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orch-fix-defect

排程修復一個錯誤 — 將其重現為一個失敗的回歸測試、修復至綠燈、審查，並通過閘門提交 — 透過將每個階段委派給對應的 ECC 代理程式。當現有行為損壞或錯誤時使用。

orch-fix-defect 是什麼？

`orch-fix-defect` 是一個專門用於程式缺陷修復的 AI 代理技能，旨在透過結構化、可重複的管線來處理程式碼庫中的錯誤、崩潰或回歸問題。它將修復過程分解為多個階段：首先在 `orch-pipeline` 共享引擎上建立一個失敗的回歸測試以重現錯誤，然後修復程式碼使其通過測試，接著進行程式碼審查，最後通過閘門提交。此技能支援 Claude Code、Cursor 等平台，並可與 `code-explorer` 和 `build-error-resolver` 等代理協作，以釐清根本原因或處理建置錯誤。與修改功能（`orch-change-feature`）或新增功能（`orch-add-feature`）不同，本技能專門針對破壞性行為，並透過強制先證明錯誤存在來確保修復的精確性。預設將問題視為小型或瑣碎問題，並在必要時啟動安全審查。

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

orch-fix-defect 是做什麼的？

參與者 · 動作 · 目標：orch · fix · defect。共享引擎 orch-pipeline 的輕量包裝。

使用時機

  • 某些東西壞了：輸出錯誤、發生異常、崩潰、回歸。
  • 與同類技能區分：
    • 行為正確但您想要不同 → orch-change-feature。
    • 該功能尚不存在 → orch-add-feature。

操作設定

  • 預設規模下限： 小（通常微不足道）。
  • 階段遮罩： 0 → （僅在根本原因不明顯或標準以上時使用輕量階段 2） → 4 → 5 → 6。研究階段（1）通常會跳過。
  • 第一步（階段 4）： 將錯誤重現為一個新的失敗測試（回歸測試），然後修復直到測試變為綠燈。先證明錯誤的存在是將修復與微調區分開來的關鍵。

運作方式

  1. 使用上述設定執行 orch-pipeline 引擎。
  2. 如果根本原因不明確，請在紅燈測試之前使用 code-explorer 界定範圍；將建置錯誤升級至 build-error-resolver / /build-fix。
  3. 在閘門 1（僅在產出計劃時）和閘門 2（提交前）停止。
  4. 如果缺陷位於安全敏感路徑中，請加入 security-reviewer。

範例

 orch-fix-defect: poller crashes on empty NWS response
 → write failing test reproducing the crash → fix to green
 → code-review → commit  [GATE 2: confirm]   (commit: fix:)

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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