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Agent skill

Genomics Cnv Calling

by TianGzlab in TianGzlab/OmicsClaw

Load when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (amplification / gain / neutral / loss /…

Apache-2.0Auto-check passedResearch & Science

Install Genomics Cnv Calling

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

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

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

At a glance

Load when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (amplification / gain / neutral / loss /…

  • 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 Cnv Calling is an agent skill from TianGzlab/OmicsClaw. Load when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (amplification / gain / neutral / loss / deepdeletion), per-chromosome summary, genome-fraction-altered. Skip when working with single-cell / spatial CNV (use spatial-cnv).

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_cnv_calling.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-cnv-calling”

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 Cnv Calling loads about 1.1k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 379 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
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
~2.7k

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). 379 words, ~1,079 tokens.

Download SKILL.mdSave it as .claude/skills/genomics-cnv-calling/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
genomics-cnv-calling
description
Load when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (`amplification` / `gain` / `neutral` / `loss` / `deep_deletion`), per-chromosome summary, genome-fraction-altered. Skip when working with single-cell / spatial CNV (use spatial-cnv).
trigger
CNV, copy number, amplification, deletion, CNVkit
tags
genomics, cnv, copy-number, cbs, segmentation, cnvkit, gatk-gcnv

genomics-cnv-calling

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-cnv-calling")
data = read_input("input.csv", reader=library.read_bins)
result = library.analyze(data)
write_output(result, "tables/result.csv")
write_output(library.copy_ratio_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_bins(path: str | Path) -> pd.DataFrame

Read a bin CSV through read_input(path, reader=library.read_bins).

:param path: CSV containing chrom, start, end and log2_ratio. :returns: Bin table. :raises ValueError: The CSV cannot be parsed.

analyze(data: pd.DataFrame, *, method: str='cbs', alpha: float=0.01) -> pd.DataFrame

Return new CNV segments from bin-level log2 ratios; leave data unchanged.

:param data: Bins with chrom, start, end and finite log2_ratio. :param method: CLI default cbs, or none to classify individual bins. :param alpha: CLI default 0.01; smaller values require stronger splits. :returns: Segments with cn_state, estimated_cn and attrs['run_info']. :raises ValueError: Columns, coordinates, method or alpha are invalid.

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

Return analysis diagnostics and numeric summary.

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

Show full SKILL.md (170 more words)Show less
copy_ratio_figure(data: pd.DataFrame)

Plot segment log2 ratios in table order without writing a file.

:param data: Segment table returned by analyze. :returns: Matplotlib Figure. :raises ValueError: log2_ratio is absent or the table is empty.

<!-- api:end -->

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(method="cbs") uses the existing simplified deterministic split heuristic, not CNVkit or a calibrated CBS permutation test.
  • run_info()["summary"]["genome_fraction_altered"] retains the legacy fraction of altered segments, not a base-pair-weighted genome fraction.
  • analyze(method="none") classifies individual bins; gain/loss thresholds are 0.3/-0.3 and amplification/deep-deletion thresholds are 1/-1.

Inputs and outputs

Input files:

  • File types: .csv

CLI output files:

  • tables/cnv_per_chromosome.csv
  • tables/cnv_segments.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-cnv-calling/genomics_cnv_calling.py --input input_file --output results/
python skills/genomics/genomics-cnv-calling/genomics_cnv_calling.py --demo --output /tmp/genomics_cnv_calling_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-cnv-calling of TianGzlab/OmicsClaw.

  • SKILL.md
  • _api.py
  • data/example.csv
  • examples/example_step.py
  • genomics_cnv_calling.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 Cnv Calling 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 Cnv Calling compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Genomics Cnv Calling 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 Cnv Calling

What does Genomics Cnv Calling do?

Load when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (amplification / gain / neutral / loss /…. Genomics Cnv Calling is an agent skill from TianGzlab/OmicsClaw. Load when calling CNV segments via CBS-style segmentation on a bin-level log2-ratio CSV from exome / WGS coverage — emits per-segment 5-class CN state (amplification / gain / neutral / loss / deepdeletion), per-chromosome summary, genome-fraction-altered.

When should I use Genomics Cnv Calling?

Genomics Cnv Calling fits situations like: tasks that involve Bioinformatics.

How do I install Genomics Cnv Calling in Claude Code?

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

How do I install Genomics Cnv Calling in Codex?

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

Can I use Genomics Cnv Calling 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-cnv-calling -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-cnv-calling, .gemini/skills/genomics-cnv-calling, .github/skills/genomics-cnv-calling and .opencode/skills/genomics-cnv-calling in your project.

What does Genomics Cnv Calling need to run?

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

Does Genomics Cnv Calling 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 Cnv Calling 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 Cnv Calling use?

Genomics Cnv Calling 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 Cnv Calling use?

About 1.1k tokens (SKILL.md is roughly 4.3k 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 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Genomics Cnv Calling?

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

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.