Gsea

Q: Why use Gsea on TypingMind?

Because you install it once and use it with any model. Gsea is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

Q: How do I install Gsea in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/gsea. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Q: Which AI models can use Gsea?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

Q: How many AI models can I use with Gsea?

As many as you like. As long as a model supports skills, you can use Gsea with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Q: Is the Gsea AI skill free?

Yes. It is published on GitHub by aipoch under the MIT license. You only pay your own AI provider for the tokens you use.

Q: What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

Q: How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

OrganizationPopular

aipoch

gsea

Run GSEA on a ranked gene list and produce the enrichment table, running-score table, and enrichment plots.

Overview

Publisher	aipoch
Repository	`medical-research-skills`
Skill name	`gsea`
Stars	1.9K
Forks	175
Bundled files	11
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
11 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by aipoch on GitHub. Read the source before you install it.

Installation

Install the Gsea AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/gsea

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Gsea in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gsea on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gsea is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

When to read external files

Situation	Read	Purpose
Need algorithm details	`references/algorithm.md`	Statistical method and formulas
Need to run an analysis	`scripts/main.R`	Full command reference
Hit an error	`references/troubleshooting.md`	Look up error codes and fixes
Need CLI examples	`references/cli-guide.md`	Worked argument examples

Scope

Use this skill for:

Running GSEA on a gene list ranked by a statistic
Generating enrichment curve plots from existing enrichGSEA.csv and gsea_running_scores.csv
Smoke-testing the pipeline with tests/data/sample_deg_results.csv

Do not use it for:

Differential expression on raw expression matrices
Single-sample ssGSEA
Network analysis or multi-omics integration

Usage

Analysis mode: Rscript scripts/main.R --input tests/data/sample_deg_results.csv --outdir ./GSEA_analysis --type KEGG --species human --seed 42 --timeout 300

Plot mode: Rscript scripts/main.R --running_file ./GSEA_analysis/Table/gsea_running_scores.csv --enrich_file ./GSEA_analysis/Table/enrichGSEA.csv --plot_output ./GSEA_analysis/plot/gsea_plot.pdf --top_n 5 --plot_format pdf --seed 42 --timeout 300

See references/cli-guide.md for more.

Mode selection:

Passing only --input runs analysis mode
Passing both --running_file and --enrich_file runs plot mode
If both sets of arguments are provided, plot mode takes precedence; analysis mode is skipped and a warning is logged

Arguments

Analysis-mode arguments

Short	Long	Type	Default	Required	Description
`-i`	`--input`	character	`NULL`	yes	Input CSV file
`-o`	`--outdir`	character	`GSEA_analysis`	no	Output directory
`-g`	`--gene_col`	character	`name`	no	Gene column name
`-f`	`--fc_col`	character	`logFC`	no	Ranking-statistic column name
`-t`	`--type`	character	`KEGG`	no	Gene-set type: `KEGG`, `HALLMARKS`, `GO_BP`, `GO_MF`, `GO_CC`. With a preloaded RDS, `HALLMARKS` is automatically mapped to the asset key `Hallmarks`
`-s`	`--species`	character	`human`	no	Species: `human`, `mouse`, `rat`
`-p`	`--pvalue_cutoff`	numeric	`0.05`	no	Significance threshold
`-m`	`--method`	character	`fgsea`	no	GSEA backend: `fgsea` or `DOSE`
`-c`	`--chunk_size`	numeric	`1000`	no	Chunk size for large gene-set conversion
`-r`	`--rds_path`	character	`NULL`	no	Path to a pre-stored gene-set RDS
`-v`	`--verbose`	logical	`FALSE`	no	Verbose logging
	`--seed`	integer	`42`	no	Random seed
	`--timeout`	integer	`300`	no	Timeout in seconds; `<=0` disables it
`-h`	`--help`	logical	`FALSE`	no	Show help

