Skip to content
cccskills
FreePublished on GitHubEnglish overview

agent-sort

Build a project-specific ECC installation plan using evidence from code and configuration. Sort skills, rules, hooks, and other components into items loaded every session and references kept available for occasional use.

OriginalShow the original 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.

AI rating100/ 100See how to install

Good for

  • Select ECC components that match a project's technology
  • Separate everyday tools from occasional references
  • Clean up outdated language rules and incompatible hooks
  • Document installation decisions with repository evidence

Overview

What it does

Select ECC components for a project and produce an installation plan supported by evidence. It examines file extensions, dependencies, framework settings, and build and test procedures to classify skills, commands, rules, hooks, and other components. Items to load every session go into DAILY. Items to search or consult when needed go into LIBRARY. The results include both inventories, an installation plan, and verification results after installation.

When it helps

Use it when a full ECC installation contains too many items, or when rules for languages the project no longer uses remain active. It also helps teams decide what to install based on actual project files and settings rather than individual preferences. The classification records the evidence behind those decisions.

Example requests

  • “Select the ECC components this project needs, explain the classifications, and prepare an installation plan.”
  • “Separate skills loaded every session from skills consulted only when needed.”
  • “Prepare a skill-library for finding reference skills.”

Notes

Classification requires access to the target repository's code and configuration. LIBRARY items remain accessible; they are not marked for deletion. If parallel review is unavailable, the same checks run sequentially.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt explicitly classifies ECC into DAILY and LIBRARY using evidence from the target repository.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt provides six steps from repository review to verification, an evidence table example, and a report order.
SafetyAre sending data, deleting files, and running commands disclosed?
AGoodIt prohibits deleting LIBRARY items and installing hooks, rules, or scripts that the repository cannot use.
Easy setupDoes it need few tools or API keys, and are they documented?
AGoodIt lists rg and cat for inspection and provides a sequential fallback when parallel agents are unavailable.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 19, 2026. Treat it as a guide and read the original before installing.

Disagree? Request a re-evaluation

Tell us which point is wrong and why. No login needed. We will check it against the SKILL.md, redo the evaluation, and show the result here.

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

Files included (1)

  • SKILL.md5.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

accessibility

FreeSummaryAI rating 100

Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.

  • Adding descriptive labels to icon-only buttons
  • Reviewing keyboard navigation and modal focus
  • Checking contrast and control sizes
affaan-m/ECC283.7KUpdated Oct 16, 2026

agent-architecture-audit

FreeSummaryAI rating 80

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

  • Investigating failures that appear only inside an agent application
  • Tracing old conversation content leaking into new sessions
  • Checking skipped or falsely claimed tool execution
affaan-m/ECC283.7KUpdated Oct 16, 2026

agent-eval

FreeSummaryAI rating 80

Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.

  • Comparing coding agents on real development tasks
  • Evaluating a new tool or model before adoption
  • Checking performance after agent updates
affaan-m/ECC283.7KUpdated Oct 16, 2026

agent-harness-construction

FreeSummaryAI rating 100

Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.

  • Define an AI agent’s tool set and input formats
  • Make tool results and next steps easier to interpret
  • Specify safe retries and stopping conditions
affaan-m/ECC283.7KUpdated Oct 16, 2026

agent-introspection-debugging

FreeSummaryAI rating 100

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

  • Diagnosing repeated failures in an agent run
  • Investigating loops through the same tool calls
  • Reviewing output degradation as context grows
affaan-m/ECC283.7KUpdated Oct 5, 2026

agent-introspection-debugging

FreeSummaryAI rating 100

Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.

  • Diagnosing repeated tool calls with no progress
  • Investigating quality loss as context grows
  • Checking file paths and environment mismatches
affaan-m/ECC283.7KUpdated Oct 16, 2026

See all skills by affaan-m

Report a problem with this skill