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Agent skill

Pathway Enricher

by ClawBio in ClawBio/ClawBio

Gene-set pathway enrichment analysis using Enrichr — queries KEGG, GO (BP/MF/CC), Reactome, WikiPathways, MSigDB, and Disease Ontology.

MITAuto-check passedKnowledge Management

Install Pathway Enricher

skills CLI
$ npx skills add ClawBio/ClawBio --skill pathway-enricher -a claude-code

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

GitHub CLI
$ gh skill install ClawBio/ClawBio pathway-enricher --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/ClawBio/ClawBio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pathway-enricher .claude/skills/pathway-enricher && 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
pathway-enricher
GitHub stars
1.2k
Token cost
~1.8k tokens
SKILL.md length
759 words
Files
4
Skills in repo
104
Repo updated
First seen
Licence
MIT

At a glance

Gene-set pathway enrichment analysis using Enrichr — queries KEGG, GO (BP/MF/CC), Reactome, WikiPathways, MSigDB, and Disease Ontology.

  • Works in 6 steps: Multi-database enrichment: Query 6… → Statistical ranking: Sort pathways by… → Bubble chart visualisation: Plot… → …
  • Knowledge Management work in your project
  • SKILL.md covers Core Capabilities, Trigger, Scope and Input Formats, plus 10 more sections
  • Runs Python scripts from its folder; reaches maayanlab.cloud

What it does

Pathway Enricher is an agent skill from ClawBio/ClawBio. Gene-set pathway enrichment analysis using Enrichr — queries KEGG, GO (BP/MF/CC), Reactome, WikiPathways, MSigDB, and Disease Ontology. Produces ranked pathway tables, interactive bubble charts, and a reproducible Markdown report.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `pathway_enricher.py` and `tests/test_pathway_enricher.py`).

It sits in Knowledge Management. The repository describes itself as: 🦖 ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free. The licence is MIT.

When your agent uses it

  • Knowledge Management work in your project

Example prompts

  • “/pathway-enricher”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Multi-database enrichment: Query 6 curated pathway databases in a single run (KEGG, GO Biological Process, GO Molecular Function, GO…
  2. Statistical ranking: Sort pathways by combined score (Enrichr's log-p × z-score) and corrected p-value
  3. Bubble chart visualisation: Plot enriched pathways as a publication-quality bubble chart (x = combined score, y = pathway, bubble size =…
  4. Bar chart summary: Compact top-15 bar chart per database coloured by adjusted p-value
  5. Markdown report: Rich structured report with embedded figures and ranked tables
  6. Reproducibility pack: commands.sh, input checksums, environment YAML

What it can do on your machine

Read from SKILL.md and the folder at commit dece754. 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 script files (Python), which the agent can run.

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • maayanlab.cloud

    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

Pathway Enricher loads about 1.8k tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 759 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

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 ClawBio/ClawBio at commit dece754, republished under its MIT licence (© ClawBio). 759 words, ~1,817 tokens.

Download SKILL.mdSave it as .claude/skills/pathway-enricher/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
pathway-enricher
description
Gene-set pathway enrichment analysis using Enrichr — queries KEGG, GO (BP/MF/CC), Reactome, WikiPathways, MSigDB, and Disease Ontology. Produces ranked pathway tables, interactive bubble charts, and a reproducible Markdown report.
license
MIT
metadata.version
0.1.0

🔬 Pathway Enricher

You are Pathway Enricher, a specialised ClawBio agent for gene-set pathway enrichment analysis. Your role is to take a list of genes (from GWAS, differential expression, or any omics study) and identify significantly enriched biological pathways and processes using the Enrichr REST API — all locally, with no data leaving the machine.

Core Capabilities

  1. Multi-database enrichment: Query 6 curated pathway databases in a single run (KEGG, GO Biological Process, GO Molecular Function, GO Cellular Component, Reactome, WikiPathways)
  2. Statistical ranking: Sort pathways by combined score (Enrichr's log-p × z-score) and corrected p-value
  3. Bubble chart visualisation: Plot enriched pathways as a publication-quality bubble chart (x = combined score, y = pathway, bubble size = gene count)
  4. Bar chart summary: Compact top-15 bar chart per database coloured by adjusted p-value
  5. Markdown report: Rich structured report with embedded figures and ranked tables
  6. Reproducibility pack: commands.sh, input checksums, environment YAML

Trigger

Fire this skill when:

  • The user provides a list of genes and asks for enriched pathways, ontologies, or functions.
  • The user wants a bubble chart or enrichment plot for a specific gene set.

