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平行執行最佳化器

當使用者希望透過平行工作、並行代理、批次工具呼叫、隔離工作目錄或多重獨立驗證通道來大幅加速任務,同時不失去正確性時使用。

平行執行最佳化器 是什麼?

這個技能的核心在於將緊迫性轉化為依賴圖,並在執行大規模平行操作時確保正確性。它適用於程式碼庫檢查、檔案讀取、API 驗證、瀏覽器檢查、建置/測試通道、部署回讀或多工作目錄實作。支援如 Claude Code、Cursor 或 Codex 等代理工具,透過定義目標與完成訊號、將工作拆分為通道 (車道)、標記每個通道為平行、序列或閘控,然後安全地平行執行獨立讀取/檢查。寫入操作須依檔案、工作目錄、分支、服務或資料集隔離,僅在證據顯示通道相容時合併。最終以驗證表呈現,而非模糊的速度聲明,避免毀滅性命令、遷移或影響線上的部署。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/parallel-execution-optimizer

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

平行執行最佳化器 是做什麼的?

當速度來自同時進行獨立工作時使用此技能: 程式碼庫檢查、檔案讀取、API 檢查、瀏覽器檢查、建置/測試通道、 部署回讀或多工作目錄實作通道。

核心模式

在行動之前,將緊迫性轉化為依賴圖。

  1. 定義目標與完成訊號。
  2. 將工作拆分為通道。
  3. 將每個通道標記為平行、序列或閘控。
  4. 同時執行獨立的讀取/檢查。
  5. 依檔案、工作目錄、分支、服務或資料集隔離寫入操作。
  6. 只有在證據顯示通道相容時才合併。
  7. 以驗證表結束,而非模糊的速度聲明。

通道矩陣

在大型推送之前,寫入一個緊湊的矩陣:

通道 | 可以平行執行? | 寫入範圍 | 風險 | 驗證
程式碼庫掃描 | 是 | 無 | 低 | rg/git status 輸出
後端修補 | 或許 | src/api | 中 | 單元測試
前端修補 | 或許 | app/components | 中 | 瀏覽器截圖
部署回讀 | 在建置後 | 遠端服務 | 高 | 即時 URL + 日誌

僅在其寫入範圍不衝突時才平行執行通道。

執行規則

  • 批次處理檔案讀取、搜尋、狀態檢查與後設資料查詢。
  • 對大型不相關的實作通道使用隔離的工作目錄。
  • 在獨立工作階段中啟動長時間執行的測試、建置、回填與部署, 然後審慎地輪詢它們。
  • 如果某個通道發現阻礙者而改變了計畫,暫停依賴的通道 並更新矩陣。
  • 除非使用者明確要求一個持續服務,否則絕不讓背景程序存活超過該回合。
  • 未經明確閘控,不得平行執行毀滅性命令、遷移、對同一資料表的寫入, 或影響線上客戶的部署。

輸出格式

報告時請使用以下格式:

平行執行結果:
- 執行的通道數:5
- 完成的通道數:4
- 被阻擋的通道:部署回讀,等待 DNS 傳播
- 找到的捷徑:批次程式碼庫掃描 + 焦點測試
- 驗證:語法檢查通過、單元測試通過、即時煙霧測試通過

失敗模式

  • 過多的並行導致衝突的編輯。
  • 對工具進行基準測試而非任務本身。
  • 在正確性尚未驗證前就將「快速」視為完成。
  • 忘記輪詢正在執行的工作階段。
  • 在成功摘要中隱藏跳過的檢查。

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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