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orch-fix-defect

協調修復錯誤的流程:將缺陷重現為失敗的回歸測試、修正到通過、程式碼審查,最後進行閘控提交。當現有行為出錯或錯誤時使用。

orch-fix-defect 是什麼?

此技能「orch-fix-defect」是一個專門用於協調修復程式碼缺陷的 AI 代理技能。它透過委派缺陷修復的各個階段給對應的 ECC 代理(探索、修正、審查、提交)來運作。核心工作流程是先將 Bug 重現為一個新的失敗回歸測試(紅燈),然後修正程式碼直到測試通過(綠燈),最後進行程式碼審查與閘控提交。此技能特別適用於現有行為不正確、發生錯誤或崩潰的情況,並與修改功能(orch-change-feature)和新增功能(orch-add-feature)明確區分。支援的平台包括 Claude Code 等 AI 編碼代理,其預設操作規模為「小型」,且通常會跳過研究階段,直接進入重現與修正。

相容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/orch-fix-defect

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

orch-fix-defect 是做什麼的?

Actor · action · target:orch · fix · defect。此為 orch-pipeline 中共享引擎的薄封裝。

使用時機

  • 某件事出錯了:輸出錯誤、發生錯誤、崩潰或回歸。
  • 與同系列技能區分:
    • 行為正確但您想改變它 → orch-change-feature。
    • 該功能尚不存在 → orch-add-feature。

操作設定

  • 預設規模下限: 小型(通常很簡單)。
  • 階段遮罩: 0 → (僅在根因不明顯或標準以上時使用輕量 2)→ 4 → 5 → 6。通常跳過研究(1)。
  • 第一步驟(階段 4): 將錯誤重現為一個新的失敗測試(回歸測試),然後修正直到測試變綠。先證明錯誤存在,是區分「修復」與「微調」的關鍵。

運作方式

  1. 使用上述設定執行 orch-pipeline 引擎。
  2. 如果根因不明確,先在紅燈測試之前使用 code-explorer 界定範圍;將建置失敗升級給 build-error-resolver / /build-fix處理。
  3. 在閘門 1(僅當產出計畫時)和閘門 2(提交前)停止。
  4. 如果缺陷位於安全敏感路徑中,則加入 security-reviewer。

範例

orch-fix-defect:poller crashes on empty NWS response
→ 撰寫失敗測試重現崩潰 → 修正到通過
→ 程式碼審查 → 提交  [閘門 2:確認]   (提交訊息: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/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

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