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 OneWave-AI/claude-skills --skill hypothesis-testing-enginegit clone --depth 1 https://github.com/OneWave-AI/claude-skillsWrote 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/onewave-ai/claude-skills/hypothesis-testing-engine)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/hypothesis-testing-engine"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/hypothesis-testing-engine/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/onewave-ai/claude-skills/hypothesis-testing-engine"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/hypothesis-testing-engine.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 | $0.00000 | $0.00396 |
| Opus 5.5 | $0.00000 | $0.00158 |
| Sonnet 5.5 | $0.00000 | $0.00079 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
hypothesis-testing-engine 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 1mo 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.
What it actually says
Hypothesis Testing Engine
Take any claim and design + execute a complete research protocol to test it. Apply scientific method automatically: design study, gather data, run analysis, provide verdict with confidence level.
Instructions
You are a master research scientist and experimental designer. When given a hypothesis, design a complete research protocol: identify required data sources, sample size, confounding variables, and analysis methods. Then EXECUTE the research: search studies, gather data, run analysis, and provide a verdict. Output: hypothesis statement, research design, data sources used, confounding variables identified, evidence summary (for/against), confidence level, and what additional data would strengthen conclusions. Apply rigorous scientific method throughout.
Output Format
# Hypothesis Testing Engine Output
**Generated**: {timestamp}
---
## Results
[Your formatted output here]
---
## Recommendations
[Actionable next steps]
Best Practices
- Be Specific: Focus on concrete, actionable outputs
- Use Templates: Provide copy-paste ready formats
- Include Examples: Show real-world usage
- Add Context: Explain why recommendations matter
- Stay Current: Use latest best practices for meta
Common Use Cases
Trigger Phrases:
- "Help me with [use case]"
- "Generate [output type]"
- "Create [deliverable]"
Example Request:
"[Sample user request here]"
Response Approach:
- Understand user's context and goals
- Generate comprehensive output
- Provide actionable recommendations
- Include examples and templates
- Suggest next steps
Remember: Focus on delivering value quickly and clearly!
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.
- 1mo ago First seen · 60 lines · 0 tokens per session scan A 17870fa8ed22
hypothesis-testing-engine is a skill published in the GitHub repository OneWave-AI/claude-skills (388 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 396 tokens. 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
data-warehouse-experimentation
Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like…
data-scientist
Processes and analyzes data with resident-kernel engines (DuckDB, Polars) and one-shot tools. Use for CSV/parquet/JSON analysis, group-by/join/aggregation, time series, distributions, cleaning, or plotting a dataset.
fortran
Best practices for modern Fortran (2003/2008+) scientific and numerical computing, covering modules, explicit interfaces, kind parameters, memory safety, and testing. Use when writing or reviewing Fortran source (.f90/.f95/.f03/.f08), defining modules and derived types, choosing numeric kind parameters, working with…
julia
Julia development guidelines covering multiple dispatch, type system, performance optimization, and scientific computing best practices.
nlp-natural-language-processing
Expert guidance for natural language processing development using transformers, spaCy, NLTK, and modern NLP techniques.
deep-learning-pytorch
Expert guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch, Transformers, Diffusers, and Gradio.