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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aipoch/medical-research-skillsnpx agentmods add skills/aipoch/medical-research-skills/lasso-logistics-analysisWrote 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/lasso-logistics-analysis)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/lasso-logistics-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/lasso-logistics-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/lasso-logistics-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/lasso-logistics-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 | $0.00055 | $0.02268 |
| Opus 5 | $0.00028 | $0.01134 |
| Sonnet 5 | $0.00011 | $0.00454 |
| Haiku 4.5 | $0.00006 | $0.00227 |
Grade A, and why
lasso-logistics-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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LASSO Logistic Regression Analysis
When to Read External Files
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md |
LASSO objective function, cross-validation, and interpretation |
| Need to run analysis | scripts/main.R |
Execute: Rscript scripts/main.R --input_file ... --group_file ... |
| Encounter errors | references/troubleshooting.md |
Common errors and solutions |
| Need CLI examples | references/cli-guide.md |
Detailed CLI usage examples |
| Need test data | tests/data/ |
Sample input files for testing |
| Need workflow implementation details | scripts/run_analysis.R |
Inspect orchestration, outputs, and file-writing behavior |
| Need input-validation or error-handling details | scripts/utils.R, scripts/io.R |
Inspect validation, parsing, logging, and standardized safeguards |
Usage
Rscript scripts/main.R \
--input_file ./expression_matrix.csv \
--group_file ./groups.csv \
--case_group case \
--control_group control \
--output_dir ./output/ \
--nfolds 10 \
--timeout_seconds 1800 \
--seed 42
Arguments
| Short | Long | Type | Default | Description |
|---|---|---|---|---|
-i |
--input_file |
character | required | Expression matrix file (features as rows, samples as columns) |
-g |
--group_file |
character | required | Group file with sample and group columns |
-c |
--case_group |
character | required | Case class label encoded as 1 |
-t |
--control_group |
character | required | Control class label encoded as 0 |
-f |
--feature |
character | NULL |
Optional feature list file or comma-separated feature names |
-n |
--nfolds |
integer | 10 |
Cross-validation folds: 3, 5, 7, 10 |
--cv_title |
character | "" |
Optional title for the cross-validation plot | |
--path_title |
character | "" |
Optional title for the coefficient path plot | |
--timeout_seconds |
integer | 1800 |
Maximum elapsed runtime in seconds | |
-o |
--output_dir |
character | ./output/ |
Output directory |
-s |
--seed |
integer | 42 |
Random seed for reproducibility |
What ships with it
20 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_lasso-logistics-analysis_result.json 12 KB
- references/algorithm.md 1.7 KB
- references/cli-guide.md 3.8 KB
- references/troubleshooting.md 6.5 KB
- scripts/cli_utils.R 4.3 KB
- scripts/io.R 3.3 KB
- scripts/main.R 1.7 KB
- scripts/modeling.R 2.5 KB
- scripts/plotting.R 586 B
- scripts/run_analysis.R 3.7 KB
- scripts/runtime_utils.R 2.4 KB
- scripts/utils.R 2.2 KB
- tests/data/expression_matrix.csv 47 KB
- tests/data/genes.csv 56 B
- tests/data/groups.csv 633 B
- tests/run_tests.R 770 B
- tests/testthat.R 885 B
- tests/testthat/helper-load-scripts.R 446 B
- tests/testthat/setup.R 247 B
- tests/testthat/test-standardization.R 2.1 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 · 275 lines · 55 tokens per session scan A 9f8839e505a3
lasso-logistics-analysis is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 2,268 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 2026-08-30.
Other skills, from other repositories
statistical-modeling
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bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
chromatin-regulation
Chromatin regulation analysis from called peaks and count matrices — differential binding, signal summarisation, peak annotation, and scATAC-seq.
spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.
atac-seq-bam-read-alignment-processing
Use when when you have aligned ATAC-seq BAM files and need to quantify Tn5 transposase insertion patterns around specific genomic coordinates (motif sites, peaks, regulatory regions) to detect transcription factor occupancy footprints or compare chromatin accessibility between bound and unbound.
bedgraph-file-format-manipulation
Use when you have aligned ChIP-Seq reads (in BED or BEDPE format) and need to convert them into quantitative genome-wide signal tracks (coverage, p-value, or q-value scores) for downstream statistical comparison or peak detection.