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 inflexa-ai/inflexa --skill statistical-modelinggit clone --depth 1 https://github.com/inflexa-ai/inflexaWrote 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/inflexa-ai/inflexa/statistical-modeling)<a href="https://agentmods.dev/skills/inflexa-ai/inflexa/statistical-modeling"><img src="https://agentmods.dev/badge/skills/inflexa-ai/inflexa/statistical-modeling/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/inflexa-ai/inflexa/statistical-modeling"><img src="https://agentmods.dev/badge/skills/inflexa-ai/inflexa/statistical-modeling.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 | $0.00026 | $0.03204 |
| Opus 5.5 | $0.00010 | $0.01282 |
| Sonnet 5.5 | $0.00005 | $0.00641 |
| Haiku 4.5 | $0.00003 | $0.00320 |
Grade A, and why
statistical-modeling 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Modeling
This skill guides method selection and execution for survival analysis, classification, regression, feature selection, mixed-effects modeling, and model interpretation in biomedical contexts.
Method Selection Decision Tree
Choose the method based on your outcome type and analytical goal:
1. Survival Analysis (time-to-event data with censoring)
- Univariate (single variable, Kaplan-Meier curves)
lifelines.KaplanMeierFitterfor survival curves,logrank_test()for group comparison.
- Multivariate (adjust for covariates)
lifelines.CoxPHFitterfor Cox proportional hazards regression. Check PH assumption withcheck_assumptions(). If the check fails, stratify on the covariate at fault (strata=[...]), or add a time-varying term. Then report the hazard ratio as time-averaged, not as a constant effect.
- ML-based survival (non-linear, high-dimensional)
scikit-survival.RandomSurvivalForestfor non-linear survival prediction.scikit-survival.GradientBoostingSurvivalAnalysisfor best predictive performance.
- Censoring encoding: event indicator = 1 means the event occurred, 0 means censored. Verify this before fitting.
2. Binary Classification (predict discrete outcome)
Escalate complexity only when simpler models underperform:
- Start:
sklearn.LogisticRegression(interpretable, baseline). - If non-linear patterns:
sklearn.RandomForestClassifier(handles interactions, feature importance built in). - If maximum performance needed:
xgboost.XGBClassifier(gradient boosting, tunable).- xgboost is thread-parallel: raise the thread limit for the training command to the full CPU budget of the step. Do not run it under forked workers (for example joblib) at the same time.
- Metric: Use AUC-ROC as primary metric. For imbalanced classes, also report AUPRC (precision-recall). Never use accuracy alone on imbalanced data.
3. Regression (predict continuous outcome)
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 241 lines · 26 tokens per session scan A fcff9e36156e
statistical-modeling is a skill published in the GitHub repository inflexa-ai/inflexa (35 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 3,204 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 2026-09-09.
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