agent-sort

A repository-specific plan for choosing which coding-agent skills, commands, rules, and automatic checks a project actually needs.

In plain words
What is it for?
Use it to separate everyday tools from optional reference tools, create an install plan, and verify the result.
Why use it?
It reduces noise from installing a large collection of unrelated tools and bases the choices on evidence from the codebase.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/mturac/everything-openai-codex/agent-sort
Any agent
npx skills add mturac/everything-openai-codex --skill agent-sort
Clone the repo
git clone --depth 1 https://github.com/mturac/everything-openai-codex

Made for: Claude Code, Codex.

Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,306 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 97% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00058 $0.01306
Opus 5 $0.00029 $0.00653
Sonnet 5 $0.00012 $0.00261
Haiku 4.5 $0.00006 $0.00131

Measured 3d ago against content hash 2303f5e763f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agent-sort scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

97% identical to agent-sort — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/agent-sort/SKILL.md · 215 lines

How it starts

The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 215 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 215 lines · 58 tokens per session scan A 2303f5e763f5

Subscribe to this mod's changes

agent-sort is a skill published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 9d ago), licensed MIT. It adds 58 tokens to every session and 1,306 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to agent-sort, differing in 14 lines, and is treated as a copy.

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