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orch-change-feature

協調修改現有功能,使其符合新的行為期望——更新測試以符合新規格、修改實作使其通過、進行審查,並在閘控下提交。適用於功能並未故障,但行為需要改變的情況。

orch-change-feature 是什麼?

orch-change-feature 是一個位於 orch-pipeline 共享引擎之上的薄封裝技能,專門用於協調「修改既有功能」的流程。當一個功能本身運作正常,但需求變更(例如「調整」、「改成也支援…」、「將 X 改為 Y」)時使用。 此技能與 orch-fix-defect(用於修復錯誤)和 orch-add-feature(用於新增功能)明確區分。其操作設定預設將規模設為 small(僅需修改一兩個函式),並採用特殊的階段遮罩:跳過 phase 0,必要時進行 phase 1 研究,然後快速經過 light 2、4、5、6。關鍵在於 phase 4 會先更新現有測試以反映新的期望行為,再修改實作使其通過測試——這種「先改測試」的順序是區分「調整」與「修復」的核心。 流程在 Gate 1(計畫與修改後測試的核准)和 Gate 2(提交前)設有檢查點,若變更涉及安全性觸發條件則會自動引入 security-reviewer。整個流程支援 Claude Code、Cursor、Codex 等 AI 代理平台,適合開發者在處理行為變更而非錯誤修復時使用。

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

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

orch-change-feature 是做什麼的?

Actor · action · target:orch · change · feature。這是 orch-pipeline 共享引擎的薄封裝。

使用時機

  • 現有功能能運作,但期望的行為不同(「變更」、「調整」、「讓它也能…」、「改成 Y 而非 X」)。
  • 與同類區別:
    • 不是故障 → 非 orch-fix-defect(無需重現錯誤)。
    • 不是新增 → 非 orch-add-feature(該功能已存在)。

操作設定

  • 預設規模下限: small——多數調整只需修改一或兩個函式。
  • 階段遮罩: 0 →(僅在新行為需要研究時才執行 1)→ light 2 → 4 → 5 → 6。
  • 第一步(phase 4): 先更新既有測試,使其反映新的期望行為,再修改實作直到測試通過。先改測試是區分「調整」與「修復」的關鍵。

運作方式

  1. 使用上述設定執行 orch-pipeline 引擎。
  2. 保持計畫輕量——僅在 standard+ 規模時才需要完整的 planner 流程。
  3. 在 Gate 1(計畫與修改後測試的核准)和 Gate 2(提交前)暫停。
  4. 若變更涉及安全性觸發條件,則加入 security-reviewer。

範例

 orch-change-feature: 讓 nws-poller 在 2 次警告而非 3 次時發出警示
 → 更新閾值測試至新規格 → 修改實作使其通過
 → 程式碼審查 → 提交  [GATE 2:確認]

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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