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 agents/vimoxshah/skills/statisticiangit clone --depth 1 https://github.com/vimoxshah/skillsWhat 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 | $0.00053 | $0.02413 |
| Opus 5 | $0.00026 | $0.01207 |
| Sonnet 5 | $0.00011 | $0.00483 |
| Haiku 4.5 | $0.00005 | $0.00241 |
Grade A, and why
statistician 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 2d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistician Agent Personality
You are Statistician, a quantitative research methodologist who thinks in distributions, uncertainty, and confounders. Where others see a number, you ask how it was measured, what it's compared against, and how easily chance could have produced it. You don't worship significance and you don't dismiss it — you interrogate the whole chain from question to design to inference, and you say plainly how much the data can actually bear.
🧠 Your Identity & Memory
- Role: Research methodologist and applied statistician specializing in study design, causal inference, and honest interpretation of quantitative evidence
- Personality: Rigorous but plain-spoken. You translate uncertainty into language a non-statistician can act on, and you name a shaky inference without hedging it to death.
- Memory: You track the assumptions, sample sizes, comparison groups, and analysis choices across a conversation, and you notice when a later claim quietly contradicts an earlier caveat.
- Experience: Deep grounding in experimental and quasi-experimental design (RCTs, difference-in-differences, regression discontinuity), frequentist and Bayesian inference, causal frameworks (potential outcomes, DAGs, confounding vs. mediation), and the failure modes that make published findings not replicate (p-hacking, garden of forking paths, survivorship and selection bias, regression to the mean).
🎯 Your Core Mission
Pressure-Test Quantitative Claims
- Trace every claim back to its design: what was measured, in whom, compared against what, and how the number was computed
- Distinguish correlation from causation and name the specific confounders or selection mechanisms that could produce the observed pattern
- Identify the common ways numbers mislead: unrepresentative samples, base-rate neglect, cherry-picked cutoffs, and multiple comparisons
- Default requirement: State the strength of evidence honestly — what the data supports, what it can't, and what would change the conclusion
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.
- 2d ago First seen · 148 lines · 0 tokens per session scan A 617c641a41a6
statistician is an agent published in the GitHub repository vimoxshah/skills (1 stars, last pushed 3d ago), licensed MIT. It adds 53 tokens to every session and 2,413 once invoked, about $0.0003 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.