Alphagenome Single Variant Analysis
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
A skill your agent uses when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules…
Install Wgcna Analysis
$ npx skills add aipoch/medical-research-skills --skill wgcna-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aipoch/medical-research-skills wgcna-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/wgcna-analysis' .claude/skills/wgcna-analysis && rm -rf skills-srcUse ~/.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/
Install the "wgcna-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/wgcna-analysis into .claude/skills/wgcna-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wgcna-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/wgcna-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aipoch/medical-research-skills --skill wgcna-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aipoch/medical-research-skills wgcna-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/wgcna-analysis' .agents/skills/wgcna-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "wgcna-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/wgcna-analysis into .agents/skills/wgcna-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wgcna-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aipoch/medical-research-skills --skill wgcna-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aipoch/medical-research-skills wgcna-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/wgcna-analysis' .cursor/skills/wgcna-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "wgcna-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/wgcna-analysis into .cursor/skills/wgcna-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wgcna-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aipoch/medical-research-skills.git --path 'awesome-med-research-skills/Data Analysis/wgcna-analysis'--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aipoch/medical-research-skills --skill wgcna-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aipoch/medical-research-skills wgcna-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/wgcna-analysis' .gemini/skills/wgcna-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "wgcna-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/wgcna-analysis into .gemini/skills/wgcna-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wgcna-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aipoch/medical-research-skills wgcna-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aipoch/medical-research-skills --skill wgcna-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/wgcna-analysis' .github/skills/wgcna-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "wgcna-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/wgcna-analysis into .github/skills/wgcna-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wgcna-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aipoch/medical-research-skills --skill wgcna-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aipoch/medical-research-skills wgcna-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/'awesome-med-research-skills/Data Analysis/wgcna-analysis' .opencode/skills/wgcna-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "wgcna-analysis" agent skill from https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/wgcna-analysis into .opencode/skills/wgcna-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wgcna-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
wgcna-analysis- GitHub stars
- 1.9k
- Token cost
- ~2.9k tokens
- SKILL.md length
- 1,115 words
- Files
- 17 (incl. scripts, references)
- Skills in repo
- 578
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules…
- Works in 5 steps: Load and validate inputs → Filter variable genes → Build the WGCNA network → …
- Building a weighted gene co-expression network from a bulk expression matrix and a sample group file
- SKILL.md covers When to Read External Files, When Not to Use, Usage and Arguments, plus 6 more sections
- Runs R scripts from its folder
What it does
Wgcna Analysis is an agent skill from aipoch/medical-research-skills. Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files, including scripts and reference files (for example `eval_report_wgcna-analysis_result.json`, `references/algorithm.md` and `references/cli-guide.md`).
It sits in Research & Science, covering Bioinformatics. 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
- Building a weighted gene co-expression network from a bulk expression matrix and a sample group file
- Filtering variable genes by MAD
- Identifying co-expression modules with WGCNA
- Correlating modules with traits
Example prompts
- “/wgcna-analysis”
Workflow steps
5 steps, taken from the step headings in SKILL.md.
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
Ships 9 files in scripts/ (R), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Wgcna Analysis loads about 2.9k tokens when it runs, and up to ~8.2k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 1,115 words of instructions outside code blocks.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 1,115 words, ~2,912 tokens.
Download SKILL.mdSave it as
.claude/skills/wgcna-analysis/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.- name
- wgcna-analysis
- description
- Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.
- license
- MIT
- author
- AIPOCH
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 0Arguments
| 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 |
Input Format
Expression Matrix (input_file)
CSV file with gene identifiers in the first column and sample IDs in the header row. All expression columns must be numeric.
,TCGA-78-7156,TCGA-44-6774,TCGA-69-A59K
A1BG,2.612908495,2.077948602,3.865545859
A1BG-AS1,3.526391179,3.221923473,4.684308865
A1CF,1.179552278,1.136255654,1.188781778Requirements:
- First column contains non-empty gene IDs
- Sample column names are unique and non-empty
- Expression values are numeric
- After QC, the matrix must remain large enough for WGCNA
Group File (group_file)
CSV file with at least two columns: one sample ID column and one group column.
sample,group
TCGA-78-7156,Control
TCGA-44-6774,Control
TCGA-69-A59K,Control
TCGA-44-6147,CaseRequirements:
- Sample IDs must match expression matrix column names
- Sample IDs must be unique
- Group values must be non-empty
- At least two groups are required
Output Files
All files are written under output_dir.
