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Agent skill

Dual Disease Transcriptomic ML Planner

by aipoch in aipoch/medical-research-skills

Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair.

MITAuto-check passedResearch & Science

Install Dual Disease Transcriptomic ML Planner

skills CLI
$ npx skills add aipoch/medical-research-skills --skill dual-disease-transcriptomic-ml-planner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills dual-disease-transcriptomic-ml-planner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Protocol Design/dual-disease-transcriptomic-ml-planner' .claude/skills/dual-disease-transcriptomic-ml-planner && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dual-disease-transcriptomic-ml-planner
GitHub stars
2k
Token cost
~3.7k tokens
SKILL.md length
1,531 words
Files
6 (incl. references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair.

  • Works in 9 steps: Infer Study Type → Output Four Configurations → Recommend One Primary Plan → …
  • Users want to identify shared DEGs
  • SKILL.md covers Supported Study Styles, Minimum User Input, Step-by-Step Execution and R Code Framework Guidelines, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Dual Disease Transcriptomic ML Planner is an agent skill from aipoch/medical-research-skills. Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair. Use when users want to identify shared DEGs, common hub genes, cross-disease biomarkers, or shared molecular mechanisms between two diseases using public GEO data. Triggers:"shared biomarker study for two diseases", "dual-disease transcriptomic ML paper", "identify common DEGs between disease A and B", "cross-disease hub gene discovery", "shared DEG + PPI + ROC design", "immune infiltration shared…

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `eval_report_dual-disease-transcriptomic-ml-planner_polished_result.json`, `references/figure_plan_template.md` and `references/geo_search_and_tools.md`).

It sits in Research & Science, covering Bioinformatics and Machine learning. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Users want to identify shared DEGs
  • Common hub genes
  • Cross-disease biomarkers
  • Shared molecular mechanisms between two diseases using public GEO data

Example prompts

  • “shared biomarker study for two diseases”
  • “dual-disease transcriptomic ML paper”
  • “identify common DEGs between disease A and B”
  • “/dual-disease-transcriptomic-ml-planner”

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Infer Study Type
  2. Output Four Configurations
  3. Recommend One Primary Plan
  4. Full Step-by-Step Workflow
  5. Figure Plan
  6. Validation and Robustness Plan
  7. Risk Review
  8. Minimal Executable Version
  9. Publication Upgrade Path

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are r).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • ncbi.nlm.nih.gov

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dual Disease Transcriptomic ML Planner loads about 3.7k tokens when it runs, and up to ~8.3k if it reads all its reference files. Until then it costs about 213 tokens; SKILL.md has 1,531 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~213
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 1,531 words, ~3,742 tokens.

Download SKILL.mdSave it as .claude/skills/dual-disease-transcriptomic-ml-planner/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
dual-disease-transcriptomic-ml-planner
description
Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair. Use when users want to identify shared DEGs, common hub genes, cross-disease biomarkers, or shared molecular mechanisms between two diseases using public GEO data. Triggers:"shared biomarker study for two diseases", "dual-disease transcriptomic ML paper", "identify common DEGs between disease A and B", "cross-disease hub gene discovery", "shared DEG + PPI + ROC design", "immune infiltration shared biomarker", or "I want to study disease X and Y together". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Dual-Disease Transcriptomic Machine Learning Research Planner

Generates a complete dual-disease transcriptomic + ML study design from a user-provided disease pair. Always outputs four workload configurations and a recommended primary plan.

