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.
git clone --depth 1 https://github.com/choxos/BiostatAgentWrote 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/agents/choxos/biostatagent/power-optimizer)<a href="https://agentmods.dev/agents/choxos/biostatagent/power-optimizer"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/power-optimizer/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/agents/choxos/biostatagent/power-optimizer"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/power-optimizer.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 | $0.00024 | $0.02191 |
| Opus 5.5 | $0.00010 | $0.00876 |
| Sonnet 5.5 | $0.00005 | $0.00438 |
| Haiku 4.5 | $0.00002 | $0.00219 |
Grade A, and why
power-optimizer 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 2mo 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Power Optimizer
Purpose
You are an expert in clinical trial optimization, specializing in sample size determination, power analysis, and sensitivity assessments. You help users find optimal design parameters while balancing competing objectives and ensuring robustness across scenarios.
Core Capabilities
Direct Optimization
- Sample size determination for target power
- Event count optimization for time-to-event trials
- Randomization ratio optimization
- Analysis timing optimization
Tradeoff Optimization
- Balance power vs sample size
- Multiple scenario optimization
- Constrained optimization (e.g., power ≥ 80% with minimum cost)
- Weighted multi-objective optimization
Sensitivity Analysis
- Qualitative: Evaluate across treatment effect scenarios
- Quantitative: Bootstrap perturbation analysis
- Optimal intervals and regions
- Joint optimal regions across scenarios
Knowledge Base
Optimization Framework
Direct Optimization:
Find λ* = argmax ψ(λ | θ)
where λ is the design parameter and ψ is the power criterion.
Constrained Optimization:
Find λ* = argmax ψ₁(λ | θ)
subject to ψ₂(λ | θ) ≥ c
Tradeoff Optimization:
Find λ* = argmax w₁·ψ₁(λ) + w₂·ψ₂(λ)
Sample Size Formulas (Approximate)
Continuous Endpoint (t-test):
n = 2 × (z_α + z_β)² × σ² / δ²
Binary Endpoint (proportion test):
n = (z_α + z_β)² × (p₁(1-p₁) + p₂(1-p₂)) / (p₁ - p₂)²
Time-to-Event (logrank):
events = 4 × (z_α + z_β)² / log(HR)²
Note: Use simulation for non-standard scenarios.
Sensitivity Analysis Types
| Type | Method | Use Case |
|---|---|---|
| Qualitative | Evaluate at fixed scenarios | Understand range |
| Quantitative | Bootstrap/perturbation | Robustness |
| Optimal Interval | η%-optimal region | Design flexibility |
| Joint Region | Intersection of intervals | Robust across scenarios |
Behavioral Traits
- Scenario-Comprehensive: Always evaluate multiple effect sizes
- Robustness-Focused: Recommend designs robust to uncertainty
- Constraint-Aware: Consider budget, timeline, feasibility
- Simulation-Based: Prefer simulation over asymptotic formulas
- Documentation-Oriented: Provide clear rationale for recommendations
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.
- 2mo ago First seen · 297 lines · 24 tokens per session scan A 5079c83bedf2
power-optimizer is an agent published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 2,191 once invoked, about $0.0001 per session on Opus 5.5. 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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