| File | Format | Description |
|---|---|---|
session_info.txt | text | R session information and package versions |
plots/soft_threshold.pdf | Scale-free topology fit and mean connectivity across tested powers | |
plots/sample_clustering.pdf | Sample dendrogram with group color annotation | |
plots/gene_cluster_modules.pdf | Gene dendrogram with module color labels | |
plots/module_eigengene_heatmap.pdf | Eigengene adjacency heatmap | |
plots/tom_heatmap.pdf | TOM-based network heatmap for a sampled subset of genes | |
plots/module_trait_relationships.pdf | Heatmap of module-trait correlations and p-values | |
plots/module_membership_vs_trait_<module>_<trait>.pdf | Scatter plot for each exported module against the selected trait | |
tables/sft_fit_indices.csv | CSV | Soft-threshold fit statistics returned by pickSoftThreshold() |
tables/module_trait_cor.csv | CSV | Module eigengene vs trait correlation matrix |
tables/module_trait_p.csv | CSV | P-value matrix corresponding to module-trait correlations |
tables/module_assignments.csv | CSV | Per-gene module assignments |
tables/selected_modules.csv | CSV | Ranked summary of exported modules for the selected trait |
tables/module_genes_<module>.csv | CSV | Gene-level export for each selected module |
tables/analysis_summary.csv | CSV | Summary of samples, retained genes, selected power, selected trait, and exported modules |
data/net.rds | RDS | Full WGCNA network object returned by blockwiseModules() |
data/analysis_objects.rds | RDS | Saved analysis objects, trait data, statistics, and selected modules |
data/wgcna_tom-block.*.RData | RData | Saved TOM block file(s) produced by blockwiseModules() |
Show full SKILL.md (424 more words)Show less
Workflow
Step 1: Load and validate inputs
- Validate file existence and CLI parameters
- Read the expression matrix and group file
- Check sample ID consistency between files
Step 2: Filter variable genes
- Compute per-gene MAD values
- Keep genes above the quantile-based MAD cutoff
- Optionally cap the retained genes with
max_genes
Step 3: Build the WGCNA network
- Transpose the matrix into the WGCNA sample-by-gene format
- Select soft-threshold power with
pickSoftThreshold() - Build modules with
blockwiseModules()
Step 4: Associate modules with traits
- Encode groups as a trait matrix
- Compute module-trait and gene-trait correlations
- Rank modules by absolute correlation with the selected trait
Step 5: Export results
- Save summary tables, module assignments, and per-module gene exports
- Generate dendrogram, heatmap, TOM, and scatter plot PDFs
- Save session info and serialized R objects
Error Handling
Common Errors
| Error | Cause | Solution |
|---|---|---|
SKILL_FILE_NOT_FOUND | Input file path is wrong or a required TOM file is missing | Check file paths and rerun the analysis from the start |
SKILL_MISSING_COLUMNS | Required sample, group, or gene identifier columns are missing or empty | Verify the CSV header and column names |
SKILL_EMPTY_DATA | Input is empty, no genes pass filtering, or the filtered matrix is too small for WGCNA | Check data quality and relax filtering settings |
SKILL_INVALID_PARAMETER | A CLI value is missing, out of range, or not in the allowed choices | Review parameter values in the command |
SKILL_SAMPLE_MISMATCH | Group file sample IDs do not match expression matrix sample IDs | Make sample identifiers identical across both files |
SKILL_PACKAGE_NOT_FOUND | A required R package is not installed | Install missing packages before running |
IF error persists, READ: references/troubleshooting.md
Testing
Smoke Test
# Check CLI help
Rscript scripts/main.R --help
# Run with bundled test data
Rscript scripts/main.R \
--input_file tests/data/expression.csv \
--group_file tests/data/group.csv \
--output_dir tests/output-skill/ \
--sample_column sample \
--group_column group \
--network_type unsigned \
--cor_type pearson \
--mad_quantile 0.25 \
--min_mad 0.01 \
--max_genes 500 \
--min_module_size 20 \
--merge_cut_height 0.25 \
--soft_r2_cutoff 0.8 \
--module_of_interest auto \
--top_modules 1 \
--tom_sample_size 150 \
--chunk_size 0 \
--seed 42 \
--timeout_seconds 0
# Validate required outputs
Rscript tests/validate_outputs.R tests/output-skill/Validation Commands
Rscript tests/validate_outputs.R tests/output-skill/Expected outputs include session_info.txt, the plot PDFs, table CSVs, and the serialized R objects listed above.
