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orch-change-feature

协调对现有工作特性进行变更，使其满足新行为要求——先根据新规范更新测试，再修改实现以通过测试，最后进行审查并提交。适用于行为正常但需要改变的情况。

orch-change-feature 是什么？

orch-change-feature 是一个用于协调修改已有工作特性行为的技能，基于 orch-pipeline 共享引擎构建。当功能代码正常工作但预期行为需要调整时使用，例如“调整”、“改为”、“同时支持 X”等场景。它与 orch-fix-defect（修复缺陷）和 orch-add-feature（添加新功能）不同，专门针对功能正常工作但行为需要改变的情况。该技能默认采用小粒度变更（通常涉及一两个函数），执行流程包括：更新现有测试以表达新行为（这是区分调整与修复的关键步骤）、修改实现使测试通过、代码审查、以及两步审批（计划/变更测试审批和提交前审批）。如果变更涉及安全触发条件，会自动引入安全审查员。

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

角色 · 动作 · 目标：orch · change · feature。是 orch-pipeline 中共享引擎的轻量封装。

何时使用

  • 现有功能工作正常，但期望的行为不同（“变更”、“调整”、“让它也……”，“不执行 X 而是执行 Y”）。
  • 与同类技能区分：
    • 不是故障 → 不适用 orch-fix-defect（无缺陷可复现）。
    • 不是新功能 → 不适用 orch-add-feature（功能已存在）。

操作设置

  • 默认大小下限： 小——大多数调整只涉及一两个函数。
  • 阶段掩码： 0 →（仅当新行为需要调研时才进入 1）→ 轻量 2 → 4 → 5 → 6。
  • 第一步（阶段 4）： 更新现有测试以表达新的期望行为，然后修改实现直到测试通过。先修改测试是区分调整与修复的关键。

工作原理

  1. 使用以上设置运行 orch-pipeline 引擎。
  2. 保持计划简洁——仅当规模为 standard+ 时才需要完整的 planner 流程。
  3. 在关卡 1（计划 / 变更测试审批）和关卡 2（提交前审批）暂停。
  4. 如果变更涉及安全触发条件，添加 security-reviewer。

示例

orch-change-feature: 让 nws-poller 在出现 2 次警告时发出警报，而非 3 次
→ 更新阈值测试以匹配新规范 → 修改实现以使测试通过
→ 代码审查 → 提交  [关卡 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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