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.
npx agentmods add skills/synthetic-sciences/openscience/deeptoolsnpx skills add synthetic-sciences/openscience --skill deeptoolsgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00051 | $0.04233 |
| Opus 5 | $0.00026 | $0.02116 |
| Sonnet 5 | $0.00010 | $0.00847 |
| Haiku 4.5 | $0.00005 | $0.00423 |
Grade A, and why
deeptools 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 2d ago.
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.
Copies of this mod
8 near-identical copies found in the catalogue:
- deeptools — 97% identical, 7 lines differ
- deeptools — 97% identical, 4 lines differ
- deeptools — 97% identical, 3 lines differ
- deeptools — 97% identical, 3 lines differ
- deeptools — 97% identical, 3 lines differ
- deeptools — 92% identical, 6 lines differ
- deeptools — 92% identical, 6 lines differ
- deeptools — 88% identical, 46 lines differ
How it starts
The opening of the file, as written. The whole thing — 531 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 workflows: "analyze ChIP-seq data", "RNA-seq coverage", "ATAC-seq analysis", "complete workflow"
- Working with specific file types: BAM files, bigWig files, BED region files in genomics context
Quick Start
For users new to deepTools, start with file validation and common workflows:
1. Validate Input Files
Before running any analysis, validate BAM, bigWig, and BED files using the validation script:
python scripts/validate_files.py --bam sample1.bam sample2.bam --bed regions.bed
This checks file existence, BAM indices, and format correctness.
2. Generate Workflow Template
For standard analyses, use the workflow generator to create customized scripts:
# 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 \
--input-bam Input.bam --chip-bams "ChIP1.bam ChIP2.bam" \
--genome-size 2913022398
# Make executable and run
chmod +x qc_workflow.sh
./qc_workflow.sh
What ships with it
7 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.
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.
- 2d ago First seen · 531 lines · 51 tokens per session scan A 153da92fdc43
deeptools is a skill published in the GitHub repository synthetic-sciences/openscience (3,385 stars, last pushed today), licensed Apache-2.0. It adds 51 tokens to every session and 4,233 once invoked, about $0.0003 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.
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