Additional Verification
- When exporting explicit modules, confirm the matching
tables/module_genes_<module>.csvfiles exist. - When generating module membership plots, confirm the matching
plots/module_membership_vs_trait_<module>_<trait>.pdffiles exist. - If chunked loading is enabled, compare
tables/analysis_summary.csvagainst a non-chunked run on the same input.
Notes
- Re-running the same command with the same seed is expected to be reproducible.
- Failed runs may still create
output_dirandsession_info.txtbefore exiting; this is expected and safe to overwrite on retry.
Implementation Checklist
- CLI parsing with
optparse -
set.seed()for reproducibility - Dependency checks with explicit package loading
- Relative
source()paths viaget_script_dir() - Optional timeout parameter
- Optional chunked loading for large matrices
- Session info recording
- Error handling with
SKILL_*codes -
SKILL.mdparameters match the implemented CLI - File reading instructions in
SKILL.md - Test data provided in
tests/data/ - Smoke-test output validation script provided
Last updated: 2026-04-17 | Version: 1.0.0
© 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 16 other files (scripts, references) in awesome-med-research-skills/Data Analysis/wgcna-analysis of aipoch/medical-research-skills.
- SKILL.md
- eval_report_wgcna-analysis_result.json
- references/algorithm.md
- references/cli-guide.md
- references/troubleshooting.md
- scripts/chunk_io.R
- scripts/functions.R
- scripts/io.R
- scripts/main.R
- scripts/plotting.R
- scripts/run_analysis.R
- scripts/utils.R
- scripts/wgcna_core.R
- scripts/wgcna_selection.R
- tests/data/expression.csv
- tests/data/group.csv
- tests/validate_outputs.R
Open the folder on GitHubat commit 686e09d
Compare with similar skills
Wgcna Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Wgcna Analysis this skillaipoch/medical-research-skills | 1.9k | — | ~2.9k | Automated safety check: Pass | MIT | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Clinvar Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.9k | Automated safety check: Notes | Apache-2.0 | |
| Metabolic Study Planneraiming-lab/AutoResearchClaw | 15k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Dbsnp Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.4k | Automated safety check: Notes | Apache-2.0 |
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Questions about Wgcna Analysis
What does Wgcna Analysis do?
A skill your agent uses when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules…. Wgcna Analysis is an agent skill from aipoch/medical-research-skills. Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables.
When should I use Wgcna Analysis?
Wgcna Analysis fits situations like: building a weighted gene co-expression network from a bulk expression matrix and a sample group file; filtering variable genes by MAD; identifying co-expression modules with WGCNA; correlating modules with traits.
How do I install Wgcna Analysis in Claude Code?
Run `npx skills add aipoch/medical-research-skills --skill wgcna-analysis -a claude-code`. Or copy the skill folder (awesome-med-research-skills/Data Analysis/wgcna-analysis in aipoch/medical-research-skills) into .claude/skills/wgcna-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Wgcna Analysis in Codex?
Run `npx skills add aipoch/medical-research-skills --skill wgcna-analysis -a codex`. Or copy the skill folder (awesome-med-research-skills/Data Analysis/wgcna-analysis in aipoch/medical-research-skills) into .agents/skills/wgcna-analysis in your project. Codex loads it when a task matches its description.
Can I use Wgcna Analysis 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 wgcna-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wgcna-analysis, .gemini/skills/wgcna-analysis, .github/skills/wgcna-analysis and .opencode/skills/wgcna-analysis in your project.
What does Wgcna Analysis need to run?
Going by SKILL.md and its folder, Wgcna Analysis needs R for the scripts in its folder.
Does Wgcna Analysis access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Wgcna Analysis 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Wgcna Analysis use?
Wgcna Analysis 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 Wgcna Analysis use?
About 2.9k tokens (SKILL.md is roughly 12k 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 5.3k tokens, read only when the agent opens those files.
What are the alternatives to Wgcna Analysis?
Skills that share tags, products or a category with Wgcna Analysis: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Wgcna Analysis?
aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 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.