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cccskills
免费在 GitHub 公开中文介绍

agent-sort

根据项目代码和配置,筛选适用的ECC技能、规则、钩子等组件,将其分为每次加载和按需查阅两类,并整理分类依据、安装计划及安装后的验证结果。

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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 评分100分/ 100查看安装方法

适用场景

  • 筛选适合项目的ECC组件
  • 区分常用技能与参考技能
  • 整理过时规则和不兼容钩子
  • 记录安装决策的依据

中文介绍

功能

选择项目需要的ECC组件,并编写有依据的安装计划。检查代码文件扩展名、依赖关系、框架配置,以及构建和测试流程,对技能、命令、规则、钩子等进行分类。每次会话都应加载的组件归入DAILY,需要时再搜索或查阅的组件归入LIBRARY。最终整理两类清单、安装计划和安装后的验证结果。

适用场景

适合完整安装ECC后组件过多,或项目中仍保留未使用语言的规则时使用。团队希望根据实际文件和配置,而非个人偏好决定安装方案时,也可以用它记录分类依据,让安装决策有据可查。

使用示例

  • “筛选这个项目需要的ECC组件,整理分类依据和安装计划。”
  • “区分每次都加载的技能与按需查阅的技能。”
  • “准备一个能搜索参考技能的skill-library。”

注意事项

分类需要查阅目标仓库的代码和配置。LIBRARY并不表示删除,而是保留为可访问的参考资料。环境不支持并行审查时,按顺序完成相同的检查。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

客观评估

AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。

指令清晰度能否看出何时使用、做什么
A良好明确依据目标仓库,将ECC组件分为DAILY和LIBRARY。
步骤具体性步骤、输入输出和示例是否具体
A良好提供从仓库调查到验证的六个步骤、依据表示例和报告输出顺序。
安全性发送数据、删除文件、执行命令等是否有说明
A良好明确禁止删除LIBRARY项,或安装仓库无法使用的钩子、规则和脚本。
安装难易度所需工具和 API 密钥是否少且有说明
A良好列出调查用的rg和cat,并说明无并行代理时按顺序执行。

总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(1)

  • SKILL.md5.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Agent Sort

Use this skill when a repo needs a project-specific ECC surface instead of the default full install.

The goal is not to guess what "feels useful." The goal is to classify ECC components with evidence from the actual codebase.

When to Use

  • A project only needs a subset of ECC and full installs are too noisy
  • The repo stack is clear, but nobody wants to hand-curate skills one by one
  • A team wants a repeatable install decision backed by grep evidence instead of opinion
  • You need to separate always-loaded daily workflow surfaces from searchable library/reference surfaces
  • A repo has drifted into the wrong language, rule, or hook set and needs cleanup

Non-Negotiable Rules

  • Use the current repository as the source of truth, not generic preferences
  • Every DAILY decision must cite concrete repo evidence
  • LIBRARY does not mean "delete"; it means "keep accessible without loading by default"
  • Do not install hooks, rules, or scripts that the current repo cannot use
  • Prefer ECC-native surfaces; do not introduce a second install system

Outputs

Produce these artifacts in order:

  1. DAILY inventory
  2. LIBRARY inventory
  3. install plan
  4. verification report
  5. optional skill-library router if the project wants one

Classification Model

Use two buckets only:

  • DAILY
    • should load every session for this repo
    • strongly matched to the repo's language, framework, workflow, or operator surface
  • LIBRARY
    • useful to retain, but not worth loading by default
    • should remain reachable through search, router skill, or selective manual use

Evidence Sources

Use repo-local evidence before making any classification:

  • file extensions
  • package managers and lockfiles
  • framework configs
  • CI and hook configs
  • build/test scripts
  • imports and dependency manifests
  • repo docs that explicitly describe the stack

Useful commands include:

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

Parallel Review Passes

If parallel subagents are available, split the review into these passes:

  1. Agents
    • classify agents/*
  2. Skills
    • classify skills/*
  3. Commands
    • classify commands/*
  4. Rules
    • classify rules/*
  5. Hooks and scripts
    • classify hook surfaces, MCP health checks, helper scripts, and OS compatibility
  6. Extras
    • classify contexts, examples, MCP configs, templates, and guidance docs

If subagents are not available, run the same passes sequentially.

Core Workflow

1. Read the repo

Establish the real stack before classifying anything:

  • languages in use
  • frameworks in use
  • primary package manager
  • test stack
  • lint/format stack
  • deployment/runtime surface
  • operator integrations already present

2. Build the evidence table

For every candidate surface, record:

  • component path
  • component type
  • proposed bucket
  • repo evidence
  • short justification

Use this format:

skills/frontend-patterns | skill | DAILY | 84 .tsx files, next.config.ts present | core frontend stack
skills/django-patterns   | skill | LIBRARY | no .py files, no pyproject.toml       | not active in this repo
rules/typescript/*       | rules | DAILY | package.json + tsconfig.json            | active TS repo
rules/python/*           | rules | LIBRARY | zero Python source files             | keep accessible only

3. Decide DAILY vs LIBRARY

Promote to DAILY when:

  • the repo clearly uses the matching stack
  • the component is general enough to help every session
  • the repo already depends on the corresponding runtime or workflow

Demote to LIBRARY when:

  • the component is off-stack
  • the repo might need it later, but not every day
  • it adds context overhead without immediate relevance

4. Build the install plan

Translate the classification into action:

  • DAILY skills -> install or keep in .claude/skills/
  • DAILY commands -> keep as explicit shims only if still useful
  • DAILY rules -> install only matching language sets
  • DAILY hooks/scripts -> keep only compatible ones
  • LIBRARY surfaces -> keep accessible through search or skill-library

If the repo already uses selective installs, update that plan instead of creating another system.

5. Create the optional library router

If the project wants a searchable library surface, create:

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

That router should contain:

  • a short explanation of DAILY vs LIBRARY
  • grouped trigger keywords
  • where the library references live

Do not duplicate every skill body inside the router.

6. Verify the result

After the plan is applied, verify:

  • every DAILY file exists where expected
  • stale language rules were not left active
  • incompatible hooks were not installed
  • the resulting install actually matches the repo stack

Return a compact report with:

  • DAILY count
  • LIBRARY count
  • removed stale surfaces
  • open questions

Handoffs

If the next step is interactive installation or repair, hand off to:

  • configure-ecc

If the next step is overlap cleanup or catalog review, hand off to:

  • skill-stocktake

If the next step is broader context trimming, hand off to:

  • strategic-compact

Output Format

Return the result in this order:

STACK
- language/framework/runtime summary

DAILY
- always-loaded items with evidence

LIBRARY
- searchable/reference items with evidence

INSTALL PLAN
- what should be installed, removed, or routed

VERIFICATION
- checks run and remaining gaps

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