Plot-mode arguments

Short	Long	Type	Default	Required	Description
	`--running_file`	character	`NULL`	yes	Path to `gsea_running_scores.csv`
	`--enrich_file`	character	`NULL`	yes	Path to `enrichGSEA.csv`
	`--plot_output`	character	`gsea_plot.pdf`	no	Output plot path
	`--plot_width`	numeric	`8`	no	Plot width
	`--plot_height`	numeric	`6`	no	Plot height
	`--plot_format`	character	`pdf`	no	Output format: `pdf` or `png`
	`--top_n`	numeric	`1`	no	Number of top pathways to plot when `geneSetID` is not given
	`--rank_by`	character	`p.adjust`	no	Column used to rank pathways
	`--geneSetID`	character	`""`	no	Comma-separated pathway IDs
	`--plot_title`	character	`""`	no	Plot title
	`--colors`	character	`#4DBBD5,#E64B35,#00A087,#F39B7F,#3C5488,#8491B4`	no	Color list
	`--base_size`	numeric	`11`	no	Base font size
	`--subplots`	character	`1,2,3`	no	Sub-panel indices to display
	`--rel_heights`	character	`1.5,0.8,1`	no	Relative panel heights
	`--NES_table`	logical	`TRUE`	no	Show NES annotation
	`--no_NES_table`	logical	`FALSE`	no	Disable NES annotation
	`--NES_label_size`	numeric	`4`	no	NES label font size
	`--NES_label_x`	numeric	`0.75`	no	NES label x position
	`--NES_label_y`	numeric	`0.75`	no	NES label y position
	`--NES_label_color`	character	`black`	no	NES label color
	`--NES_label_hjust`	numeric	`0`	no	NES label horizontal justification
	`--NES_label_vjust`	numeric	`1`	no	NES label vertical justification
	`--line_width`	numeric	`1`	no	ES line width
	`--dot_size`	numeric	`1.2`	no	ES dot size
	`--legend_position`	character	`auto`	no	Legend position
	`--legend_x`	numeric	`0.02`	no	Inset legend x coordinate
	`--legend_y`	numeric	`0.02`	no	Inset legend y coordinate
	`--legend_just_x`	numeric	`0`	no	Legend horizontal justification
	`--legend_just_y`	numeric	`0`	no	Legend vertical justification
	`--legend_text_size`	numeric	`9`	no	Legend text size
	`--legend_key_size`	numeric	`0.6`	no	Legend key size
	`--legend_bg_alpha`	numeric	`0`	no	Legend background alpha
	`--grid_major_color`	character	`grey92`	no	Major grid color
	`--grid_minor_color`	character	`grey92`	no	Minor grid color
	`--ylab_es`	character	`Enrichment Score`	no	ES panel y-axis title
	`--ylab_rank`	character	`Ranked List Metric`	no	Rank panel y-axis title
	`--xlab_rank`	character	`Rank in Ordered Dataset`	no	Rank panel x-axis title
	`--hit_height`	numeric	`1`	no	Hit-bar height
	`--hit_gap`	numeric	`0`	no	Hit-bar gap
	`--hit_linewidth`	numeric	`0.5`	no	Hit-bar line width
	`--rank_bar_alpha`	numeric	`0.9`	no	Rank-bar alpha
	`--rank_bar_height_ratio`	numeric	`0.3`	no	Rank-bar height ratio
	`--rank_metric_segment_color`	character	`grey`	no	Rank-line color
	`--rank_metric_segment_width`	numeric	`0.3`	no	Rank-line width
	`--rank_metric_segment_alpha`	numeric	`1`	no	Rank-line alpha
	`--pvalue_table`	logical	`FALSE`	no	Show p-value table
	`--ES_geom`	character	`line`	no	ES geometry: `line` or `dot`
	`--verbose`	logical	`FALSE`	no	Verbose logging
	`--seed`	integer	`42`	no	Random seed
	`--timeout`	integer	`300`	no	Timeout in seconds; `<=0` disables it
`-h`	`--help`	logical	`FALSE`	no	Show help

Input format

Analysis-mode input is a CSV with at least:

a gene column (default name name)
a ranking-statistic column (default name logFC)

Example:

csv
name,logFC,pvalue,padj
TP53,2.5,0.001,0.01
BRCA1,1.8,0.005,0.02
EGFR,-1.2,0.01,0.05

Value constraints:

type accepts KEGG, HALLMARKS, GO_BP, GO_MF, GO_CC
When using a preloaded RDS, HALLMARKS is automatically matched to the asset key Hallmarks
species accepts human, mouse, rat

Output files

File	Format	Description
`data/GSEA_list.rda`	RDA	Full GSEA result object
`Table/enrichGSEA.csv`	CSV	Enrichment result table
`Table/gsea_running_scores.csv`	CSV	Running-score table; if no enrichment passes, a header-only file is still written
`plot/`	directory	Plot output directory
`session_info.txt`	TXT	R version and package versions

enrichGSEA.csv mainly contains: ID, Description, NES, pvalue, p.adjust, core_enrichment.

Error handling

Common error codes:

SKILL_FILE_NOT_FOUND: input file does not exist
SKILL_MISSING_COLUMNS: required columns are missing
SKILL_EMPTY_DATA: input is empty, or empty after filtering
SKILL_INVALID_PARAMETER: an argument has an invalid value
SKILL_PACKAGE_NOT_FOUND: a required package is not installed
SKILL_ANALYSIS_FAILED: GSEA still failed after retries

Triage doc: references/troubleshooting.md

Exit codes:

0: success
1: failure

Testing

Minimal test dataset: tests/data/sample_deg_results.csv

Minimal command: Rscript scripts/main.R --input tests/data/sample_deg_results.csv --outdir ./test_output --type KEGG --species human --seed 42 --timeout 300 --verbose

Expected output:

./test_output/data/GSEA_list.rda
./test_output/Table/enrichGSEA.csv
./test_output/Table/gsea_running_scores.csv
./test_output/session_info.txt
If no significant enrichment is found, gsea_running_scores.csv is still written but contains only the header
Exit code 0

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gsea AI skill do?

Run GSEA on a ranked gene list and produce the enrichment table, running-score table, and enrichment plots.

Why use Gsea on TypingMind?

Because you install it once and use it with any model. Gsea is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gsea in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aipoch/medical-research-skills/tree/main/awesome-med-research-skills/Data%20Analysis/gsea. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gsea?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gsea?

As many as you like. As long as a model supports skills, you can use Gsea with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gsea AI skill free?

Yes. It is published on GitHub by aipoch under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Use it in TypingMind

Works with any AI model

Skill instructions

When to read external files

Scope

Usage

Arguments

Analysis-mode arguments

Plot-mode arguments

Input format

Output files

Error handling

Testing

Bundled files

Frequently asked questions

Related AI Skills

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