deck-cleanup

A cleanup procedure for reducing one selected kind of clutter in the AgentDeck codebase. It covers unnecessary prose, unused code, duplicate helpers, or inconsistent coding patterns.

In plain words
What is it for?
It is for auditing and removing one category of codebase entropy in an isolated worktree, with checks for references and concurrent work.
Why use it?
It keeps cleanup changes narrow and evidence-based, reducing the risk of changing unrelated behaviour during a refactor.

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/agentdecksdk/agentdeck/deck-cleanup
Any agent
npx skills add agentdecksdk/agentdeck --skill deck-cleanup
Clone the repo
git clone --depth 1 https://github.com/agentdecksdk/agentdeck

Made for: Claude Code, Codex.

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 498 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 $0.00048 $0.00498
Opus 5 $0.00024 $0.00249
Sonnet 5 $0.00010 $0.00100
Haiku 4.5 $0.00005 $0.00050

Measured 2d ago against content hash abbddeeaa769, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deck-cleanup 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.

.agents/skills/deck-cleanup/SKILL.md · 49 lines

How it starts

The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deck Cleanup

Remove entropy without changing unrelated behavior.

Scope

  • Classify findings as narrative prose, dead code, duplicate helpers, or pattern drift.
  • A broad audit may inspect every category, but each category gets a separate branch and review path.
  • Inspect active branches and worktrees before choosing files. Avoid paths under concurrent development.
  • Work in an isolated worktree and preserve unrelated user changes.
  • For an interactive cleanup, propose and apply one small block at a time.

Evidence

Narrative prose

Run uv run scripts/slopcheck.py --all <file> on one file at a time. The tool does not replace manual review.

Delete prose when the code remains clear. Keep only concise public contracts or non-obvious rationale involving correctness, concurrency, security, compatibility, or external-system behavior. Each source file keeps one focused top-level description.

Read references/narrative-prose.md for the deletion and detection rubric.

Dead code

Build the symbol map with uv run scripts/repomap.py, then verify references outside the definition's module and tests. A symbol is removable only after checking dynamic registration, exports, compatibility migrations, and framework discovery.

Duplicate helpers

Require evidence of the same responsibility, not merely similar syntax. Prefer the architectural owner named by docs/engineering/architecture.md. Do not centralize trivial provider-local helpers or create adapter-to-adapter dependencies.

Pattern drift

Cite the binding engineering rule and the canonical implementation. Behavior changes require contract tests and a branch separate from prose cleanup.

Change discipline

  • Prefer deletion over rewriting.
  • Keep issue history, removed designs, review arguments, and changelog narration out of code.
  • Add docs/patterns guidance only for a repeated implementation shape not already covered by engineering standards.
  • Run focused checks after each file and make check before completion.
  • Use a read-only reviewer when an independent audit is requested. The reviewer names patterns and evidence but does not edit.
  • Commit, push, and open a PR only with explicit authorization for each action.
  • Nothing found means no change. Report the scanned scope and evidence.

Read the full file on GitHub · 49 lines

Files

What ships with it

2 files 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 · 49 lines · 48 tokens per session scan A abbddeeaa769

Subscribe to this mod's changes

deck-cleanup is a skill published in the GitHub repository agentdecksdk/agentdeck (2 stars, last pushed 3d ago), licensed MIT. It adds 48 tokens to every session and 498 once invoked, about $0.0002 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.