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/megaprompting/torque-loop/attacknpx skills add Megaprompting/torque-loop --skill attackgit clone --depth 1 https://github.com/Megaprompting/torque-loopWhat 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 | $0.00065 | $0.00756 |
| Opus 5 | $0.00032 | $0.00378 |
| Sonnet 5 | $0.00013 | $0.00151 |
| Haiku 4.5 | $0.00006 | $0.00076 |
Grade A, and why
attack 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 yesterday.
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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ratchet:attack — hostile validation
Self-review is theater. The model that built the artifact is the worst judge of it. This command assumes the artifact is wrong and spends its effort proving where.
Step 0 — Load state and target
ratchet status
Identify the artifact under attack (usually the last one in state). For anything that
looks finished, delegate the assault to the ratchet-auditor subagent.
The five-voice board
Speak in each voice distinctly. Do not blur them into generic "concerns".
- Impatient User — slower, harder, or more confusing than the alternative.
- Competitor — where a rival beats this and takes the user.
- Maintainer — how this rots or becomes unchangeable in six months.
- Auditor — which claims are asserted without evidence.
- Saboteur — the specific input or state that makes it fall over.
Rules
- Every objection is a concrete failure scenario: specific input/state → wrong output or crash. "Might be fragile" is not a finding; "empty input throws at step 3" is.
- Rank by severity: critical / high / medium / low. Critical = wrong result, data loss, or unusable. Order most-severe first.
- Demand evidence for every self-serving claim ("robust", "fast", "complete"). Find the contradicting case or mark the claim unproven.
- Name the smallest patch each finding needs — REMOVE / ADD / CHANGE. You specify the delta; you do not apply it here.
- Flag fog, not just defects. A finding that says the premise was wrong — a hidden
convention, wrong-by-default data, a claim that contradicts the locked target or the
unknowns-map — is fog the aperture missed, not merely a bug. Serialize it
(
ratchet state append assumptions ...with a kill test, or anopenLoopsentry prefixedfog:), and at two or more such findings recommend/ratchet:mapbefore any further patching: you are patching inside unmapped terrain.
Output contract
VERDICT: ship | patch-then-ship | do-not-ship
FINDINGS (most severe first):
- [CRITICAL] <voice> — <failure scenario> → <REMOVE/ADD/CHANGE ...>
- [HIGH] ...
UNPROVEN CLAIMS:
- "<quote>" — evidence demanded: ...
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.
- yesterday First seen · 73 lines · 65 tokens per session scan A 432e9efef3c3
attack is a skill published in the GitHub repository Megaprompting/torque-loop (5 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 756 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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