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 axiom-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/axiom-negation)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/axiom-negation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/axiom-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/axiom-negation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/axiom-negation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 59 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00026 | $0.00674 |
| Opus 5 | $0.00013 | $0.00337 |
| Sonnet 5 | $0.00005 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
axiom-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 9d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Axiom Negation
Identify and suspend fundamental assumptions via de Bono PO.
State Ledger
| Resource | Target | Current | % |
|---|---|---|---|
| web-search | 20 | 0 | 0% |
| web-research | 5 | 0 | 0% |
| paper-overview | 20 | 0 | 0% |
| paper-search | 12 | 0 | 0% |
| paper-research | 5 | 0 | 0% |
HARD-GATE
Cannot exit strategy until ≥80% of each budget line is consumed OR yield targets are met with justification for remaining budget.
Available Tactics
| Tactic | Role |
|---|---|
| provocation-generation | Generate PO provocations from negated axioms |
| assumption-enumeration | Surface assumptions to negate |
Available SOPs
| SOP | Role |
|---|---|
| assumption-perturbation | Perturb each axiom and observe consequences |
| reversal-generation | Generate formal negations of axioms |
| constructive-rebellion | Build alternatives from negated axioms |
| destruction-synthesis | Synthesize negation outputs |
Execution Guidance
- Surface axioms: Use assumption-enumeration tactic to identify paradigmatic assumptions
- Apply PO: For each axiom, state "PO: [negation]" — suspend judgment
- Perturb: Run assumption-perturbation to observe system response under negation
- Extract movement: Use provocation-generation to find constructive directions
- Build alternatives: Use constructive-rebellion to form viable solutions
- Synthesize: Produce structured output via destruction-synthesis
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| creative-ideation-assumption-enumeration | Surface, perturb, and prioritize assumptions by disruption potential. Orchestrates assumption surfacing → perturbation → sacred cow identification → prioritization. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| constructive-rebellion | Build constructive alternatives from destructive negation. Transform violated assumptions into viable innovation directions. |
| creative-ideation-assumption-perturbation | Perturb each assumption, observe system response. Systematic stress-testing of assumptions to reveal fragility and opportunity. |
| destruction-synthesis | Synthesize all assumption destruction outputs into structured destructive innovation report. |
| reversal-generation | Systematically reverse positive statements to generate creative inversions. Produces reversed statements with initial associations. |
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.
- 9d ago First seen · 81 lines · 26 tokens per session scan A eb6c56ea9e7f
axiom-negation is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (462 stars, last pushed 3d ago), licensed Apache-2.0. It adds 26 tokens to every session and 674 once invoked, about $0.0001 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 2026-09-03.
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