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 skills add yogsoth-ai/de-anthropocentric-research-engine --skill assumption-negationgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/assumption-negation)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-negation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-negation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-negation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-negation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00027 | $0.00575 |
| Opus 5.5 | $0.00011 | $0.00230 |
| Sonnet 5.5 | $0.00005 | $0.00115 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
assumption-negation 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- assumption-negation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assumption Negation
Tactics
- contradiction-derivation
- counterexample-heuristics
Method
- Extract the core claim or assumption from the artifact
- Formally negate it (produce ~P from P)
- Derive logical consequences of ~P through deductive chains
- Evaluate whether derivation reaches genuine contradiction
- If contradiction found: original claim survives this test
- If no contradiction: claim may be contingent, not necessary
Budget
| Size | Negation chains | Max derivation depth |
|---|---|---|
| S | 3 | 5 steps |
| M | 6 | 8 steps |
| L | 10 | 12 steps |
Orchestration
- Dispatch
claim-negationto produce formal negation - For each negation, dispatch
deductive-chainto derive consequences - Dispatch
contradiction-detectionto evaluate results - If no contradiction, dispatch
claim-refinementfor weakened version
Subagents
- claim-negation
- deductive-chain
- contradiction-detection
- claim-refinement
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| contradiction-derivation | Negate a claim, derive logical consequences step by step, detect whether a genuine contradiction or absurdity emerges. |
| counterexample-heuristics | Generate counterexamples (monsters), attempt monster-barring, incorporate surviving counterexamples as lemma refinements (Lakatos method). |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| claim-negation | Formally negate the core claim, producing the logical complement for reductio testing. |
| claim-refinement | Propose a refined claim that survives counterexamples while preserving maximum explanatory power (Lakatos lemma-incorporation). |
| contradiction-detection | Evaluate whether a derivation chain has reached a genuine contradiction, absurdity, or inconclusive state. |
| deductive-chain | Derive logical consequences step by step from a given premise, building a traceable derivation chain. |
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.
- 1mo ago First seen · 78 lines · 27 tokens per session scan A 41d02b36ccf3
assumption-negation is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (505 stars, last pushed 5d ago), licensed Apache-2.0. It adds 27 tokens to every session and 575 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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