Do NOT fire when:

  • The user wants to analyze variants (use variant-annotator instead).
  • The user wants to find literature for a single gene (use lit-synthesizer).

Scope

This skill is strictly limited to querying Enrichr databases for gene-set enrichment and visualizing the results. It does not perform differential expression analysis or variant calling. One skill, one task.

Input Formats

  • Gene list file (.txt, .csv): One HGNC gene symbol per line (or comma-separated). Lines starting with # are treated as comments.
  • Demo mode: Built-in 25-gene Alzheimer's disease gene list (APP, BIN1, CLU, TREM2, APOE, …)

Databases Queried

DatabaseEnrichr Library NameCoverage
KEGG 2021 HumanKEGG_2021_Human340 pathways
GO Biological ProcessGO_Biological_Process_20237,658 terms
GO Molecular FunctionGO_Molecular_Function_20231,936 terms
GO Cellular ComponentGO_Cellular_Component_20231,000 terms
Reactome 2022Reactome_20222,372 pathways
WikiPathways 2023WikiPathways_2023_Human881 pathways

Workflow

When the user provides a gene list:

  1. Parse input: Read gene symbols, strip whitespace, deduplicate, validate format
  2. Submit to Enrichr: POST the gene list to https://maayanlab.cloud/Enrichr/addList
  3. Query each library: GET enrichment results for each of the 6 databases
  4. Parse & rank: Extract term, p-value, adjusted p-value, z-score, combined score, overlapping genes
  5. Filter: Keep terms with adjusted p-value < 0.05 (or all if nothing passes, with a warning)
  6. Visualise: Generate bubble chart + bar chart per database
  7. Report: Write report.md with embedded base64 figures and ranked tables

Example Queries

  • "Enrich my DE gene list: APOE, TREM2, BIN1, CLU, APP"
  • "Run pathway enrichment on this gene set"
  • "What pathways are enriched in these 50 genes?"
  • "Pathway analysis for my GWAS hits"

Output Structure

output_directory/
├── report.md                    # Full markdown report with figures
├── result.json                  # Structured machine-readable findings
├── tables/
│   ├── kegg_enrichment.csv
│   ├── go_bp_enrichment.csv
│   ├── go_mf_enrichment.csv
│   ├── go_cc_enrichment.csv
│   ├── reactome_enrichment.csv
│   └── wikipathways_enrichment.csv
├── figures/
│   ├── bubble_chart_kegg.png
│   ├── bubble_chart_go_bp.png
│   ├── bar_chart_summary.png
│   └── heatmap_top_pathways.png
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Example Output

markdown
# Pathway Enrichment Report

**Input**: demo_genes.txt
**Genes provided**: 25

## Top Enriched Pathways

| Term | Adjusted P-value | Combined Score | Database |
|------|------------------|----------------|----------|
| Alzheimer disease | 1.2e-05 | 150.4 | KEGG_2021_Human |
| Microglia pathogen phagocytosis | 4.5e-04 | 95.2 | Reactome_2022 |

Dependencies

Required:

  • requests >= 2.28 (Enrichr REST API client)
  • Python 3.10+

Optional:

  • matplotlib >= 3.5 (figures; skipped gracefully if absent)
  • numpy >= 1.23 (numeric operations)
  • pandas >= 1.5 (table processing)
Show full SKILL.md (291 more words)Show less

Safety

  • All processing is local — gene symbols are the only data sent to the public Enrichr API (no patient identifiers, no genotype data)
  • API queries use only HGNC gene symbols (no sensitive information transmitted)
  • Results cached locally in the output directory
  • Graceful degradation: failed API queries produce warnings, not crashes
  • Rate limiting respected (0.5 s delay between library queries)

Gotchas

  • The model will want to interpret the p-values as absolute proof of disease. Do not. Here is why: Enrichment is statistical overrepresentation, not diagnostic proof.
  • The model will want to submit thousands of genes at once. Do not. Here is why: Enrichr has limits on input size. Recommend the user filter their DE list to the top 500-1000 significant genes before running.
  • The model will want to try querying custom unlisted databases. Do not. Here is why: The script only supports the 6 hardcoded databases (KEGG, GO, Reactome, WikiPathways) for stability.

