 · 目标: orch · add · feature。这是对共享引擎 orch-pipeline 的轻量封装。

使用时机

  • 用户需要一个尚不存在的能力(“添加”、“构建”、“实现”、“支持…”)。
  • 这是全新的行为——不是修正(orch-fix-defect),也不是对现有行为的更改(orch-change-feature)。

操作设置

  • 默认规模下限: 标准——除非明显很小,否则先执行调研+规划。
  • 阶段掩码: 0 → 1 → 2 → 4 → 5 → 6(跳过阶段3“脚手架”;该阶段仅用于MVP)。
  • 第一步(阶段4): 为新行为编写新的失败测试,然后实现直到测试通过。

工作原理

  1. 使用上述设置运行 orch-pipeline 引擎。
  2. 首先对功能进行规模分类;小/琐碎功能直接进入 4 → 5 → 6 阶段。
  3. 在门控1(规划审批)和门控2(预提交)处暂停。
  4. 如果新功能涉及安全触发器,自动添加 security-reviewer。

相关: /feature-dev 是该流程的独立版本。orch-add-feature 的不同之处在于它与家族中其他技能共享 orch-pipeline 引擎——包括规模分类器和两个门控——因此它能够将琐碎功能自动调整到 4 → 5 → 6 阶段。

示例

orch-add-feature: 为 nws-poller 添加 OAuth2 登录
→ 调研现有认证库 → 规划任务列表  [门控1: 批准]
→ TDD 每个任务 → 代码审查(+ security-reviewer: 认证路径)
→ 提交  [门控2: 确认]

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.

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