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genomics-cnv-calling

genomics-cnv-calling is a skill for Claude Code, Codex from TianGzlab/OmicsClaw. It costs 95 tokens per session (1,427 once invoked), scanned A, original, Apache-2.0.

A tool for turning bin-level log2-ratio data from exome or whole-genome sequencing into copy-number segments. Copy-number variation means that parts of DNA occur in extra or missing copies.

In plain words
What is it for?
Use it with CSV output from CNVkit, GATK gCNV, or Control-FREEC to produce segment tables, chromosome summaries, and the fraction of the genome affected. It is not for single-cell or spatial data.
Why use it?
It removes the manual work of grouping nearby coverage measurements and labeling them as amplification, gain, neutral, loss, or deep deletion.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 160repo +1 1mo ago A scan SkillSpector: warn 95 tokens original Apache-2.0

Good fit Use it with CSV output from CNVkit, GATK gCNV, or Control-FREEC to produce segment tables, chromosome summaries, and the fraction of the genome affected. It is not for single-cell or spatial data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tiangzlab/omicsclaw/genomics-cnv-calling
View source ↗ TianGzlab/OmicsClaw tiangzlab.github.io
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add TianGzlab/OmicsClaw --skill genomics-cnv-calling
Clone the repo
git clone --depth 1 https://github.com/TianGzlab/OmicsClaw

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for genomics-cnv-calling

README.md
[![agentmods](https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-cnv-calling/github.svg)](https://agentmods.dev/skills/tiangzlab/omicsclaw/genomics-cnv-calling)
Your own site
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/genomics-cnv-calling"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-cnv-calling/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for genomics-cnv-calling

Your own site · 80×15
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/genomics-cnv-calling"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-cnv-calling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,427 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Rogue Agent · line 3
    Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.
    Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00095 $0.01427
Opus 5 $0.00048 $0.00714
Sonnet 5 $0.00019 $0.00285
Haiku 4.5 $0.00010 $0.00143

Measured 9d ago against content hash 6809295b1398, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

genomics-cnv-calling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (genomics_cnv_calling.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/genomics/genomics-cnv-calling/SKILL.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

genomics-cnv-calling

When to use

The user has a bin-level log2-ratio CSV (typically from CNVkit cnr files, GATK gCNV denoised copy ratios, or Control-FREEC ratio output) and wants to segment into discrete CNV calls. Each segment is classified into one of five copy-number states based on mean log2 ratio: amplification (> +1.0), gain (> +0.3), neutral, loss (< -0.3), deep_deletion (< -1.0). --alpha controls segmentation significance (default 0.01).

This skill does NOT generate the bin-level log2-ratio CSV — it consumes the output of CNVkit / GATK gCNV / Control-FREEC. For spatial / single-cell CNV use spatial-cnv.

Inputs & Outputs

Inputs

  • File types: .csv

Outputs

  • tables/cnv_per_chromosome.csv
  • tables/cnv_segments.csv
  • report.md
  • result.json

Flow

  1. Load bin CSV (--input <bins.csv>) or generate a demo bin file at output_dir/demo_cnv_bins.csv (genomics_cnv_calling.py:229).
  2. Read columns via pd.read_csv (genomics_cnv_calling.py:250); group by df["chrom"] (:254) and segment per chromosome.
  3. Classify each segment via np.select (genomics_cnv_calling.py:161-162) into one of amplification / gain / neutral / loss / deep_deletion based on mean log2.
  4. Aggregate per-chromosome counts + genome-fraction-altered (:281-291).
  5. Write tables/cnv_segments.csv (genomics_cnv_calling.py:373) + tables/cnv_per_chromosome.csv (:382) + report.md + result.json (:385).

Gotchas

  • Required CSV column is chrom, NOT chromosome. Code reads df["chrom"] at genomics_cnv_calling.py:254. CNVkit cnr files have a chromosome column — rename to chrom first (pd.read_csv(...).rename(columns={"chromosome": "chrom"})). Other required columns are start, end, log2_ratio.
  • cn_state has 5 classes, NOT 3. genomics_cnv_calling.py:161-162 produces amplification (log2 > 1.0), gain (> 0.3), neutral, loss (< -0.3), deep_deletion (< -1.0). The summary reports n_gains and n_losses as inclusive of amplification / deep_deletion (:281-282); inspect n_amplifications / n_deep_deletions for the high-magnitude subset.
  • No bin generator is invoked. This skill consumes a bin-level log2-ratio CSV — it does NOT run CNVkit / GATK gCNV / Control-FREEC. Run them upstream and feed the bin file here.
  • --input REQUIRED unless --demo. genomics_cnv_calling.py:363 raises ValueError("--input required when not using --demo"); non-existent paths raise FileNotFoundError at :366.
  • --alpha controls segmentation aggressiveness. Lower values (e.g. 0.001) yield fewer / larger segments; higher values (0.1) yield more / smaller. Default 0.01 is suitable for clean exome / WGS data; for noisy panels consider --alpha 0.001.
  • Classification thresholds are hard-coded. ±0.3 (gain/loss) and ±1.0 (amplification/deep_deletion) at genomics_cnv_calling.py:50-52 — no CLI flag to tune. For tumour-purity-corrected calling, scale the input log2 ratios upstream.

Read the full file on GitHub · 95 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 95 lines · 95 tokens per session scan A 6809295b1398

Subscribe to this mod's changes

genomics-cnv-calling is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,427 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Scientificbioinformaticscbscnvcnvkitcopy-numbergatk-gcnvgenomicsknowledge-graphllm-agentmulti-agentsmulti-omicssegmentation
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