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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.

DeepseekModel 官方收录技能 ★ 精选 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=affaan-m-ecc-agents-skills-agent-sort-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name agent-sort description 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. 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: DAILY inventory LIBRARY inventory install plan verification report 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: Agents classify agents/* Skills classify skills/* Commands classify commands/* Rules classify rules/* Hooks and scripts classify hook surfaces, MCP health checks, helper scripts, and OS compatibility 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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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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