Agent skill

deeptools

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

K-Dense-AIgithub.com/K-Dense-AIGitHub ↗
claude-codecan modify filesships scriptsMIT
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill deeptools --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 9
SKILL.md size: 15 KB
Bundled scripts: yes
Version: 1.2
Allowed tools: ReadWriteEditBash
Requires: Requires Python >3.8 and deepTools 3.5.6-compatible dependencies. The upstream project recommends conda/bioconda for…
Path: skills/deeptools/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 32,619
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# deepTools: NGS Data Analysis Toolkit ## Overview deepTools is a comprehensive suite of Python command-line tools designed for processing and analyzing high-throughput sequencing data. Use deepTools to perform quality control, normalize data, compare samples, and generate publication-quality visualizations for ChIP-seq, RNA-seq, ATAC-seq, MNase-seq, and other NGS experiments. **Core capabilities:** - Convert BAM alignments to normalized coverage tracks (bigWig/bedGraph) - Quality control assessment (fingerprint, correlation, coverage) - Sample comparison and correlation analysis - Heatmap and profile plot generation around genomic features - Enrichment analysis and peak region visualization ## When to Use This Skill This skill should be used when: - **File conversion**: "Convert BAM to bigWig", "generate coverage tracks", "normalize ChIP-seq data" - **Quality control**: "check ChIP quality", "compare replicates", "assess sequencing depth", "QC analysis" - **Visualization**: "create heatmap around TSS", "plot ChIP signal", "visualize enrichment", "generate profile plot" - **Sample comparison**: "compare treatment vs control", "correlate samples", "PCA analysis" - **Analysis workflo

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Quick Start
  4. 1. Validate Input Files
  5. 2. Generate Workflow Template
  6. 3. Most Common Operations
  7. Installation
  8. Core Workflows and Tool Categories
  9. Normalization Methods
  10. Effective Genome Sizes
  11. Common Parameters Across Tools
  12. Best Practices
  13. File Validation
  14. Analysis Strategy
Ships with 8 files
  • assets/quick_reference.md
  • references/core_workflows.md
  • references/effective_genome_sizes.md
  • references/normalization_methods.md
  • references/tools_reference.md
  • references/workflows.md
  • scripts/validate_files.py
  • scripts/workflow_generator.py
Commands it runs
python scripts/validate_files.py --bam sample1.bam sample2.bam --bed regions.bed
List available workflows
python scripts/workflow_generator.py --list
Generate ChIP-seq QC workflow
python scripts/workflow_generator.py chipseq_qc -o qc_workflow.sh \
Make executable and run
chmod +x qc_workflow.sh
uv pip install deepTools==3.5.6
conda install -c conda-forge -c bioconda deeptools
samtools index input.bam
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About this skill
What does the deeptools skill do?

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

How do I install it?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill deeptools --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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