Supported Study Styles

StyleDescriptionExample
A. Shared DEG → Hub Gene CoreDEG overlap → PPI → hub consensusIntracranial aneurysm + AAA; diabetic + hypertensive nephropathy
B. Dual-Disease Shared MechanismPathway-level convergenceECM, inflammation, fibrosis linking two diseases
C. PPI + Multi-Algorithm Hub PrioritizationSTRING + MCODE + CytoHubba consensusAny pair with sufficient shared DEGs
D. Dual-Disease Biomarker ValidationROC in discovery + validation cohortsAny pair with ≥2 GEO datasets per disease
E. Immune Infiltration + Shared BiomarkerCIBERSORT/alternative + gene–immune correlationImmunologically active disease pairs
F. Single-Gene Cross-Disease DeepeningHub-gene GSEA in both diseasesSingle top hub with strong AUC
G. Publication-Oriented Integrated DesignFull pipeline: DEG → PPI → ROC → immune → GSEAHigh-impact submission target

Minimum User Input

  • Two diseases or phenotypes
  • If limited detail is provided, infer a reasonable default design and state all assumptions explicitly (Hard Rule 9)

Step-by-Step Execution

Step 1: Infer Study Type

Identify:

  • Disease pair and biological theme (vascular, autoimmune, fibrotic, metabolic, neurodegenerative, infectious-oncologic, comorbidity)
  • User goal: shared biomarkers, shared mechanisms, immune relevance, or publication strength
  • Whether ML is central (hub consensus, ROC) or supportive (biological interpretation)
  • Whether immune analysis is appropriate — consult Hard Rule 5 and tissue/tool decision guide below
  • Resource constraints: public data only, dataset count per disease, time limit, single-gene focus
Step 2: Output Four Configurations

Always generate all four. For each describe: goal, required data, major modules, expected workload, figure set, strengths, weaknesses.

ConfigGoalTimeframeBest For
LiteShared DEG + basic hub, 1 dataset per disease2–4 weeksPilot, skeleton manuscript, single-dataset constraint
StandardFull pipeline + validation + ROC + one deepening layer5–9 weeksCore publishable paper
AdvancedStandard + immune + GSEA + multi-cohort robustness9–14 weeksCompetitive journal target
Publication+Full multi-layer + experimental suggestions + reviewer defense12–20 weeksHigh-impact submission
Step 3: Recommend One Primary Plan

Select the best-fit configuration and explain why, given disease pair biology, GEO data availability, time constraints, and publication ambition.

Step 4: Full Step-by-Step Workflow

For each step include: step name, purpose, input, method, key parameters/thresholds, expected output, failure points, alternative approaches.

Dataset & Preprocessing

  • GEO dataset search: one discovery + one validation per disease when feasible (see references/geo_search_and_tools.md)
  • Tissue-only filtering: exclude blood/CSF unless disease-appropriate; match tissue type across both diseases
  • Tissue selection rule: use the tissue most proximal to disease pathology; for metabolic diseases refer to the tissue/tool decision guide
  • Platform compatibility check: verify GPL IDs match or are cross-compatible before merging
  • Normalization; batch-awareness without forced merging
  • Disease vs control group assignment

Fault tolerance — dataset level:

  • If no GEO dataset exists for one disease: state infeasibility, suggest the closest available proxy phenotype, downgrade to Lite with discovery-only design
  • If only one dataset is available per disease: downgrade to Lite; clearly state validation ROC is not feasible; provide GEO search strategy for a second cohort

DEG & Shared Signature

  • limma-based DEG analysis (logFC > 1–2, adj.p < 0.05)
  • Volcano plots, heatmaps
  • Shared up/downregulated DEG intersection (Venn diagram)
  • Shared-gene summary table

Fault tolerance — DEG intersection:

  • If shared DEG count = 0: do not proceed with PPI/hub analysis; apply the following recovery sequence in order:
    1. Relax logFC threshold to 0.5 (report alongside original results)
    2. Extend to top 500 DEGs per disease regardless of threshold
    3. Switch to WGCNA co-expression module overlap instead of direct DEG intersection
    4. Re-evaluate whether the disease pair shares a common tissue or biological mechanism; recommend alternative pairing if not

Enrichment & Shared Mechanism

  • GO enrichment (BP, MF, CC) + KEGG enrichment (clusterProfiler / DAVID)
  • Pathway visualization; shared biological module summarization

