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 dralkh/iktinah --skill statistical-powergit clone --depth 1 https://github.com/dralkh/iktinahWrote 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/dralkh/iktinah/statistical-power)<a href="https://agentmods.dev/skills/dralkh/iktinah/statistical-power"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/statistical-power/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/dralkh/iktinah/statistical-power"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/statistical-power.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 | $0.00190 | $0.03445 |
| Opus 5 | $0.00095 | $0.01723 |
| Sonnet 5 | $0.00038 | $0.00689 |
| Haiku 4.5 | $0.00019 | $0.00345 |
Grade A, and why
statistical-power 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 7d 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.
This is a copy
89% identical to statistical-power — 21 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Power & Sample Size
Overview
Power analysis answers one of the most consequential questions in study planning: how large a sample do you need to reliably detect an effect of a given size, and what could you detect with the sample you can afford? An underpowered study wastes resources and produces inconclusive or irreproducible results; an overpowered one wastes participants, money, and (in clinical work) exposes more people to risk than necessary. Getting this right before data collection is the single highest-leverage statistical decision in a project.
Four quantities are locked together for any given test: sample size (n), effect size, significance level (α), and power (1 − β). Fix any three and the fourth is determined. Every calculation in this skill is some rearrangement of that relationship.
This skill covers the two ways to do power analysis:
- Closed-form formulas (fast, exact for standard tests) — see
references/closed_form_recipes.md. - Simulation / Monte Carlo (works for any design or model you can simulate and analyze) — see
references/simulation_based_power.md.
For choosing and converting effect sizes — usually the hardest part — see references/effect_sizes.md.
When to Use This Skill
- Determining required sample size before collecting data (a priori power analysis)
- Finding the minimum detectable effect (MDE) for a fixed, already-determined sample size
- Producing power curves (power vs. n, or power vs. effect size) for a grant or protocol
- Justifying a sample size for an IRB submission, grant, or pre-registration
- Powering designs with unequal group sizes or non-1:1 allocation
- Powering anything without a textbook formula (mixed models, logistic/Poisson regression, cluster-randomized trials, survival analysis, mediation, interactions) via simulation
- Accounting for multiple comparisons, attrition/dropout, or clustering in the sample-size estimate
Installation
Use uv. Pin versions in production; unpinned is fine for exploration.
What ships with it
5 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.
- 7d ago First seen · 199 lines · 190 tokens per session scan A 40f9c8bd0cc9
statistical-power is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 2mo ago), licensed MIT. It adds 190 tokens to every session and 3,445 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to statistical-power, differing in 21 lines, and is treated as a copy.
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