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.
npx agentmods add skills/andyzengmath/quantum-loop/ql-deslopnpx skills add andyzengmath/quantum-loop --skill ql-deslopgit clone --depth 1 https://github.com/andyzengmath/quantum-loopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/andyzengmath/quantum-loop/ql-deslop)<a href="https://agentmods.dev/skills/andyzengmath/quantum-loop/ql-deslop"><img src="https://agentmods.dev/badge/skills/andyzengmath/quantum-loop/ql-deslop.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00083 | $0.02531 |
| Opus 5 | $0.00042 | $0.01265 |
| Sonnet 5 | $0.00017 | $0.00506 |
| Haiku 4.5 | $0.00008 | $0.00253 |
Grade A, and why
ql-deslop 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.
How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ql-deslop — post-review AI-slop cleanup
Purpose
Review approval does not mean the code is clean. LLM-authored code routinely introduces:
- Duplication of helpers / types / imports under slightly different names.
- Dead code — functions added but never called; exports that no consumer uses.
- Needless abstraction — factory for one caller; wrapper that only aliases; configurable field never varied.
- Boundary violations — private functions accessed via string names; cross-module reaches that skip the public API.
- Missing tests — paths and branches not exercised by any test.
Per Schreiber & Tippe 2025, 12.1% of AI-generated files contain ≥1 CWE, and per Zhong et al. 2026 (278k conversations) AI suggestions increase cyclomatic complexity 10-50× more than human edits. Without an explicit cleanup step, every autonomous run accumulates slop.
ql-deslop adds an explicit cleanup pass AFTER the story's review gate passes but BEFORE the merge to the feature branch. Borrowed directly from OMC Ralph Steps 7.5 (deslop) + 7.6 (regression re-verify).
When to use
- Automatically, as a mandatory step between
ql-reviewPASS and thegit mergeinql-execute. - Manually, at any point after a story is implemented, to clean up before review.
- After absorbing a foreign branch (CPC promotion, cherry-pick) — the absorbed work may carry slop.
Opt-out
Single flag: --no-deslop. Used only when:
- The story is a pure test addition (nothing to clean).
- The deslop pass itself was run earlier in the same wave on the same files.
- The user explicitly demands skip (rare; recorded in commit trailer
Deslop: skipped | <reason>).
Scope — strict file-list only
The deslop pass operates on git diff --name-only BASE_SHA..HEAD_SHA — the EXACT files the story touched. It never broadens scope silently. Reaching into unchanged files is an anti-pattern and is explicitly forbidden.
Smell taxonomy — 5 categories
1. Duplication
Detectors:
- Identifier similarity across changed files: extract top-level symbol names; flag near-duplicates (e.g.,
parseRequest/parseReq/handleParse) via Levenshtein or canonical-alpha-rename + exact match (per HyClone arXiv:2508.01357). - Function-body AST hash: for each function in the diff, compute tree-sitter AST hash after alpha-renaming; flag duplicates across files.
- Import redundancy: same module imported as different names in sibling files.
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.
- 3d ago First seen · 197 lines · 0 tokens per session scan A e3d56d61914b
ql-deslop is a skill published in the GitHub repository andyzengmath/quantum-loop (24 stars, last pushed 2mo ago), licensed MIT. It adds 83 tokens to every session and 2,531 once invoked, about $0.0004 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 2026-08-30.
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