PPI & Hub Prioritization

  • STRING PPI construction (confidence score > 0.4)
  • Cytoscape visualization; MCODE dense-cluster identification
  • CytoHubba multi-algorithm ranking (≥5 algorithms required: Degree, MCC, Betweenness, Closeness, EPC)
  • Hub-gene consensus logic → top 1 / top 3 / top 10 candidates

Biomarker Performance

  • ROC / AUC analysis (pROC); AUC > 0.70 as minimum threshold
  • Discovery-cohort ROC + validation-cohort ROC (Standard and above)
  • Expression validation across cohorts

Fault tolerance — ROC:

  • If AUC ≈ 0.5 in discovery cohort: do not interpret as biomarker; flag as non-informative; consider mini-signature (3–5 genes) instead of single hub gene
  • If n < 30 per group: explicitly flag AUC inflation risk; interpret AUC with bootstrap CI; do not generalize

Immune Infiltration (when disease-appropriate per Hard Rule 5)

  • Deconvolution tool selection — consult references/tissue_and_tool_decisions.md for the correct tool by tissue type
  • Immune-cell proportion comparison (disease vs control); gene–immune cell correlation (Spearman)
  • Violin plots, lollipop / heatmap correlation

Single-Gene Deepening (Standard and above)

  • Stratify samples by hub gene expression (high vs low quartile)
  • Single-gene GSEA in both diseases; cross-disease pathway convergence interpretation
Step 5: Figure Plan

→ Full figure list and table templates: references/figure_plan_template.md

Core figures: workflow schematic (Fig 1), DEG volcanos + Venn (Fig 2), shared DEG heatmap (Fig 3), GO/KEGG enrichment (Fig 4), PPI + MCODE + hub ranking (Fig 5), ROC curves (Fig 6), immune infiltration + correlation (Fig 7), single-gene GSEA (Fig 8). Tables: dataset summary, shared DEG list, hub rankings, ROC/AUC summary.

Step 6: Validation and Robustness Plan

State what each layer proves and what it does not prove:

  • Shared-expression evidence — DEG overlap + threshold reproducibility
  • Hub-prioritization evidence — PPI topology + multi-algorithm consensus (association, not causation)
  • Biomarker performance evidence — ROC/AUC in discovery + validation cohorts (diagnostic signal, not mechanistic proof)
  • Immune support — immune landscape differences + gene–immune correlation (associative only; Hard Rule 8)
  • Single-gene mechanistic support — GSEA pathway themes (hypothesis-generating only; Hard Rule 7)
Step 7: Risk Review

Always include a self-critical section addressing:

  • Strongest part of the design
  • Most assumption-dependent part (typically: small cohort ROC inflation; platform differences across datasets)
  • Most likely false-positive source (hub ranking with few shared DEGs; AUC > 0.9 in n < 50)
  • Easiest part to overinterpret (immune deconvolution as causal; one hub gene as mechanistic proof)
  • Most likely reviewer criticisms: small cohorts, no experimental validation, platform heterogeneity, overinterpretation of single biomarker, immune deconvolution limitations, CRC/infectious disease subtype heterogeneity
  • Revision strategy if first-pass findings fail (broaden DEG threshold, alternate validation cohort, switch to mini-signature)
Show full SKILL.md (585 more words)Show less
Step 8: Minimal Executable Version

Public data only, one discovery dataset per disease, DEG + Venn + GO/KEGG, STRING + MCODE + CytoHubba top gene, ROC in discovery cohort, one-page interpretation. 2–4 week timeline. Confirm feasibility against any stated time or dataset constraints before recommending.

Step 9: Publication Upgrade Path

→ Full upgrade impact table: references/upgrade_path.md

Key upgrades by impact: validation cohort per disease (High / Low–Medium), multi-algorithm hub consensus (High / Low), cross-platform reproducibility logic (High / Medium), immune infiltration (Medium / Medium), single-gene GSEA (Medium / Low), mini-signature 3–5 genes (Medium / Medium).

