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 tf-target-gene-regulatory-networkgit 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/tf-target-gene-regulatory-network)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/tf-target-gene-regulatory-network"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/tf-target-gene-regulatory-network/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/tf-target-gene-regulatory-network"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/tf-target-gene-regulatory-network.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 | $0.00052 | $0.02611 |
| Opus 5 | $0.00026 | $0.01306 |
| Sonnet 5 | $0.00010 | $0.00522 |
| Haiku 4.5 | $0.00005 | $0.00261 |
Grade A, and why
tf-target-gene-regulatory-network 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 — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcription Factor (TF) Regulatory Network Analysis
When to Use
- Use this skill when you have a human or mouse gene list and want to identify upstream TFs from the Dorothea database.
- Use it when you need a ready-to-export TF-target network table plus a publication-ready PDF network plot.
- Use it for reproducible CLI execution with saved session information and optional local Dorothea
.rdsdatabases.
When Not to Use
- Do not use this skill for differential expression, pathway enrichment, cell type annotation, or survival analysis.
- Do not use it to infer causal direction beyond curated Dorothea TF-target relationships.
- Do not use it when your input genes are aliases or mixed-species symbols that have not been normalized first.
Input Validation
This skill accepts: a human or mouse gene list (HGNC symbols for human, first-letter-uppercase for mouse) for TF regulatory network analysis using Dorothea.
If the user's request does not involve identifying upstream transcription factors from a gene list — for example, asking to run differential expression, pathway enrichment, cell type annotation, or multi-omics integration — do not proceed with the workflow. Instead respond:
"tf-target-gene-regulatory-network is designed to identify upstream transcription factors from a gene list using the Dorothea database and generate a TF-target network visualization. Your request appears to be outside this scope. Please provide a gene list for TF regulatory analysis, or use a more appropriate tool for your task."
Entry Point
- Primary CLI entry point:
scripts/main.R - Canonical visualization values: English tokens
fr,curve,diamond,triangle,square
When to Read External Files
| Situation | File to Read | Purpose |
|---|---|---|
| Need algorithm details | references/algorithm.md |
Statistical methods, Dorothea database, network analysis algorithms |
| Need to run analysis | scripts/main.R |
Execute: Rscript scripts/main.R --gene ... --species ... |
| 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 gene lists for testing |
What ships with it
42 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.
- database/database-get.R 294 B
- database/dorothea_hs.rds 1691 KB
- database/dorothea_mm.rds 1446 KB
- DESCRIPTION 882 B
- eval_report_tf-target-gene-regulatory-network_result.json 17 KB
- references/algorithm.md 5.2 KB
- references/cli-guide.md 6.0 KB
- references/troubleshooting.md 5.7 KB
- references/visualization-parameters.md 4.4 KB
- scripts/functions.R 9.0 KB
- scripts/main.R 7.2 KB
- scripts/run_analysis.R 2.5 KB
- scripts/utils.R 7.6 KB
- scripts/visualization.R 4.1 KB
- tests/audit_v2_case_aliases/data/tf.Rdata 19 KB
- tests/audit_v2_case_aliases/session_info.txt 2.3 KB
- tests/audit_v2_case_aliases/table/tf_network.xlsx 8.2 KB
- tests/audit_v2_case_aliases/table/TF_Target_Filtered_Core_human.xlsx 6.6 KB
- tests/audit_v2_case_canonical/data/tf.Rdata 19 KB
- tests/audit_v2_case_canonical/session_info.txt 2.3 KB
- tests/audit_v2_case_canonical/table/tf_network.xlsx 9.9 KB
- tests/audit_v2_case_canonical/table/TF_Target_Filtered_Core_human.xlsx 8.2 KB
- tests/audit_v2_case_custom/data/tf.Rdata 19 KB
- tests/audit_v2_case_custom/session_info.txt 2.3 KB
- tests/audit_v2_case_custom/table/tf_network.xlsx 8.2 KB
- tests/audit_v2_case_custom/table/TF_Target_Filtered_Core_human.xlsx 6.6 KB
- tests/audit_v2_case_file/data/tf.Rdata 19 KB
- tests/audit_v2_case_file/session_info.txt 2.3 KB
- tests/audit_v2_case_file/table/tf_network.xlsx 11 KB
- tests/audit_v2_case_file/table/TF_Target_Filtered_Core_human.xlsx 9.3 KB
- tests/audit_v2_case_mouse/data/tf.Rdata 19 KB
- tests/audit_v2_case_mouse/session_info.txt 2.3 KB
- tests/audit_v2_case_mouse/table/tf_network.xlsx 9.5 KB
- tests/audit_v2_case_mouse/table/TF_Target_Filtered_Core_mouse.xlsx 7.8 KB
- tests/data/human_genes.csv 24 B
- tests/data/human_genes.txt 49 B
- tests/data/mouse_genes.txt 49 B
- tests/test.R 5.7 KB
- tests/verify_legacy_aliases/data/tf.Rdata 19 KB
- tests/verify_legacy_aliases/session_info.txt 2.3 KB
- tests/verify_legacy_aliases/table/tf_network.xlsx 8.2 KB
- tests/verify_legacy_aliases/table/TF_Target_Filtered_Core_human.xlsx 6.6 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 · 298 lines · 52 tokens per session scan A 442cb287a94c
tf-target-gene-regulatory-network is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 2,611 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 2026-09-03.
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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.