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 agentmods add skills/wentorai/research-plugins/power-analysis-guidenpx skills add wentorai/research-plugins --skill power-analysis-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/power-analysis-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/power-analysis-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/power-analysis-guide.svg" alt="Measured on agentmods" 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 | $0.00012 | $0.02080 |
| Opus 5 | $0.00006 | $0.01040 |
| Sonnet 5 | $0.00002 | $0.00416 |
| Haiku 4.5 | $0.00001 | $0.00208 |
Grade A, and why
power-analysis-guide 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 5d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Power Analysis Guide
Calculate appropriate sample sizes for your study using power analysis, understand effect sizes, and avoid underpowered or wastefully overpowered designs.
Core Concepts
The Four Parameters of Power Analysis
Every power analysis involves four interrelated quantities. Fix any three to solve for the fourth:
| Parameter | Symbol | Definition | Typical Value |
|---|---|---|---|
| Effect size | d, r, f, etc. | Magnitude of the phenomenon you expect to detect | Varies by field |
| Significance level (alpha) | alpha | Probability of Type I error (false positive) | 0.05 |
| Statistical power (1 - beta) | 1 - beta | Probability of detecting a true effect | 0.80 or 0.90 |
| Sample size | N | Number of observations needed | Solve for this |
Error Types
| H0 is true (no effect) | H0 is false (effect exists) | |
|---|---|---|
| Reject H0 | Type I error (alpha) | Correct (power = 1 - beta) |
| Fail to reject H0 | Correct (1 - alpha) | Type II error (beta) |
Effect Size Conventions
Cohen's d (Two-Group Comparison)
d = (M1 - M2) / SD_pooled
| Size | Cohen's d | Interpretation |
|---|---|---|
| Small | 0.2 | Subtle, may need large N to detect |
| Medium | 0.5 | Noticeable, typical in social sciences |
| Large | 0.8 | Obvious, often visible without statistics |
Correlation (r)
| Size | r | r-squared |
|---|---|---|
| Small | 0.1 | 1% variance explained |
| Medium | 0.3 | 9% variance explained |
| Large | 0.5 | 25% variance explained |
Cohen's f (ANOVA)
| Size | f | Equivalent eta-squared |
|---|---|---|
| Small | 0.10 | 0.01 |
| Medium | 0.25 | 0.06 |
| Large | 0.40 | 0.14 |
Odds Ratio (Logistic Regression)
| Size | OR |
|---|---|
| Small | 1.5 |
| Medium | 2.5 |
| Large | 4.0 |
Power Analysis in Python (statsmodels)
Two-Sample t-Test
from statsmodels.stats.power import TTestIndPower
analysis = TTestIndPower()
# Solve for sample size
n = analysis.solve_power(
effect_size=0.5, # Cohen's d = medium
alpha=0.05, # Significance level
power=0.80, # 80% power
ratio=1.0, # Equal group sizes
alternative='two-sided'
)
print(f"Required N per group: {int(n) + 1}") # Output: 64
# Solve for power (given N)
power = analysis.solve_power(
effect_size=0.5,
alpha=0.05,
nobs1=50,
ratio=1.0,
alternative='two-sided'
)
print(f"Power with N=50 per group: {power:.3f}") # Output: 0.697
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.
- 5d ago First seen · 241 lines · 12 tokens per session scan A 08e82ea6f244
power-analysis-guide is a skill published in the GitHub repository wentorai/research-plugins (287 stars, last pushed 2mo ago), licensed MIT. It adds 12 tokens to every session and 2,080 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 2026-08-30.
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