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Agent skill

Proteomics Enrichment

by TianGzlab in TianGzlab/OmicsClaw

Load for Fisher over-representation analysis of protein identifiers against caller-supplied pathways.

Apache-2.0Auto-check passedResearch & Science

Install Proteomics Enrichment

skills CLI
$ npx skills add TianGzlab/OmicsClaw --skill proteomics-enrichment -a claude-code

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

GitHub CLI
$ gh skill install TianGzlab/OmicsClaw proteomics-enrichment --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/TianGzlab/OmicsClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/proteomics/proteomics-enrichment .claude/skills/proteomics-enrichment && 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
proteomics-enrichment
GitHub stars
161
Token cost
~1.2k tokens
SKILL.md length
470 words
Files
9 (incl. references)
Skills in repo
88
Repo updated
First seen
Licence
Apache-2.0

At a glance

Load for Fisher over-representation analysis of protein identifiers against caller-supplied pathways.

  • Tasks that involve Bioinformatics
  • SKILL.md covers When to use, Use from a step, API and Methods and parameters, plus 5 more sections
  • Runs Python scripts from its folder; calls python

What it does

Proteomics Enrichment is an agent skill from TianGzlab/OmicsClaw. Load for Fisher over-representation analysis of protein identifiers against caller-supplied pathways. Skip rank-based GSEA (use bulkrna-enrichment) or protein differential testing (use proteomics-de).

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `_api.py`, `examples/example_step.py` and `prot_enrichment.py`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: Conversational & memory-enabled AI research partner for multi-omics analysis. CLI + Desktop App (installers in Releases). From biological idea to full research paper. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Bioinformatics

Example prompts

  • “/proteomics-enrichment”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 90a3bec. 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.

    Shell commands in SKILL.md call:

    • python

    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

Proteomics Enrichment loads about 1.2k tokens when it runs, and up to ~1.4k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 470 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.4k

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 TianGzlab/OmicsClaw at commit 90a3bec, republished under its Apache-2.0 licence (© TianGzlab). 470 words, ~1,157 tokens.

Download SKILL.mdSave it as .claude/skills/proteomics-enrichment/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
proteomics-enrichment
description
Load for Fisher over-representation analysis of protein identifiers against caller-supplied pathways. Skip rank-based GSEA (use bulkrna-enrichment) or protein differential testing (use proteomics-de).
trigger
proteomics enrichment, pathway analysis, ORA
tags
proteomics, enrichment

proteomics-enrichment

When to use

ORA uses one-sided Fisher tests and BH correction. Pass an explicit pathway_db and a background_size matching the measured universe. Use existing search-engine tables; this skill does not search raw spectra.

Use from a step

python
from skills._sdk.notebook import load_skill, write_output
library = load_skill('proteomics-enrichment')
data = library.demo_data(random_state=42)
result = library.enrich(data['protein_id'].tolist(), pathway_db=library.demo_pathways())
write_output(result, 'tables/enrichment_results.csv')

For real data, use read_input and pass any read_table helper as reader=. The executable examples/example_step.py also checks the result and writes a Figure.

API

<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->
enrich(genes: list[str], *, pathway_db: dict | None=None, background_size: int | None=None, method: str='ora') -> pd.DataFrame

Run one-sided Fisher tests with BH correction against an explicit pathway library.

:param genes: Protein or gene identifiers in the same identifier space as pathway_db. :param pathway_db: Required pathway-to-member mapping; no default biological database is assumed. :param background_size: CLI default None uses the input/library union plus at least one background-only member. :param method: CLI default ora is the only implemented method. :returns: Ranked pathway overlaps, odds ratios and adjusted p values. :raises ValueError: The library is absent, the method is invalid, or the background is too small.

run_info(table: pd.DataFrame, *, keep: bool=True) -> dict

Read enrichment diagnostics.

:param table: Output of enrich. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate dictionary with library provenance and summary. :raises TypeError: The input is not a DataFrame.

enrichment_figure(table: pd.DataFrame, *, top_n: int=10)

Plot leading pathways by enrichment ratio.

:param table: Enrichment results sorted by p value. :param top_n: Display ten pathways by default; change for a larger result table. :returns: A matplotlib Figure without writing files. :raises KeyError: pathway or enrichment_ratio is absent.

demo_data(*, random_state: int=42) -> pd.DataFrame

Generate a synthetic significant-protein list.

:param random_state: CLI seed 42; change for another simulation. :returns: Eighteen example identifiers with simulated statistics. :raises ValueError: The seed is invalid.

