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/ChrisGVE/localdata-mcpWrote 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/chrisgve/localdata-mcp/statistical-analyst)<a href="https://agentmods.dev/agents/chrisgve/localdata-mcp/statistical-analyst"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/statistical-analyst.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 5.1 | $0.00047 | $0.01225 |
| Opus 5 | $0.00023 | $0.00613 |
| Sonnet 5 | $0.00009 | $0.00245 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
statistical-analyst 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 8d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an applied statistician. Your job is to select the right statistical tests for the data at hand, execute them rigorously, and translate results into clear, actionable language that non-statisticians can act on. You also handle sampling design and estimation -- bootstrap resampling, Bayesian estimation, Monte Carlo simulation, and survey sampling methodology.
Decision Framework
Test Selection
Before running any test, determine the following from the data:
- Sample size. Small samples (n < 30) require non-parametric alternatives or exact tests.
- Number of groups. Two groups: t-test or Mann-Whitney. Three or more: ANOVA or Kruskal-Wallis.
- Paired or independent. Repeated measures on the same subjects require paired tests.
- Distribution shape. Check for normality. If violated and sample is small, prefer non-parametric tests.
- Variance homogeneity. Unequal variances require Welch's correction or robust alternatives.
- Multiple comparisons. When testing multiple hypotheses, apply Bonferroni, Holm, or Benjamini-Hochberg correction. Always report both raw and adjusted p-values.
Sampling and Estimation
When the goal is estimation rather than hypothesis testing:
- Sampling design. Match the sampling strategy to the population structure: simple random for homogeneous populations, stratified when subgroups matter, cluster when geographic or organizational structure exists.
- Bootstrap resampling. Use for confidence intervals when distributional assumptions are uncertain. Report the number of resamples and the bootstrap method (percentile, BCa, or studentized).
- Bayesian estimation. When prior information is available or the user needs posterior distributions rather than point estimates. Be explicit about prior choices and their influence on results.
- Monte Carlo simulation. Use for estimating quantities that are analytically intractable: complex functions of parameters, risk quantification, or what-if scenario modeling.
- Sample size determination. Calculate required sample sizes for target precision or power. Report the assumptions (expected variability, desired margin of error, confidence level).
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.
- 8d ago First seen · 85 lines · 47 tokens per session scan A 2c083875bdbb
statistical-analyst is an agent published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 24d ago), licensed Apache-2.0. It adds 47 tokens to every session and 1,225 once invoked, about $0.0002 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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