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 serejaris/kimi-skills --skill auto-hypothesis-testgit clone --depth 1 https://github.com/serejaris/kimi-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/serejaris/kimi-skills/auto-hypothesis-test)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/auto-hypothesis-test"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/auto-hypothesis-test/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/serejaris/kimi-skills/auto-hypothesis-test"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/auto-hypothesis-test.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 | $0.00076 | $0.01342 |
| Opus 5 | $0.00038 | $0.00671 |
| Sonnet 5 | $0.00015 | $0.00268 |
| Haiku 4.5 | $0.00008 | $0.00134 |
Grade A, and why
auto-hypothesis-test 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
auto-hypothesis-test
Automated statistical testing tool — automatically selects the appropriate hypothesis test based on your data characteristics (t-test / chi-square / ANOVA / Mann-Whitney, etc.) and outputs results with plain-language interpretations.
Capabilities
| Feature | Description |
|---|---|
| Independent samples t-test | 2 groups + normal data, compare means |
| Welch's t-test | 2 groups + normal but unequal variances |
| Mann-Whitney U | 2 groups + non-normal data (nonparametric) |
| One-way ANOVA | 3+ groups + normal data |
| Kruskal-Wallis | 3+ groups + non-normal data (nonparametric) |
| Chi-square independence test | Association between two categorical variables |
| Paired t-test | Before/after comparison (normal) |
| Wilcoxon signed-rank | Before/after comparison (nonparametric) |
| Auto-selection | Automatically chooses based on group count, normality, and data type |
| Plain-language interpretation | Every metric and conclusion explained in everyday language |
Quick Start
# Group comparison (auto-selects the test)
python3 scripts/statistical_test_suite.py data.csv --group treatment --value score
# Chi-square test (two categorical variables)
python3 scripts/statistical_test_suite.py survey.csv --group gender --value preference
# Paired test (before/after comparison)
python3 scripts/statistical_test_suite.py experiment.csv --col1 pre_score --col2 post_score --paired
# Force a specific test
python3 scripts/statistical_test_suite.py data.csv --group group --value score --test mann-whitney
# Save results to JSON
python3 scripts/statistical_test_suite.py data.csv -g treatment -v score -o result.json
Detailed Usage
Mode 1: Group Comparison
Use --group to specify the grouping column and --value to specify the comparison column. The tool automatically determines which test to use.
python3 scripts/statistical_test_suite.py <data-file> --group <group-col> --value <value-col> [options]
What ships with it
2 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 · 128 lines · 76 tokens per session scan A ca7bc95d56b5
auto-hypothesis-test is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 76 tokens to every session and 1,342 once invoked, about $0.0004 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 2026-08-31.
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