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 knn-imputationgit 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/knn-imputation)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/knn-imputation"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/knn-imputation/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/knn-imputation"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/knn-imputation.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 | $0.00095 | $0.02410 |
| Opus 5 | $0.00048 | $0.01205 |
| Sonnet 5 | $0.00019 | $0.00482 |
| Haiku 4.5 | $0.00010 | $0.00241 |
Grade A, and why
knn-imputation 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KNN Imputation
When to Use
Use this skill when you need to remove genes with more than 50% missing values from a bulk expression matrix and then run group-aware KNN imputation, with the donor pool restricted by one grouping column.
Do not use this skill for:
- single-cell data
- multi-column stratification
- non-tabular inputs
- network-dependent workflows
- interactive analysis sessions
When to Read External Files
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md |
Group-stratified KNN method, fallback rules, and assumptions |
| 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 sample input fixtures | tests/data/ |
Repository fixtures for local validation and examples |
Input Validation
This skill accepts: a bulk expression matrix CSV (features × samples) and a sample annotation CSV file with a single grouping column for KNN stratification.
If the user's request does not involve imputing missing values in a bulk expression matrix — for example, asking to impute single-cell data, use multi-column stratification, or run network-dependent workflows — do not proceed with the workflow. Instead respond:
"knn-imputation is designed to filter and impute missing values in bulk expression matrices using group-aware KNN with DMwR2. Your request appears to be outside this scope. Please provide a bulk expression matrix with a single grouping column, or use a more appropriate tool for your task."
Prerequisites
DMwR2 is not available on CRAN. Install it from GitHub before running:
install.packages("remotes")
remotes::install_github("cran/DMwR2")
If SKILL_DEPENDENCY_MISSING is raised, use the command above to install DMwR2 before retrying. Standard install.packages("DMwR2") will not work.
What ships with it
13 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_knn-imputation_result.json 15 KB
- references/algorithm.md 3.5 KB
- references/cli-guide.md 4.0 KB
- references/troubleshooting.md 3.0 KB
- scripts/functions.R 4.2 KB
- scripts/imputation_functions.R 5.0 KB
- scripts/imputation_helpers.R 2.9 KB
- scripts/main.R 4.9 KB
- scripts/run_analysis.R 5.4 KB
- scripts/utils.R 4.0 KB
- tests/data/sample_expression_matrix.csv 7142 KB
- tests/data/sample_groups_custom.csv 634 B
- tests/data/sample_groups.csv 674 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 · 268 lines · 95 tokens per session scan A d0da0e05ae48
knn-imputation is a skill published in the GitHub repository aipoch/medical-research-skills (1,869 stars, last pushed today), licensed MIT. It adds 95 tokens to every session and 2,410 once invoked, about $0.0005 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
Statistical modeling and machine learning for biomarker discovery, survival analysis, classification, regression, and model interpretation.
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.