Medical Research Agent Skills is a library of agent instructions for medical and biomedical research, covering evidence analysis, study protocol design, data analysis, and academic writing. Researchers use it to guide compatible coding agents through common scientific workflows. The catalogue contains many of the library's skills and commands.
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 aipoch/medical-research-skills --skill lightgbm-analysisgit clone --depth 1 https://github.com/aipoch/medical-research-skillsWrote 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/aipoch/medical-research-skills/lightgbm-analysis)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/lightgbm-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/lightgbm-analysis/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/aipoch/medical-research-skills/lightgbm-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/lightgbm-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.03568 |
| Opus 5 | $0.00017 | $0.01784 |
| Sonnet 5 | $0.00007 | $0.00714 |
| Haiku 4.5 | $0.00003 | $0.00357 |
Grade A, and why
lightgbm-analysis 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 13d 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 — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LightGBM Analysis
Use this skill to build a LightGBM model on tabular data and export feature importance ranking results as both a table and a figure.
Use This Skill When
- You need a command-line LightGBM workflow written in R.
- You need classification or regression on structured tabular data.
- You need ranked feature importance outputs for reporting or interpretation.
- You need standardized outputs under
table/,figure/, anddata/.
Primary Command
Rscript scripts/main.R \
--data_file <input_file> \
--target_var <target_column> \
--output_dir <output_dir>
Prerequisites
Rscriptis available in the shell.- Required R packages:
optparse,data.table,lightgbm. - Install basic dependencies with
Rscript -e 'install.packages(c("optparse", "data.table"), repos="https://cloud.r-project.org")'. - Install the R
lightgbmpackage from the LightGBM project because it is usually not available from CRAN.
Core Arguments
| Argument | Required | Description |
|---|---|---|
--data_file |
Yes | Input data file in CSV format or tab-delimited TXT/TSV format |
--target_var |
Yes | Target column used for modeling |
--output_dir |
No | Output directory, default ./LightGBM_Results |
--fail_if_output_exists |
No | Stop instead of overwriting when output_dir already contains files |
--task_type |
No | auto, regression, binary, or multiclass. Default auto |
--feature_cols |
No | Comma-separated feature columns. Default uses all columns except target and dropped columns |
--drop_cols |
No | Comma-separated columns to exclude before modeling |
--importance_type |
No | gain or split. Default gain |
--top_n |
No | Number of features to show in the importance plot. Default 20 |
--output_format |
No | csv or txt table export. Default csv |
What ships with it
11 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.
- eval_report_lightgbm-analysis_result.json 16 KB
- references/algorithm.md 4.3 KB
- references/cli-guide.md 6.0 KB
- references/troubleshooting.md 6.7 KB
- scripts/functions.R 26 KB
- scripts/main.R 5.9 KB
- scripts/run_analysis.R 4.5 KB
- scripts/utils.R 6.2 KB
- tests/data/dt_sample1.csv 13 KB
- tests/data/dt_sample2.csv 4.6 KB
- tests/data/dt_sample3.txt 71 KB
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.
- 13d ago First seen · 357 lines · 34 tokens per session scan A 07961bdfb1c1
lightgbm-analysis is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 3,568 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 2026-08-30.
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