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 competing-hypothesis-constructiongit 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/competing-hypothesis-construction)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/competing-hypothesis-construction"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/competing-hypothesis-construction/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/competing-hypothesis-construction"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/competing-hypothesis-construction.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 | $0.00018 | $0.00916 |
| Opus 5 | $0.00009 | $0.00458 |
| Sonnet 5 | $0.00004 | $0.00183 |
| Haiku 4.5 | $0.00002 | $0.00092 |
Grade A, and why
competing-hypothesis-construction 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competing Hypothesis Construction
Construct multiple competing hypotheses for the same phenomenon: actively counter confirmation bias, maintain epistemic openness by building genuinely different explanations in parallel, and design decisive predictions that can distinguish them.
When to Use
- The researcher already has a "preferred hypothesis" and needs to actively challenge it
- The phenomenon has multiple plausible explanations, and premature convergence would lead in the wrong direction
- You need to show reviewers or funders that alternative explanations have been considered
- When designing experiments, you need to determine which variable best discriminates between competing explanations
Not applicable: the phenomenon has overwhelming evidence supporting a single explanation → directly use deductive-hypothesis-generation to refine that explanation.
Thinking Framework
Avoid confirmation bias by generating genuinely different explanations, then find discriminating predictions
The core logic of competing hypothesis construction:
- Force diversity: competing hypotheses must be genuinely different at the mechanism level, not variants of the same mechanism
- Symmetric treatment: each hypothesis is treated with equal seriousness; the preferred hypothesis is not allowed special treatment
- Discriminating predictions: find decisive predictions that distinguish the hypotheses — what result supports H1 but contradicts H2, and vice versa
- Matrix comparison: reveal structural differences between hypotheses through a systematic matrix
Quality criteria for competing hypotheses:
- Genuinely competing: two hypotheses give different causal explanations for the same phenomenon (not weaker/stronger versions of the same explanation)
- Mutual exclusivity: there exists at least one observable result that can support one while contradicting the other
- Comparability: both hypotheses have clear testable predictions
Budget Gate
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.
- 9d ago First seen · 94 lines · 18 tokens per session scan A d74988cb984a
competing-hypothesis-construction is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (464 stars, last pushed today), licensed Apache-2.0. It adds 18 tokens to every session and 916 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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