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 abductive-hypothesis-generationgit 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/abductive-hypothesis-generation)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/abductive-hypothesis-generation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/abductive-hypothesis-generation/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/abductive-hypothesis-generation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/abductive-hypothesis-generation.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 | $0.00021 | $0.00897 |
| Opus 5 | $0.00010 | $0.00449 |
| Sonnet 5 | $0.00004 | $0.00179 |
| Haiku 4.5 | $0.00002 | $0.00090 |
Grade A, and why
abductive-hypothesis-generation 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 12d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Abductive Hypothesis Generation
Inference to the best explanation in the face of anomalies: when an anomalous phenomenon that existing theory cannot explain is observed, systematically generate candidate explanations and select the most plausible one as the hypothesis.
When to Use
- A clear anomalous phenomenon is observed (a result inconsistent with existing theoretical predictions)
- Existing theory cannot adequately explain a known phenomenon
- One of several competing explanations must be selected as the most worth testing
- The research starting point is "this result is strange, why?"
Not applicable: no clear anomaly, just wanting to explore a new field → use inductive-hypothesis-generation instead.
Thinking Framework
Anomaly → Generate candidate explanations → Rank by plausibility → Best explanation = hypothesis
The core logic of abductive reasoning:
- Anomaly: precisely describe the anomaly — what phenomenon, inconsistent with what expectation, how large the deviation
- Generate candidate explanations: systematically generate all candidate explanations that can account for the anomaly (no premature filtering)
- Rank by plausibility: rank by plausibility — which explanation is most parsimonious, most consistent with known facts, most testable
- Best explanation = hypothesis: select the most plausible explanation as the working hypothesis, retaining the rest as competing hypotheses
Core principles of abduction:
- Occam's razor: when explanatory power is comparable, prefer the explanation with fewer assumptions
- Consistency: the best explanation should not contradict other known facts
- Testability: the best explanation must be able to produce observable predictions (otherwise it cannot be verified)
- Generation completeness: candidate explanations must be exhausted before ranking, to avoid premature convergence
Budget Gate
| Tier | Anomaly description | Candidate explanations | Hypothesis output | Competing hypotheses |
|---|---|---|---|---|
| S | 1 precisely described anomaly | ≥2 candidate explanations | 1 best-explanation hypothesis | ≥1 competing hypothesis retained |
| M | 1–2 anomalies | ≥3 candidate explanations | ≥2 structured hypotheses | complete plausibility ranking |
| L | ≥2 related anomalies | ≥5 candidate explanations | ≥3 structured hypotheses | complete ranking + discriminating prediction design |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 95 lines · 21 tokens per session scan A bac51e2c9178
abductive-hypothesis-generation is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (462 stars, last pushed 2d ago), licensed Apache-2.0. It adds 21 tokens to every session and 897 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 2026-08-30.
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