Show full SKILL.md (197 more words)Show less
demo_pathways() -> dict

Return the eight small illustrative pathway sets used by --demo.

:returns: A separate mapping, not a production pathway database. :raises RuntimeError: No runtime failures are expected.

<!-- api:end -->

Methods and parameters

ORA uses one-sided Fisher tests and BH correction. Pass an explicit pathway_db and a background_size matching the measured universe. Functions return new DataFrames. run_info(result) reads diagnostic attrs; use keep=False before serialization when those attrs are not needed.

Gotchas

  • enrich requires pathway_db. demo_pathways contains eight illustrative sets and is only for demonstrations. The CLI requires --pathways for non-demo input; --species is recorded only.
  • demo_data uses seed 42, matching the CLI; every demo is synthetic.
  • run_info lives in DataFrame attrs and is not preserved by CSV serialization.

Inputs and outputs

The CLI reads CSV tables and writes:

  • tables/enrichment_results.csv
  • report.md
  • result.json
  • demo_proteins.csv is written only with --demo.

Functions return data and Figures without writing files. Steps own their outputs. Demo mode also writes its synthetic input when the original CLI used a file.

CLI

bash
python skills/proteomics/proteomics-enrichment/prot_enrichment.py --demo --output /tmp/proteomics_enrichment

For real input replace --demo with --input <table>. Non-demo enrichment also requires --pathways <pathways.json>, a pathway-to-members object.

See also

  • references/methodology.md
  • references/parameters.md
  • references/output_contract.md
  • proteomics-data-import for protein-table normalization; proteomics-de for comparisons.

Dependencies

numpy, pandas, matplotlib, scipy

© TianGzlab, Apache-2.0. 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 8 other files (references) in skills/proteomics/proteomics-enrichment of TianGzlab/OmicsClaw.

  • SKILL.md
  • _api.py
  • examples/example_step.py
  • prot_enrichment.py
  • references/methodology.md
  • references/output_contract.md
  • references/parameters.md
  • tests/test_api.py
  • tests/test_cli.py

Open the folder on GitHubat commit 90a3bec

Compare with similar skills

Proteomics Enrichment 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.

Proteomics Enrichment compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Proteomics Enrichment this skillTianGzlab/OmicsClaw161—~1.2kAutomated safety check: PassApache-2.0
Scanpy Single-Cell Analysisdavila7/claude-code-templates33k15 repos~2.8kAutomated safety check: PassMIT
deepTools NGS Toolkitdavila7/claude-code-templates33k12 repos~4.5kAutomated safety check: PassMIT
LaminDB Biological Data Managementdavila7/claude-code-templates33k12 repos~3.6kAutomated safety check: PassMIT
PyDESeq2 Differential Expressiondavila7/claude-code-templates33k11 repos~4kAutomated safety check: PassMIT
Gtars Genomic Interval Toolkitdavila7/claude-code-templates33k11 repos~1.9kAutomated safety check: PassMIT

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Categories

Questions about Proteomics Enrichment

What does Proteomics Enrichment do?

Load for Fisher over-representation analysis of protein identifiers against caller-supplied pathways. Proteomics Enrichment is an agent skill from TianGzlab/OmicsClaw. Load for Fisher over-representation analysis of protein identifiers against caller-supplied pathways.

When should I use Proteomics Enrichment?

Proteomics Enrichment fits situations like: tasks that involve Bioinformatics.

How do I install Proteomics Enrichment in Claude Code?

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

How do I install Proteomics Enrichment in Codex?

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

Can I use Proteomics Enrichment 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 TianGzlab/OmicsClaw --skill proteomics-enrichment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/proteomics-enrichment, .gemini/skills/proteomics-enrichment, .github/skills/proteomics-enrichment and .opencode/skills/proteomics-enrichment in your project.

What does Proteomics Enrichment need to run?

Going by SKILL.md and its folder, Proteomics Enrichment needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Proteomics Enrichment 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 Proteomics Enrichment 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 Proteomics Enrichment use?

Proteomics Enrichment is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Proteomics Enrichment use?

About 1.2k tokens (SKILL.md is roughly 4.6k 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 228 tokens, read only when the agent opens those files.

What are the alternatives to Proteomics Enrichment?

Skills that share tags, products or a category with Proteomics Enrichment: Scanpy Single-Cell Analysis (davila7/claude-code-templates, 33k stars), deepTools NGS Toolkit (davila7/claude-code-templates, 33k stars), LaminDB Biological Data Management (davila7/claude-code-templates, 33k stars) and PyDESeq2 Differential Expression (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Proteomics Enrichment?

TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on October 7, 2026.

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