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 choxos/BiostatAgent --skill pairwise-ma-methodologygit 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/skills/choxos/biostatagent/pairwise-ma-methodology)<a href="https://agentmods.dev/skills/choxos/biostatagent/pairwise-ma-methodology"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/pairwise-ma-methodology/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/choxos/biostatagent/pairwise-ma-methodology"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/pairwise-ma-methodology.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 | $0.00040 | $0.02008 |
| Opus 5.5 | $0.00016 | $0.00803 |
| Sonnet 5.5 | $0.00008 | $0.00402 |
| Haiku 4.5 | $0.00004 | $0.00201 |
Grade A, and why
pairwise-ma-methodology 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.
How it starts
The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pairwise Meta-Analysis Methodology
Comprehensive methodological guidance for conducting rigorous pairwise meta-analysis following Cochrane and PRISMA guidelines.
When to Use This Skill
- Planning a pairwise meta-analysis
- Choosing between fixed and random effects models
- Interpreting heterogeneity statistics
- Assessing publication bias
- Designing sensitivity analyses
- Reviewing pairwise MA code or results
Fixed vs Random Effects
Decision Framework
Are studies functionally identical?
├── Yes → Fixed-effect model appropriate
│ - Same population, intervention, comparator, outcome
│ - Estimating single "true" effect
│
└── No (usually the case) → Random-effects model
- Studies differ in ways that affect true effect
- Estimating mean of distribution of effects
- More generalizable inference
When to Use Fixed-Effect
- Studies are very similar (rare in practice)
- Want to estimate effect in "identical" studies
- Very few studies (< 5) - random effects unreliable
- Sensitivity analysis alongside random effects
When to Use Random-Effects
- Studies differ in populations, settings, methods
- Want inference applicable beyond included studies
- Default choice for most meta-analyses
- Use with appropriate adjustments (Knapp-Hartung)
Key Differences
| Aspect | Fixed-Effect | Random-Effects |
|---|---|---|
| Assumption | Common true effect | Distribution of true effects |
| Weights | Based on precision only | Includes between-study variance |
| Small study | More weight | Less weight |
| Large study | Less relative weight | More weight |
| CI width | Narrower (if heterogeneity exists) | Wider (appropriately) |
| Inference | To identical studies | To broader population |
Heterogeneity Assessment
Statistics Overview
Q Statistic (Cochran's Q)
- Tests null hypothesis of homogeneity
- Follows chi-square distribution under null
- Low power with few studies
- Overpowered with many studies
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 · 325 lines · 40 tokens per session scan A 4671c29bd689
pairwise-ma-methodology is a skill published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 2,008 once invoked, about $0.0002 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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