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 silverstein/claude-scientific-skills-desktop --skill hypothesis-generationgit clone --depth 1 https://github.com/silverstein/claude-scientific-skills-desktopWrote 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/silverstein/claude-scientific-skills-desktop/hypothesis-generation)<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/hypothesis-generation"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/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/silverstein/claude-scientific-skills-desktop/hypothesis-generation"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/hypothesis-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00033 | $0.01240 |
| Opus 5 | $0.00016 | $0.00620 |
| Sonnet 5 | $0.00007 | $0.00248 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
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 11d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Hypothesis Generation
Overview
Hypothesis generation is a systematic process for developing testable explanations. Formulate evidence-based hypotheses from observations, design experiments, explore competing explanations, and develop predictions. Apply this skill for scientific inquiry across domains.
When to Use This Skill
This skill should be used when:
- Developing hypotheses from observations or preliminary data
- Designing experiments to test scientific questions
- Exploring competing explanations for phenomena
- Formulating testable predictions for research
- Conducting literature-based hypothesis generation
- Planning mechanistic studies across scientific domains
Workflow
Follow this systematic process to generate robust scientific hypotheses:
1. Understand the Phenomenon
Start by clarifying the observation, question, or phenomenon that requires explanation:
- Identify the core observation or pattern that needs explanation
- Define the scope and boundaries of the phenomenon
- Note any constraints or specific contexts
- Clarify what is already known vs. what is uncertain
- Identify the relevant scientific domain(s)
2. Conduct Comprehensive Literature Search
Search existing scientific literature to ground hypotheses in current evidence. Use both PubMed (for biomedical topics) and general web search (for broader scientific domains):
For biomedical topics:
- Use WebFetch with PubMed URLs to access relevant literature
- Search for recent reviews, meta-analyses, and primary research
- Look for similar phenomena, related mechanisms, or analogous systems
For all scientific domains:
- Use WebSearch to find recent papers, preprints, and reviews
- Search for established theories, mechanisms, or frameworks
- Identify gaps in current understanding
Search strategy:
- Begin with broad searches to understand the landscape
- Narrow to specific mechanisms, pathways, or theories
- Look for contradictory findings or unresolved debates
- Consult
references/literature_search_strategies.mdfor detailed search techniques
What ships with it
4 files 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.
- 11d ago First seen · 156 lines · 33 tokens per session scan A 13a5608354a2
hypothesis-generation is a skill published in the GitHub repository silverstein/claude-scientific-skills-desktop (22 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 1,240 once invoked, about $0.0002 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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