R Code Framework Guidelines

When providing R code examples or pipeline frameworks:

  1. EXAMPLE ID convention: All GEO accession numbers in code must carry an inline comment: # EXAMPLE ID — replace with your actual GSE accession before running
  2. Zero-intersection guard: All pipelines must include a feasibility check immediately after DEG intersection:
    r
    if (length(shared_genes) == 0) {
  stop("No shared DEGs found. Recovery options: (1) relax logFC to 0.5, (2) use top-500 DEGs per disease, (3) switch to WGCNA co-expression module overlap.")
}
  3. Standard package list: GEOquery, limma, clusterProfiler, org.Hs.eg.db, pROC, igraph, STRINGdb, WGCNA. Provide BiocManager::install() calls where needed.
  4. GEO search pattern: To find valid accession IDs, use GEOquery::getGEO("GSEsearch", ...) or direct search at https://www.ncbi.nlm.nih.gov/geo/

Standard R pipeline template:

r
library(GEOquery); library(limma); library(clusterProfiler); library(pROC)

# Load datasets — EXAMPLE IDs: replace before running
gse_disease1 <- getGEO("GSEXXXXX", GSEMatrix = TRUE)[[1]]  # EXAMPLE ID
gse_disease2 <- getGEO("GSEXXXXX", GSEMatrix = TRUE)[[1]]  # EXAMPLE ID

# DEG analysis (repeat for disease2)
design <- model.matrix(~ group, data = pData(gse_disease1))
fit    <- eBayes(lmFit(exprs(gse_disease1), design))
deg_d1 <- subset(topTable(fit, coef = 2, adjust = "BH", number = Inf),
                 abs(logFC) > 1 & adj.P.Val < 0.05)

# Shared DEG intersection with zero-guard
shared_genes <- intersect(rownames(deg_d1), rownames(deg_d2))
if (length(shared_genes) == 0) {
  stop("No shared DEGs found. Recovery: relax logFC to 0.5 or use top-500 DEGs per disease.")
}

# ROC for top hub gene — EXAMPLE: replace 'HUB_GENE' and labels/scores with real data
roc_obj <- roc(response = labels, predictor = expr_scores)
cat("AUC:", auc(roc_obj), "\n")
if (auc(roc_obj) < 0.70) warning("AUC below 0.70 threshold. Consider mini-signature approach.")

Hard Rules

  1. Never output only one generic plan — always output all four configurations.
  2. Always recommend one primary plan with justification.
  3. Always separate necessary modules from optional modules.
  4. Distinguish shared-expression evidence, biomarker performance evidence, immune support, and mechanistic support — see Step 6.
  5. Do not proceed with immune analysis if the disease pair is not immunologically suited or if deconvolution would be unreliable for the tissue type. Consult references/tissue_and_tool_decisions.md to select the correct tool.
  6. Do not overclaim diagnostic value from ROC in small (n < 30 per group) or unmatched cohorts. Always report bootstrap confidence intervals.
  7. Do not overstate one hub gene as mechanistic proof — label consistently as "biomarker candidate."
  8. Do not treat immune-correlation evidence as causal immune regulation.
  9. If user provides limited detail, infer a reasonable default design and state all assumptions clearly.
  10. Do not produce only a flat methods list or literature summary.
  11. Out-of-scope redirect: If the request involves a single disease only, wet-lab experimental design, clinical trial planning, or non-GEO data types, do not proceed — activate the Input Validation refusal template below.

Input Validation

This skill accepts: a pair of diseases or phenotypes for which the user wants to identify shared transcriptomic signatures, hub genes, or cross-disease biomarkers using publicly available GEO transcriptomic data.

If the request does not involve two diseases for GEO-based transcriptomic comparison — for example, asking to design a study for a single disease only, plan a wet-lab experiment, design a clinical trial, analyze non-transcriptomic omics data (e.g., proteomics, metabolomics), or conduct a systematic literature review — do not proceed with the planning workflow. Instead respond:

"Dual-Disease Transcriptomic ML Planner is designed to generate GEO-based transcriptomic + machine learning study designs for pairs of diseases. Your request appears to be outside this scope. Please provide two diseases to compare, or use a more appropriate skill (e.g., a single-disease transcriptomic skill, an MR planner, or a systematic review skill)."

