What is 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.
affaan-m/ECC
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.
npx skills add https://github.com/affaan-m/ECC --skill "skills/agent-sort"Quick start
Install it or open the source, trigger it with a clear task, then follow the source workflow.
npx skills add https://github.com/affaan-m/ECC --skill "skills/agent-sort"Use agent-sort to help me with: [describe your task]. Before you begin, tell me what input you need, the steps you will follow, and the expected output.
5 key workflow steps, examples, and cautions are distilled below.
Continue to the workflowDirect answers
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.
It is relevant to workflows involving Operations.
SkillSignal detected this source-specific command: npx skills add https://github.com/affaan-m/ECC --skill "skills/agent-sort". Inspect the repository and command before running it.
The upstream source does not declare a dedicated Agent platform.
Static analysis detected read-files signals. Review the cited source lines before installing; these signals are not a security audit.
This page combines upstream documentation with deterministic repository, quality, and static-risk signals. It is not described as a manual test or security review.
SkillSignal brief
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.
Useful in these contexts
Core capabilities
Distilled from the source
About 4 min · 10 sections
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
1. Read the repo
2. Build the evidence table
3. Decide DAILY vs LIBRARY
4. Build the install plan
5. Create the optional library router
Return the result in this order:
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
並行リポジトリ対応のレビューパスを使用して、スキル、コマンド、ルール、フック、エクストラを DAILY と LIBRARY のバケットに分類することで、特定のリポジトリ向けのエビデンスに基づいた ECC インストール計画を構築します。プロジェクトが完全なバンドルをロードする代わりに実際に必要なものに ECC をトリミングする必要がある場合に使用します。
通过将技能、命令、规则、钩子和额外内容并行进行仓库感知审查,为特定仓库构建基于证据的 ECC 安装计划,将其分为 DAILY 和 LIBRARY 两类。当 ECC 应精简为项目实际所需而非加载完整包时使用。
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
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.
Produce these artifacts in order:
skill-library router if the project wants oneUse two buckets only:
DAILY
LIBRARY
Use repo-local evidence before making any classification:
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
If parallel subagents are available, split the review into these passes:
agents/*skills/*commands/*rules/*If subagents are not available, run the same passes sequentially.
Establish the real stack before classifying anything:
For every candidate surface, record:
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
Promote to DAILY when:
Demote to LIBRARY when:
Translate the classification into action:
.claude/skills/skill-libraryIf the repo already uses selective installs, update that plan instead of creating another system.
If the project wants a searchable library surface, create:
.claude/skills/skill-library/SKILL.mdThat router should contain:
Do not duplicate every skill body inside the router.
After the plan is applied, verify:
Return a compact report with:
If the next step is interactive installation or repair, hand off to:
configure-eccIf the next step is overlap cleanup or catalog review, hand off to:
skill-stocktakeIf the next step is broader context trimming, hand off to:
strategic-compactReturn 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