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 pca-dimensionality-reductiongit 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/pca-dimensionality-reduction)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/pca-dimensionality-reduction"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/pca-dimensionality-reduction/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/pca-dimensionality-reduction"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/pca-dimensionality-reduction.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 | $0.00048 | $0.01504 |
| Opus 5 | $0.00024 | $0.00752 |
| Sonnet 5 | $0.00010 | $0.00301 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
pca-dimensionality-reduction 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PCA Dimensionality Reduction Analysis
Use this skill to run principal component analysis on a tabular dataset and export explained variance, sample scores, feature loadings, and diagnostic figures.
Use This Skill When
- You need to reduce multiple numeric variables into a smaller set of principal components.
- You need a command-line PCA workflow with parameter validation.
- You need standardized output files for downstream analysis.
Primary Command
Rscript scripts/main.R \
--data_file <input_file> \
--output_dir <output_dir> \
--feature_columns <comma_separated_numeric_columns>
Prerequisites
Rscriptis available in the shell.- Required R packages:
optparse,data.table. - Install missing packages with
Rscript -e 'install.packages(c("optparse", "data.table"), repos="https://cloud.r-project.org")'.
Core Arguments
| Argument | Required | Description |
|---|---|---|
--data_file |
Yes | Input data file in CSV, TXT, or TSV format |
--output_dir |
No | Output directory, default ./PCA_Results |
--feature_columns |
No | Comma-separated numeric feature columns. Default uses all numeric columns except ID/group columns |
--sample_id_column |
No | Optional sample ID column. If omitted and the first column is non-numeric with unique values, it is used automatically |
--group_column |
No | Optional grouping column to carry into score output and score plot |
--n_components |
No | Maximum number of principal components to export, default 5 |
--center_data |
No | true or false, default true |
--scale_data |
No | true or false, default true |
--top_loadings |
No | Number of top absolute loadings to export per component, default 10 |
--output_format |
No | csv or txt, default csv |
--output_prefix |
No | Output filename prefix, default pca |
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_pca-dimensionality-reduction_result.json 15 KB
- references/algorithm.md 4.0 KB
- references/cli-guide.md 3.8 KB
- references/troubleshooting.md 3.8 KB
- scripts/functions.R 7.8 KB
- scripts/main.R 3.6 KB
- scripts/run_analysis.R 5.2 KB
- scripts/utils.R 5.3 KB
- tests/data/sample_pca_1.csv 429 B
- tests/data/sample_pca_2.csv 3266 KB
- tests/data/sample_pca_3.csv 228 B
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 · 185 lines · 48 tokens per session scan A 89b118a51852
pca-dimensionality-reduction is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,504 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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