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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.

agent-sort とは?

agent-sort is a Claude Code agent skill that 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.

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

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ドキュメント

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

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/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

api-design

REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs. Use when designing or reviewing REST endpoints, resource names, status codes, pagination, or versioning.

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