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orch-fix-defect

バグ修正をオーケストレーションします。再現テストの作成、修正、レビュー、ゲート付きコミットまでを、各フェーズを適切なECCエージェントに委譲して実行します。既存の動作が壊れている場合や誤っている場合に使用します。

orch-fix-defect とは？

このスキルは、バグ修正の全工程を自動化・オーケストレーションするためのものです。具体的には、バグを再現する失敗テスト（回帰テスト）を作成し、修正が成功するまでテストをグリーンにし、コードレビューを経て、ゲート付きでコミットするまでを、各フェーズに最適なECCエージェントに委譲して実行します。 `orch-fix-defect` は、`orch-pipeline` エンジンの薄いラッパーとして動作します。主なユースケースは、出力の誤り、エラー、クラッシュ、リグレッションなど、既存の動作が壊れている場合です。類似スキルである `orch-change-feature`（動作は正しいが変更したい場合）や `orch-add-feature`（機能が存在しない場合）と明確に区別されます。 デフォルトのサイズフロアは「小」で、フェーズマスクは 0 → (必要に応じてライト2) → 4 → 5 → 6 と進み、リサーチフェーズ(1)は通常スキップされます。最初のアクション（フェーズ4）では、バグを再現する新しい失敗テストを作成し、修正後にグリーンになるまで繰り返します。バグの存在をまず証明することが、単なる調整と修正を区別する鍵です。 根本原因が不明な場合は、レッドテストの前に `code-explorer` でスコープを特定し、ビルドが壊れた場合は `build-error-resolver` や `/build-fix` にエスカレーションします。Gate 1（プラン生成時）と Gate 2（コミット前）で停止し、セキュリティに関わるパスであれば `security-reviewer` が追加されます。

対応✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/orch-fix-defect
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このエージェントスキルを事前に読み込んだ状態で新しいチャットを開きます。

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ドキュメント

orch-fix-defect は何をしますか？

Actor · action · target: orch · fix · defect。共有エンジン orch-pipeline の薄いラッパーです。

使用するタイミング

  • 何かが壊れている場合：誤った出力、エラー、クラッシュ、リグレッション。
  • 類似スキルとの区別：
    • 動作は正しいが変更したい場合 → orch-change-feature。
    • その機能がまだ存在しない場合 → orch-add-feature。

動作設定

  • デフォルトサイズフロア: 小（多くの場合トリビアル）。
  • フェーズマスク: 0 → (根本原因が自明でない、または標準以上の場合はライト2のみ) → 4 → 5 → 6。リサーチ(1)は通常スキップされます。
  • 最初のアクション（フェーズ4）: バグを新しい失敗テスト（回帰テスト）として再現し、グリーンになるまで修正します。バグの存在を最初に証明することが、修正と単なる調整を分けるポイントです。

動作の仕組み

  1. 上記の設定で orch-pipeline エンジンを実行します。
  2. 根本原因が不明な場合は、レッドテストの前に code-explorer でスコープを特定します。ビルド破損は build-error-resolver / /build-fix にエスカレーションします。
  3. Gate 1（プランが生成された場合のみ）と Gate 2（コミット前）で停止します。
  4. 欠陥がセキュリティに関わるパスにある場合は、security-reviewer を追加します。

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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