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affaan-m/agent-sort

affaan-m

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.

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v1.1Saved Apr 20, 2026

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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Overall Score

88/100

Grade

A

Excellent

Safety

95

Quality

86

Clarity

88

Completeness

82

Summary

A meta-skill that guides an agent to audit an ECC (agent skills framework) installation against a specific repository's actual tech stack, then sort ECC components (skills, commands, rules, hooks, scripts) into DAILY (always-load) and LIBRARY (searchable/reference) buckets with evidence-backed justifications. The agent reads the repo's source files, package managers, configs, and CI setup to make classifications, then produces an install plan and verification report.

Detected Capabilities

Repository filesystem scanning and analysis (grep, ripgrep, file enumeration)Package manager and dependency detection (package.json, pyproject.toml, Cargo.toml, go.mod, pubspec.yaml)Config file parsing (tsconfig.json, CI/CD configs, build scripts)Evidence-driven classification and decision logicStructured report generation (tables, inventory lists, install plans)Parallel pass execution (agents, skills, commands, rules, hooks, extras)Integration with related skills (configure-ecc, skill-stocktake, strategic-compact)

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

trim ECC installationaudit skill stackproject-specific ECCsort skills by usageclean up stale rulesevidence-backed install planseparate daily from library

Risk Signals

INFO

Recommended shell commands use ripgrep and standard Unix tools (rg, cat, grep). These are file read operations with no destructive or escalation patterns.

## Evidence Sources section, Useful commands block
INFO

Skill makes decisions based on local repository content only, with explicit instruction 'Use the current repository as the source of truth, not generic preferences' and 'Every DAILY decision must cite concrete repo evidence.'

## Non-Negotiable Rules section
INFO

No hardcoded credentials, API keys, or secrets access patterns detected. No network calls, data exfiltration, or privilege escalation.

Full skill content

Use Cases

  • Trim ECC to match a project's actual tech stack instead of loading the full bundle
  • Audit an ECC installation for stale or off-stack rules and skills
  • Create a repeatable, evidence-backed install plan for a team instead of hand-curation
  • Separate always-loaded developer surfaces from searchable reference libraries
  • Clean up a repo that has drifted into the wrong language or framework set

Quality Notes

  • Excellent scope clarity: explicitly defines two-bucket model (DAILY vs LIBRARY) with clear promotion/demotion criteria.
  • Strong evidence-driven methodology: requires concrete repo artifacts (file extensions, config files, lockfiles) before classification, avoiding opinion-based decisions.
  • Well-structured workflow with clear six-step core process: read repo → build evidence table → decide buckets → build install plan → create optional router → verify.
  • Good guardrails: explicit 'Non-Negotiable Rules' section prevents common pitfalls (installing incompatible hooks, using off-stack rules, duplicating install systems).
  • Clear output format specified with examples and templates (evidence table format, output order).
  • Parallel execution model is well-explained with six distinct passes, allowing subagents to work independently on different component types.
  • Handoff logic is clear: delegates to configure-ecc, skill-stocktake, or strategic-compact based on next step.
  • Minor gap: no explicit error handling guidance (e.g., 'what if a component exists in DAILY but the corresponding runtime is not installed?'), though the verification step addresses this implicitly.
  • Minor gap: no guidance on conflict resolution if a component could legitimately fit both DAILY and LIBRARY (e.g., a general-purpose skill used by multiple stacks).
Model: claude-haiku-4-5-20251001Analyzed: Apr 20, 2026

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Version History

v1.1

Content updated

2026-04-20

Latest
v1.0

No changelog

2026-04-12

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