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并行执行优化器

当用户希望通过并行工作、并发代理、批量工具调用、隔离工作树或多条独立验证通道来大幅加速任务且不牺牲正确性时使用。

并行执行优化器 是什么?

并行执行优化器是一项AI代理技能,旨在通过将工作分解为独立的“通道”并利用并行执行来显著缩短任务完成时间。它要求代理在行动前明确目标并绘制依赖图,将工作标记为并行、串行或门控,然后同时运行所有独立的读取和检查操作。该技能支持多种平台,包括但不限于命令行工具、AI编程助手(如Claude Code)和CI/CD管道。其核心原则包括隔离写入操作(按文件、工作树、分支或服务)、在兼容性验证后合并结果,以及提供验证表而非模糊的性能声明。它特别适用于仓库检查、文件读取、API验证、构建/测试通道和多工作树实现等场景,同时明确禁止并行执行破坏性命令、数据库迁移或影响客户的生产部署。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

并行执行优化器 是做什么的?

当速度来自同时进行独立工作时使用此技能: 仓库检查、文件读取、API验证、浏览器检测、构建/测试通道、 部署回读或多工作树实现通道。

核心模式

在行动之前将紧急情况转化为依赖关系图。

  1. 定义目标和完成信号。
  2. 将工作分解为通道。
  3. 将每个通道标记为并行、串行或门控。
  4. 同时运行独立的读取/检查操作。
  5. 保持写入操作按文件、工作树、分支、服务或数据集隔离。
  6. 仅在证据显示通道兼容后才进行合并。
  7. 以验证表结尾,而不是模糊的速度声明。

通道矩阵

在重大推进之前,编写一个简洁的矩阵:

通道 | 可否并行执行? | 写入范围 | 风险 | 验证
仓库扫描 | 是 | 无 | 低 | rg/git status 输出
后端补丁 | 可能 | src/api | 中 | 单元测试
前端补丁 | 可能 | app/components | 中 | 浏览器截图
部署回读 | 构建之后 | 远程服务 | 高 | 实时URL + 日志

仅当通道的写入范围不冲突时才并行运行它们。

执行规则

  • 批量处理文件读取、搜索、状态检查和元数据查询。
  • 对于大型不相关的实现通道,使用隔离的工作树。
  • 在单独的会话中启动长周期测试、构建、数据回填和部署, 然后有目的地轮询它们。
  • 如果一个通道发现了会改变计划的阻塞点,则暂停依赖它的通道 并更新矩阵。
  • 除非用户明确要求一个持续运行的服务,否则永远不要让后台进程 超出当前轮次的存活时间。
  • 在没有明确门控的情况下,不要并行执行破坏性命令、迁移、对同一张表的写入, 或影响线上客户的部署。

输出格式

报告时使用以下格式:

并行执行结果:
- 运行通道数:5
- 完成通道数:4
- 阻塞通道:部署回读,等待DNS传播
- 发现的快速路径:批量仓库扫描 + 聚焦测试
- 验证:lint通过、单元测试通过、线上冒烟测试通过

失败模式

  • 过多并发导致冲突的编辑。
  • 对工具本身进行基准测试而非任务本身。
  • 在正确性得到证明之前将“快”视为完成。
  • 忘记轮询正在运行的会话。
  • 在成功摘要后隐藏跳过检查的项目。

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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