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-excavationgit 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-excavation)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-excavation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-excavation/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-excavation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/assumption-excavation.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 47 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 | $0.00473 |
| Opus 5 | $0.00014 | $0.00236 |
| Sonnet 5 | $0.00005 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
assumption-excavation 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assumption Excavation
A three-phase tactic that surfaces hidden assumptions, challenges each one adversarially, and maps which assumptions are load-bearing for the conclusion. Decisions often rest on unstated beliefs — this tactic makes them explicit and tests their strength.
Stages
- Assumption Extraction — Systematically surface all assumptions underlying the decision, with confidence levels
- Assumption Challenge — For each assumption, construct the strongest counter-argument and identify alternatives
- Conclusion Sensitivity — Map which assumptions, if wrong, would change the conclusion
Available SOPs
| SOP | Phase | Purpose |
|---|---|---|
| assumption-extraction | Extract | Surface hidden assumptions with confidence |
| assumption-challenge | Challenge | Attack each assumption adversarially |
| conclusion-sensitivity | Sensitivity | Map load-bearing assumptions |
Execution Guidance
- Extract minimum 5 assumptions per decision
- Challenge ALL assumptions, not just obvious ones
- Confidence levels: HIGH (>80%), MEDIUM (50-80%), LOW (<50%)
- Critical assumption = conclusion changes if assumption is wrong
- Focus mitigation efforts on critical + low-confidence assumptions
Minimum Yield
-
= 5 assumptions extracted with confidence levels
- Challenge argument for each assumption
- Alternative assumption for each (what if the opposite is true?)
- Sensitivity map showing which assumptions are critical
- List of critical assumptions requiring mitigation
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| conclusion-sensitivity | Map which assumptions are load-bearing by assessing how the conclusion changes if each assumption fails. |
| convergence-assumption-challenge | Construct the strongest counter-argument against a specific assumption and propose alternatives. |
| convergence-assumption-extraction | Systematically surface hidden assumptions underlying a decision with confidence levels. |
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 · 60 lines · 27 tokens per session scan A 520f033bc039
assumption-excavation 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 473 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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