Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 skills add K-Dense-AI/scientific-agent-skills --skill deeptoolsgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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.
[](https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/deeptools)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/deeptools"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/deeptools/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.
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/deeptools"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/deeptools.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.03861 |
| Opus 5 | $0.00026 | $0.01930 |
| Sonnet 5 | $0.00010 | $0.00772 |
| Haiku 4.5 | $0.00005 | $0.00386 |
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 7d 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
1 near-identical copy found in the catalogue:
- deeptools — 100% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 430 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
8 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.
- assets/quick_reference.md 1.7 KB
- references/core_workflows.md 4.4 KB
- references/effective_genome_sizes.md 3.8 KB
- references/normalization_methods.md 12 KB
- references/tools_reference.md 20 KB
- references/workflows.md 11 KB
- scripts/validate_files.py 5.9 KB runs code
- scripts/workflow_generator.py 16 KB runs code
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.
- 7d ago Changed · +17 lines b8f6a7f39c5e
- 11d ago First seen · 413 lines · 51 tokens per session scan A 2724906bdf23
deeptools is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 3d ago), licensed MIT. It adds 51 tokens to every session and 3,861 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.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
notebooks
Author, execute, validate, and convert reproducible marimo or Jupyter notebooks. Use when delivering an analysis notebook with all cells run and figures embedded.