Reference Files

FileContentUsed In
references/tissue_and_tool_decisions.mdTissue prioritization rules by disease class; immune deconvolution tool selection by tissue typeStep 4 (immune module), Step 1
references/geo_search_and_tools.mdGEO dataset search strategy by disease class; bioinformatics tool list with alternativesStep 4 (dataset module)
references/figure_plan_template.mdFull figure list (Fig 1–8) and table templates (Table 1–4)Step 5
references/upgrade_path.mdPublication upgrade impact vs complexity tableStep 9

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in scientific-skills/Protocol Design/dual-disease-transcriptomic-ml-planner of aipoch/medical-research-skills.

  • SKILL.md
  • eval_report_dual-disease-transcriptomic-ml-planner_polished_result.json
  • references/figure_plan_template.md
  • references/geo_search_and_tools.md
  • references/tissue_and_tool_decisions.md
  • references/upgrade_path.md

Open the folder on GitHubat commit 686e09d

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Categories

Questions about Dual Disease Transcriptomic ML Planner

What does Dual Disease Transcriptomic ML Planner do?

Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair. Dual Disease Transcriptomic ML Planner is an agent skill from aipoch/medical-research-skills. Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair.

When should I use Dual Disease Transcriptomic ML Planner?

Dual Disease Transcriptomic ML Planner fits situations like: users want to identify shared DEGs; common hub genes; cross-disease biomarkers; shared molecular mechanisms between two diseases using public GEO data.

How do I install Dual Disease Transcriptomic ML Planner in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill dual-disease-transcriptomic-ml-planner -a claude-code`. Or copy the skill folder (scientific-skills/Protocol Design/dual-disease-transcriptomic-ml-planner in aipoch/medical-research-skills) into .claude/skills/dual-disease-transcriptomic-ml-planner in your project. Claude Code loads it when a task matches its description.

How do I install Dual Disease Transcriptomic ML Planner in Codex?

Run `npx skills add aipoch/medical-research-skills --skill dual-disease-transcriptomic-ml-planner -a codex`. Or copy the skill folder (scientific-skills/Protocol Design/dual-disease-transcriptomic-ml-planner in aipoch/medical-research-skills) into .agents/skills/dual-disease-transcriptomic-ml-planner in your project. Codex loads it when a task matches its description.

Can I use Dual Disease Transcriptomic ML Planner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill dual-disease-transcriptomic-ml-planner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dual-disease-transcriptomic-ml-planner, .gemini/skills/dual-disease-transcriptomic-ml-planner, .github/skills/dual-disease-transcriptomic-ml-planner and .opencode/skills/dual-disease-transcriptomic-ml-planner in your project.

What does Dual Disease Transcriptomic ML Planner need to run?

SKILL.md names no scripts, command-line tools or credentials: Dual Disease Transcriptomic ML Planner is instructions for the agent only.

Does Dual Disease Transcriptomic ML Planner access the network?

SKILL.md names 1 domain. As links in the text: ncbi.nlm.nih.gov. This is read from the text; nothing was executed.

Is Dual Disease Transcriptomic ML Planner safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Dual Disease Transcriptomic ML Planner use?

Dual Disease Transcriptomic ML Planner is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dual Disease Transcriptomic ML Planner use?

About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.6k tokens, read only when the agent opens those files.

What are the alternatives to Dual Disease Transcriptomic ML Planner?

Skills that share tags, products or a category with Dual Disease Transcriptomic ML Planner: Gtars Genomic Interval Toolkit (davila7/claude-code-templates, 32k stars), Bio Spatial Transcriptomics Spatial Preprocessing (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Bio Clip Seq M6a Clip (GPTomics/bioSkills, 1.2k stars) and Bio Imaging Mass Cytometry Data Preprocessing (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dual Disease Transcriptomic ML Planner?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,978 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.