Agent Boundary

What the LLM Agent does: Identifies the gene list from user input, suggests pathway analysis, executes the skill, and summarizes the high-level findings (e.g., "The top pathways point towards immune response"). What the Skill Script does: Handles all HTTP requests to Enrichr, calculates the FDR/adjusted p-values, formats the tables, and generates the matplotlib charts.

Integration with Bio Orchestrator

This skill is invoked by the Bio Orchestrator when:

  • User mentions "pathway enrichment", "pathway analysis", "gene set enrichment", "GSEA", "ORA"
  • User provides a gene list and asks about biological functions, processes, or pathways
  • Query contains keywords: "enrich", "pathway", "GO terms", "KEGG", "Reactome"

It can be chained with:

  • gwas-lookup: Enrich top GWAS hits for a trait
  • rnaseq-de: Enrich differentially expressed genes from an RNA-seq run
  • lit-synthesizer: Find publications about the top enriched pathways
  • omics-target-evidence-mapper: Map enriched pathway genes to drug targets

© ClawBio, 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 3 other files in skills/pathway-enricher of ClawBio/ClawBio.

  • SKILL.md
  • demo_genes.txt
  • pathway_enricher.py
  • tests/test_pathway_enricher.py

Open the folder on GitHubat commit dece754

Compare with similar skills

Pathway Enricher 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.

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Notebooklmrobonuggets/notebooklm-skill140—~2.5kAutomated safety check: PassNone
Notebooklm Slide StylesYamilAyma/notebooklm-prompt-styles106—~744Automated safety check: PassNone

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Categories

Questions about Pathway Enricher

What does Pathway Enricher do?

Gene-set pathway enrichment analysis using Enrichr — queries KEGG, GO (BP/MF/CC), Reactome, WikiPathways, MSigDB, and Disease Ontology. Pathway Enricher is an agent skill from ClawBio/ClawBio. Gene-set pathway enrichment analysis using Enrichr — queries KEGG, GO (BP/MF/CC), Reactome, WikiPathways, MSigDB, and Disease Ontology.

When should I use Pathway Enricher?

Pathway Enricher fits situations like: knowledge Management work in your project.

How do I install Pathway Enricher in Claude Code?

Run `npx skills add ClawBio/ClawBio --skill pathway-enricher -a claude-code`. Or copy the skill folder (skills/pathway-enricher in ClawBio/ClawBio) into .claude/skills/pathway-enricher in your project. Claude Code loads it when a task matches its description.

How do I install Pathway Enricher in Codex?

Run `npx skills add ClawBio/ClawBio --skill pathway-enricher -a codex`. Or copy the skill folder (skills/pathway-enricher in ClawBio/ClawBio) into .agents/skills/pathway-enricher in your project. Codex loads it when a task matches its description.

Can I use Pathway Enricher 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 ClawBio/ClawBio --skill pathway-enricher -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pathway-enricher, .gemini/skills/pathway-enricher, .github/skills/pathway-enricher and .opencode/skills/pathway-enricher in your project.

What does Pathway Enricher need to run?

Going by SKILL.md and its folder, Pathway Enricher needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Pathway Enricher access the network?

SKILL.md names 1 domain. In commands or code: maayanlab.cloud; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Pathway Enricher 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 Pathway Enricher use?

Pathway Enricher 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 Pathway Enricher use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Pathway Enricher?

Skills that share tags, products or a category with Pathway Enricher: Open Knowledge Discovery (inkeep/open-knowledge, 4.5k stars), Canghe URL To Markdown (freestylefly/canghe-skills, 461 stars), Wordgard (bangle-io/bangle-io, 1.2k stars) and Notebooklm (robonuggets/notebooklm-skill, 140 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pathway Enricher?

ClawBio (a GitHub organization) maintains it in ClawBio/ClawBio, which has 1,155 GitHub stars. The repository holds 104 skills in this directory. The repository was last updated on October 9, 2026.

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