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/wgcna-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/wgcna-analysis)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/wgcna-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/wgcna-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/wgcna-analysis"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/wgcna-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 | $0.00085 | $0.02967 |
| Opus 5 | $0.00043 | $0.01484 |
| Sonnet 5 | $0.00017 | $0.00593 |
| Haiku 4.5 | $0.00009 | $0.00297 |
Grade A, and why
wgcna-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 9d 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 — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WGCNA Analysis
When to Read External Files
AI Agent: This section tells you when to read additional files.
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md |
WGCNA workflow, filtering strategy, statistics, assumptions |
| Need to run analysis | scripts/main.R |
Execute: Rscript scripts/main.R --input_file ... --group_file ... |
| Encounter errors | references/troubleshooting.md |
Common errors, causes, and fixes |
| Need CLI examples | references/cli-guide.md |
Complete command examples for common scenarios |
| Need conversion audit details | references/diagnosis-report.md |
Skill readiness assessment and remediation summary |
| Need test data | tests/data/ |
Minimal example input files for validation |
When Not to Use
- Do not use for single-cell RNA-seq matrices.
- Do not use for methylation data, DEG testing, or other non-WGCNA workflows.
- Do not use if the user only wants exploratory discussion and does not want the analysis executed.
- Do not proceed if the dataset is obviously too small for WGCNA or becomes too small after QC.
When an input is out of scope, stop early and state which limitation applies.
Usage
Rscript scripts/main.R \
--input_file ./expression_matrix.csv \
--group_file ./group_info.csv \
--output_dir ./output/ \
--sample_column sample \
--group_column group \
--network_type unsigned \
--cor_type pearson \
--mad_quantile 0.25 \
--min_mad 0.01 \
--max_genes 5000 \
--min_module_size 30 \
--merge_cut_height 0.25 \
--soft_r2_cutoff 0.85 \
--module_of_interest auto \
--top_modules 1 \
--tom_sample_size 400 \
--chunk_size 0 \
--seed 42 \
--timeout_seconds 0
Arguments
| Short | Long | Type | Default | Description |
|---|---|---|---|---|
-i |
--input_file |
character | required | Expression matrix file with genes in rows and samples in columns |
-g |
--group_file |
character | required | Sample-to-group mapping file |
-o |
--output_dir |
character | ./output/ |
Output directory |
-a |
--sample_column |
character | sample |
Sample column in the group file; falls back to the first column if not found |
-b |
--group_column |
character | group |
Group column in the group file; falls back to the second column if not found |
-n |
--network_type |
character | unsigned |
Network type for WGCNA: unsigned or signed |
-c |
--cor_type |
character | pearson |
Correlation type: pearson or bicor |
-q |
--mad_quantile |
double | 0.25 |
MAD quantile used to define the variability cutoff |
-m |
--min_mad |
double | 0.01 |
Minimum MAD cutoff combined with the quantile filter |
-k |
--max_genes |
integer | 0 |
Maximum number of retained variable genes; 0 keeps all filtered genes |
-p |
--min_module_size |
integer | 30 |
Minimum module size used by blockwiseModules() |
-r |
--merge_cut_height |
double | 0.25 |
Merge cut height for module merging |
-u |
--soft_r2_cutoff |
double | 0.85 |
Target scale-free topology R-squared cutoff for soft-threshold selection |
-t |
--trait_of_interest |
character | NULL |
Trait column used for module membership vs trait scatter plots; defaults to the first trait column |
-x |
--module_of_interest |
character | auto |
Module color or comma-separated module colors to export; auto ranks modules by absolute module-trait correlation |
--top_modules |
integer | 1 |
Number of top-ranked modules to export when module_of_interest=auto |
|
-y |
--tom_sample_size |
integer | 400 |
Number of genes sampled for the TOM heatmap |
--chunk_size |
integer | 0 |
Row chunk size for large expression matrices; 0 disables chunked loading |
|
-s |
--seed |
integer | 42 |
Random seed for reproducibility and TOM heatmap sampling |
-z |
--timeout_seconds |
integer | 0 |
Optional elapsed-time limit in seconds; 0 disables timeout |
What ships with it
16 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_wgcna-analysis_result.json 15 KB
- references/algorithm.md 4.2 KB
- references/cli-guide.md 11 KB
- references/troubleshooting.md 5.3 KB
- scripts/chunk_io.R 5.4 KB
- scripts/functions.R 2.0 KB
- scripts/io.R 2.5 KB
- scripts/main.R 4.6 KB
- scripts/plotting.R 7.6 KB
- scripts/run_analysis.R 5.3 KB
- scripts/utils.R 4.3 KB
- scripts/wgcna_core.R 3.5 KB
- scripts/wgcna_selection.R 4.7 KB
- tests/data/expression.csv 674 KB
- tests/data/group.csv 299 B
- tests/validate_outputs.R 1.2 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.
- 9d ago First seen · 276 lines · 85 tokens per session scan A 9b087bf97049
wgcna-analysis is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 2,967 once invoked, about $0.0004 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 2026-09-03.
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.