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Agent skill

Genomics Epigenomics

by TianGzlab in TianGzlab/OmicsClaw

Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics.

Apache-2.0Auto-check passedResearch & Science

Install Genomics Epigenomics

skills CLI
$ npx skills add TianGzlab/OmicsClaw --skill genomics-epigenomics -a claude-code

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

GitHub CLI
$ gh skill install TianGzlab/OmicsClaw genomics-epigenomics --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/genomics/genomics-epigenomics .claude/skills/genomics-epigenomics && 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
genomics-epigenomics
GitHub stars
161
Token cost
~1.1k tokens
SKILL.md length
390 words
Files
9 (incl. references)
Skills in repo
88
Repo updated
First seen
Licence
Apache-2.0

At a glance

Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics.

  • 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

Genomics Epigenomics is an agent skill from TianGzlab/OmicsClaw. Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Skip when calling peaks from BAM (run MACS / Genrich externally first); working with single-cell ATAC (use scatac-preprocessing).

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including reference files (for example `_api.py`, `examples/example_step.py` and `genomics_epigenomics.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

  • “/genomics-epigenomics”

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

Genomics Epigenomics loads about 1.1k tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 390 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.4k

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). 390 words, ~1,089 tokens.

Download SKILL.mdSave it as .claude/skills/genomics-epigenomics/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
genomics-epigenomics
description
Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Skip when calling peaks from BAM (run MACS / Genrich externally first); working with single-cell ATAC (use scatac-preprocessing).
trigger
epigenomics, ATAC-seq, ChIP-seq, peak calling, MACS, motif, chromatin
tags
genomics, epigenomics, atac-seq, chip-seq, cut-tag, peaks, macs, bed

genomics-epigenomics

When to use

Load this skill for the file-based analysis named in the description. The function library and CLI share the same calculations; no external aligner, assembler, caller or annotation service is started.

Use from a step

python
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("genomics-epigenomics")
data = read_input("input.bed", reader=library.read_records)
result = library.analyze(data)
write_output(result, "tables/result.csv")
write_output(library.distribution_figure(result), "figures/distribution.png")

Run examples/example_step.py through the step runner for a small, hand-worked synthetic fixture. It asserts known summary values. The reader materializes the input in memory; use bounded FASTQ reads or pre-filter large genomic files before loading them.

API

<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->
read_records(path: str | Path) -> pd.DataFrame

Read records through read_input(path, reader=library.read_records).

:param path: Existing input file in the format documented under Inputs and outputs. :returns: Parsed records as a DataFrame. :raises ValueError: Input values or file structure cannot be parsed.

analyze(data: pd.DataFrame, *, assay: str='chip-seq') -> pd.DataFrame

Compute epigenomics summaries and return a new table, leaving data unchanged.

:param data: Records containing chrom, start, end. :param assay: CLI default chip-seq; atac-seq and cut-tag change descriptive expectations. :returns: Result table with diagnostics and summary in attrs['run_info']. :raises ValueError: Required columns are absent or records are empty or invalid.

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

Return the analysis diagnostics and summary.

:param data: Result returned by analyze. :param keep: Keep diagnostics by default; the CLI passes False. :returns: Independent diagnostics dictionary. :raises ValueError: analyze has not populated diagnostics.

distribution_figure(data: pd.DataFrame)

Plot width values without writing files.

:param data: Result table containing width. :returns: Matplotlib Figure. :raises ValueError: The value column is absent or the table is empty.

<!-- api:end -->
Show full SKILL.md (155 more words)Show less

Methods and parameters

analyze returns a new DataFrame and leaves the input unchanged. run_info(result) returns the summary and method diagnostics. The CLI passes keep=False so diagnostics do not enter output tables. All calculations are deterministic; synthetic CLI demos retain seed 42.

Gotchas

  • analyze uses BED zero-based, half-open coordinates and recomputes width as end minus start. It does not call peaks.
  • run_info()["summary"] retains the legacy p/q-value heuristic: medians above 1 are treated as negative-log10 values. Convert or inspect inputs before interpreting significance.
  • read_records treats a .csv suffix as CSV and other suffixes as BED/narrowPeak. assay only changes descriptive expectations.

Inputs and outputs

Input files:

  • Modalities: atac-seq, chip-seq
  • File types: .bed, .narrowpeak, .csv

CLI output files:

  • tables/peaks_per_chromosome.csv
  • tables/peaks_summary.csv
  • report.md
  • result.json

The library writes no files. Steps use write_output; the CLI owns the listed artifacts. Public figure functions return matplotlib Figures and do not add new CLI outputs.

CLI

bash
python skills/genomics/genomics-epigenomics/genomics_epigenomics.py --input input_file --output results/
python skills/genomics/genomics-epigenomics/genomics_epigenomics.py --demo --output /tmp/genomics_epigenomics_demo

See also

  • references/parameters.md
  • references/methodology.md
  • references/output_contract.md

Dependencies

numpy, pandas, matplotlib

© 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/genomics/genomics-epigenomics of TianGzlab/OmicsClaw.

  • SKILL.md
  • _api.py
  • data/example.bed
  • examples/example_step.py
  • genomics_epigenomics.py
  • references/methodology.md
  • references/output_contract.md
  • references/parameters.md
  • tests/test_api.py

Open the folder on GitHubat commit 90a3bec

Compare with similar skills

Genomics Epigenomics 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.

Genomics Epigenomics compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Genomics Epigenomics this skillTianGzlab/OmicsClaw161—~1.1kAutomated safety check: PassApache-2.0
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

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Categories

Questions about Genomics Epigenomics

What does Genomics Epigenomics do?

Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Genomics Epigenomics is an agent skill from TianGzlab/OmicsClaw. Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics.

When should I use Genomics Epigenomics?

Genomics Epigenomics fits situations like: tasks that involve Bioinformatics.

How do I install Genomics Epigenomics in Claude Code?

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

How do I install Genomics Epigenomics in Codex?

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

Can I use Genomics Epigenomics 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 genomics-epigenomics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/genomics-epigenomics, .gemini/skills/genomics-epigenomics, .github/skills/genomics-epigenomics and .opencode/skills/genomics-epigenomics in your project.

What does Genomics Epigenomics need to run?

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

Does Genomics Epigenomics 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 Genomics Epigenomics 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 Genomics Epigenomics use?

Genomics Epigenomics 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 Genomics Epigenomics use?

About 1.1k tokens (SKILL.md is roughly 4.4k 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 1.3k tokens, read only when the agent opens those files.

What are the alternatives to Genomics Epigenomics?

Skills that share tags, products or a category with Genomics Epigenomics: 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 Genomics Epigenomics?

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.