work-clean

A read-only code-cleanup analysis that produces a self-contained simplification plan for another model to carry out. It focuses on code smells, unnecessary abstractions, obsolete paths, and package choices.

In plain words
What is it for?
Use it when assessing whether code can be simplified, refactored, or stripped of unnecessary helpers, compatibility paths, or dependencies.
Why use it?
It separates investigation from editing, so proposed deletions and refactors must be supported by evidence before anyone changes the code.

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/xcaeser/work-skill/work-clean
Any agent
npx skills add xcaeser/work-skill --skill work-clean
Clone the repo
git clone --depth 1 https://github.com/xcaeser/work-skill

Made for: Claude Code, Codex.

Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,303 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00065 $0.01303
Opus 5 $0.00032 $0.00651
Sonnet 5 $0.00013 $0.00261
Haiku 4.5 $0.00006 $0.00130

Measured 2d ago against content hash b70eaf67dbe6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

work-clean 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 2d 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.

skills/work-clean/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Work / 8. Clean

Find defensible simplifications without editing. The Sol xhigh analyst remains read-only; the parent owns every cleanup decision and any later execution route.

Preserve reliability, restraint, user data, meaningful tests, and the simplest complete implementation. Remove obsolete paths instead of preserving compatibility, and require evidence for every deletion or abstraction change.

Fixed route

  • If the exact subagent model, effort, or goal tools are unavailable, return blocked with the missing capability. Do not substitute a model or perform the cleanup analysis in the parent.
  • Launch exactly one gpt-5.6-sol subagent at xhigh effort with fork_turns: none.
  • Give it a fun call sign, the exact task statement, exact scope, source of truth, behavior boundaries, package constraints, acceptance checks, and validation requirements.
  • Require independent analysis and concrete evidence for every proposed deletion or shared component; do not accept a status report, speculative smell, or “routine” migration.
  • Require it to call create_goal with the exact cleanup-analysis goal before inspection and update_goal complete only after the required checks pass.
  • Let the analyst run until it returns the cleanup plan, a real blocker, or a user-input request. Do not cancel it because a polling window or arbitrary wall-clock interval elapsed; a wait timeout is not a failure.
  • The analyst must not edit, commit, deploy, spawn, or delegate.
  • Scale depth to the scope. Inspect only relevant paths, direct consumers, and evidence needed to prove each simplification; return the plan without process narration and stop when every candidate has a supported disposition.
  • Report the successful launch as a compact table with Agent, Working on, Goal, Ownership, and Model.

Analysis

  1. Prefer an explicit path, subsystem, feature, or diff; otherwise use the smallest scope clearly implied by the request.
  2. Inspect instructions, status, relevant diffs, consumers, registrations, generated outputs, existing tests, public APIs, and actual user flows.
  3. Identify dead code, needless indirection, duplicate behavior, speculative abstractions, and unclear boundaries.
  4. Keep helpers that communicate intent, isolate side effects, improve testing, or have genuine reuse. Simplify the consumer path; do not merely move complexity into a new wrapper or hide it behind a renamed abstraction.
  5. Consider packages in this order: standard library/platform, an existing project dependency, a mature and battle-tested external package, then focused local code. Check existing dependency documentation and types before reimplementing functionality or adding a package. Choose a package only when its reliability, maintenance, license, runtime fit, and reduced complexity earn the dependency cost; record chosen and rejected options with reasons.
  6. Challenge cleanup candidates against exact behavior, error paths, partial state, retries, interruption, permissions, upgrades, and recovery before recommending removal.
  7. Preserve meaningful tests and remove only tests that are demonstrably obsolete, tautological, framework-level, or detached from a real contract. Do not add coverage-only tests.
  8. Prefer durable architecture over a stopgap intended to be replaced later. Do not propose behavior, API, persistence, security, UX, test, or type weakening without explicit authorization.

Read the full file on GitHub · 87 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. 2d ago First seen · 87 lines · 65 tokens per session scan A b70eaf67dbe6

Subscribe to this mod's changes

work-clean is a skill published in the GitHub repository xcaeser/work-skill (2 stars, last pushed 22d ago), licensed MIT. It adds 65 tokens to every session and 1,303 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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