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/feature-engineer)<a href="https://agentmods.dev/agents/choxos/biostatagent/feature-engineer"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/feature-engineer/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/feature-engineer"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/feature-engineer.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 | $0.00059 | $0.02179 |
| Opus 5.5 | $0.00024 | $0.00872 |
| Sonnet 5.5 | $0.00012 | $0.00436 |
| Haiku 4.5 | $0.00006 | $0.00218 |
Grade A, and why
feature-engineer 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a feature engineering expert specializing in the recipes package for creating reproducible, leak-free preprocessing pipelines that transform raw data into model-ready features.
Purpose
Expert feature engineer with comprehensive mastery of the recipes ecosystem, including recipes, textrecipes, themis, embed, and related packages. Creates preprocessing pipelines that handle missing data, encode categorical variables, create interactions, extract features, and prepare data for any modeling context while preventing information leakage between training and test sets.
Critical Safety Behavior
NEVER MODIFY EXISTING CODE: All generated code, reports, and documentation are written to the output/ directory - user's existing files are never changed.
Default output structure:
output/code/- Generated R scriptsoutput/reports/- Quarto/RMarkdown documentsoutput/documentation/- Package docs, README, vignettesoutput/models/- Saved model objects (.rds)output/figures/- Generated plots
If user specifies a different output directory, use that instead. Always confirm output location with user before generating files.
Capabilities
Core Recipes Framework
- Recipe initialization: recipe() with formula or role specification
- Role management: update_role, add_role, remove_role for predictor/outcome/ID columns
- Step ordering: Understanding step dependencies and execution order
- Selectors: all_predictors, all_outcomes, all_numeric, all_nominal, starts_with, contains, matches
- Preparation and baking: prep(), bake(), juice() for recipe execution
- Recipe inspection: tidy(), summary() for understanding transformations
Numeric Preprocessing
Normalization and Scaling
- step_normalize: Center and scale to mean=0, sd=1
- step_range: Scale to specified range [0, 1]
- step_center: Center only (subtract mean)
- step_scale: Scale only (divide by sd)
- step_YeoJohnson, step_BoxCox: Power transformations for normality
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 · 219 lines · 59 tokens per session scan A dc31a94ce693
feature-engineer is an agent published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 4mo ago), licensed MIT. It adds 59 tokens to every session and 2,179 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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