Agent Sort

# 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:

```bash
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:

```text
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 o