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agent-sort

通过并行、可复现的仓库感知审查,为特定仓库构建基于证据的 ECC 安装计划,将技能、命令、规则、钩子和额外项分类到 DAILY 与 LIBRARY 两个存储桶中。当 ECC 应精简为项目实际所需而非加载完整包时使用。

agent-sort 是什么?

Agent Sort 是一个 AI Agent 技能(Skill),专为 Claude Code、Cursor、Codex 等平台设计,旨在帮助开发者为特定代码仓库创建精简且证据驱动的 ECC(增强型编码能力)安装计划。其核心价值在于,当完整安装 ECC 导致噪声过大或资源浪费时,通过系统化的并行审查,将 ECC 组件(如技能、命令、规则、钩子、脚本等)分类为 DAILY(每日工作流必加载)和 LIBRARY(保留但非默认加载)两个存储桶。该技能强制要求所有分类决策必须基于仓库的实际代码证据(如文件扩展名、包管理器、框架配置、CI 配置等),而非主观偏好。它支持并行子代理来加速审查,并生成包括 DAILY 清单、LIBRARY 清单、安装计划、验证报告以及可选的技能库路由器的有序输出。通过减少加载的冗余组件,Agent Sort 能够显著提升开发环境的相关性和效率,同时通过 LIBRARY 机制确保组件仍可通过搜索或手动方式访问,实现灵活性与资源利用的平衡。

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

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

Agent Sort

当仓库需要项目特定的 ECC 界面而非默认完整安装时,使用此技能。

目标不是猜测“什么感觉有用”。目标是根据实际代码库的证据对 ECC 组件进行分类。

何时使用

  • 项目只需要 ECC 的一个子集,完整安装会产生过多噪声
  • 仓库的技术栈明确,但没有人想手动逐个筛选技能
  • 团队希望获得一个可重复的安装决策,该决策基于 grep 证据而非主观意见
  • 需要将始终加载的日常工作流界面与可搜索的库/参考界面分开
  • 仓库已偏离到错误的语言、规则或钩子组合,需要清理

不可妥协的规则

  • 使用当前仓库作为事实来源,而非通用偏好
  • 每个 DAILY 决策必须引用具体的仓库证据
  • LIBRARY 并不意味着“删除”;它意味着“保持可访问,但不默认加载”
  • 请勿安装当前仓库无法使用的钩子、规则或脚本
  • 优先使用 ECC 原生界面;不要引入第二个安装系统

产出

按顺序生成以下工件:

  1. DAILY 清单
  2. LIBRARY 清单
  3. 安装计划
  4. 验证报告
  5. 可选的 skill-library 路由器(如果项目需要)

分类模型

仅使用两个存储桶:

  • DAILY
    • 应为该仓库的每个会话加载
    • 与仓库的语言、框架、工作流或操作界面高度匹配
  • LIBRARY
    • 值得保留,但默认加载不值得
    • 应可通过搜索、路由器技能或选择性手动使用来访问

证据来源

在进行任何分类之前,使用仓库本地的证据:

  • 文件扩展名
  • 包管理器和锁文件
  • 框架配置
  • CI 和钩子配置
  • 构建/测试脚本
  • 导入和依赖清单
  • 明确描述技术栈的仓库文档

有用的命令包括:

rg --files
rg -n "typescript|react|next|supabase|django|spring|flutter|swift"
cat package.json
cat pyproject.toml
cat Cargo.toml
cat pubspec.yaml
cat go.mod

并行审查遍次

如果并行子代理可用,将审查分为以下遍次:

  1. 代理(Agents)
    • 分类 agents/*
  2. 技能(Skills)
    • 分类 skills/*
  3. 命令(Commands)
    • 分类 commands/*
  4. 规则(Rules)
    • 分类 rules/*
  5. 钩子和脚本(Hooks and scripts)
    • 分类钩子界面、MCP 健康检查、辅助脚本和操作系统兼容性
  6. 额外项(Extras)
    • 分类上下文、示例、MCP 配置、模板和指导文档

如果子代理不可用,则顺序运行相同的遍次。

核心工作流

1. 读取仓库

在分类任何内容之前,确定真实的技术栈:

  • 使用的语言
  • 使用的框架
  • 主要包管理器
  • 测试栈
  • 代码检查/格式化栈
  • 部署/运行时界面
  • 已存在的操作人员集成

2. 构建证据表

对于每个候选界面,记录:

  • 组件路径
  • 组件类型
  • 建议的存储桶
  • 仓库证据
  • 简短理由

使用此格式:

skills/frontend-patterns | skill | DAILY | 84 个 .tsx 文件,存在 next.config.ts | 核心前端栈
skills/django-patterns   | skill | LIBRARY | 无 .py 文件,无 pyproject.toml       | 此仓库未使用
rules/typescript/*       | rules | DAILY | package.json + tsconfig.json            | 活跃的 TS 仓库
rules/python/*           | rules | LIBRARY | 零个 Python 源文件             | 仅保持可访问

3. 决定 DAILY 还是 LIBRARY

升级为 DAILY,当:

  • 仓库明显使用匹配的技术栈
  • 该组件足够通用,能帮助每个会话
  • 仓库已依赖相应的运行时而工作流

降级为 LIBRARY,当:

  • 组件与技术栈不符
  • 仓库日后可能需要它,但不是每天都需要
  • 它增加了上下文开销,却没有直接相关性

4. 构建安装计划

将分类转化为行动:

  • DAILY 技能 -> 安装或保留在 .claude/skills/ 中
  • DAILY 命令 -> 仅当仍然有用时,才保留为显式 shim
  • DAILY 规则 -> 仅安装匹配的语言集
  • DAILY 钩子/脚本 -> 仅保留兼容的
  • LIBRARY 界面 -> 通过搜索或 skill-library 保持可访问

如果仓库已经使用选择性安装,则更新该计划,而不是创建另一个系统。

5. 创建可选库路由器

如果项目需要可搜索的库界面,请创建:

  • .claude/skills/skill-library/SKILL.md

该路由器应包含:

  • DAILY 与 LIBRARY 的简短说明
  • 分组的触发关键词
  • 库引用的位置

不要在路由器内复制每个技能体。

6. 验证结果

应用计划后,验证:

  • 每个 DAILY 文件是否在预期位置存在
  • 过时的语言规则是否未被激活
  • 不兼容的钩子是否未安装
  • 最终安装是否确实匹配仓库的技术栈

返回一个紧凑的报告,包含:

  • DAILY 计数
  • LIBRARY 计数
  • 已移除的过时界面
  • 未解决的问题

交接

如果下一步是交互式安装或修复,则交接给:

  • configure-ecc

如果下一步是重叠清理或目录审查,则交接给:

  • skill-stocktake

如果下一步是更广泛的上下文裁剪,则交接给:

  • strategic-compact

输出格式

按此顺序返回结果:

STACK
- 语言/框架/运行时总结

DAILY
- 始终加载的项目及其证据

LIBRARY
- 可搜索/参考的项目及其证据

INSTALL PLAN
- 应安装、移除或路由的内容

VERIFICATION
- 已执行的检查和剩余缺口

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.

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.

api-connector-builder

Build a new API connector or provider by matching the target repo

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