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-destructiongit 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-destruction)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-destruction"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-destruction/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-destruction"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-destruction.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 127 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 | $0.00027 | $0.01757 |
| Opus 5 | $0.00014 | $0.00879 |
| Sonnet 5 | $0.00005 | $0.00351 |
| Haiku 4.5 | $0.00003 | $0.00176 |
Grade A, and why
assumption-destruction 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assumption Destruction
Open new solution spaces by negating, reversing, and challenging fundamental assumptions.
Strategy Routing
| Strategy | Signal Keywords |
|---|---|
| axiom-negation | negate, suspend, PO, de Bono, what if not, challenge axiom |
| reverse-brainstorming | worse, reverse, anti-solution, how to fail, sabotage |
| worst-method-inversion | worst possible, terrible solution, invert bad, flip failure |
| anti-benchmark | best practice, industry standard, benchmark, conventional wisdom |
| sacred-cow-hunting | unquestioned, sacred cow, taboo, never challenged, dogma |
Manifest
Strategies
| Strategy | Description |
|---|---|
| axiom-negation | Identify and suspend fundamental assumptions via de Bono PO |
| reverse-brainstorming | How to make it worse? → reverse for solutions |
| worst-method-inversion | Design worst possible solution → extract insights → invert |
| anti-benchmark | Challenge industry best practices' hidden assumptions |
| sacred-cow-hunting | Find and challenge domain's unquestioned beliefs |
Tactics
| Tactic | Description |
|---|---|
| provocation-generation | Generate PO provocations and extract constructive movement (shared) |
| assumption-enumeration | Surface, perturb, and prioritize assumptions by disruption potential |
| inversion-protocol | Reverse statements → extract insights → build constructive alternatives |
SOPs
| SOP | Description |
|---|---|
| assumption-perturbation | Perturb each assumption, observe system response |
| reversal-generation | Systematically reverse positive statements |
| worst-case-design | Design the worst possible solution |
| inversion-extraction | Extract constructive insights from worst solutions |
| benchmark-challenge | Identify and negate benchmark assumptions |
| sacred-cow-identification | Find domain's unquestioned beliefs |
| constructive-rebellion | Build constructive alternatives from destructive negation |
| destruction-synthesis | Synthesize all assumption destruction outputs |
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 · 167 lines · 27 tokens per session scan A c05fee7a69e0
assumption-destruction 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 27 tokens to every session and 1,757 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.
Other skills, from other repositories
Deep Research
Produce a deep, structured research report on a topic: decompose into key dimensions, analyze each with evidence and reasoning, synthesize cross-cutting insights, and surface open questions. Use for deep research, analysis, and literature/landscape reviews.
literature-review-tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use to ..." for…
papers-reading-skill
Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to…
paper-fulltext-harvest
Batch download academic paper full-text (PDF/XML) from a list of DOIs. Handles 25 DOI prefixes across 19 publisher families via three layered routes: (1) publisher TDM APIs requiring institutional subscription (Elsevier ScienceDirect, Wiley Online, Springer Nature), (2) Open Access sources (Crossref, Unpaywall…
academic-figure-generation
Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).
paper-reading
Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden…