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 genomic-intelligencegit 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/genomic-intelligence)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/genomic-intelligence"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/genomic-intelligence/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/genomic-intelligence"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/genomic-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
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 →
- medium Data Exfiltration · line 29 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 63 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 148 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 170 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00151 | $0.06237 |
| Opus 5.5 | $0.00060 | $0.02495 |
| Sonnet 5.5 | $0.00030 | $0.01247 |
| Haiku 4.5 | $0.00015 | $0.00624 |
Grade A, and why
genomic-intelligence scanned grade A with 1 finding 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 24d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
r = requests.post(f"{BASE}/v1/tasks/{task}/predict", headers=HEADERS, Copies of this mod
1 near-identical copy found in the catalogue:
- genomic-intelligence — 100% identical, 335 lines differ
How it starts
The opening of the file, as written. The whole thing — 417 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Genomic Intelligence — DNA Sequence Models
Genomic Intelligence (GI) serves transformer DNA language models over six sequence-analysis tasks on managed GPUs. Give it a gene symbol, a genomic region, or a DNA/FASTA sequence; it returns structured predictions — promoter regions, splice sites, enhancer activity, chromatin state, expression (log TPM), and de-novo gene annotation. Nothing runs locally: no model weights, no GPU, no heavy Python stack. It is a thin client over a hosted, versioned inference API.
Official docs: docs.genomicintelligence.ai ·
REST contract at api.genomicintelligence.ai/v1/openapi.json ·
hosted MCP server at https://mcp.genomicintelligence.ai/mcp
When to use this skill
Use GI when the user has DNA and wants a model prediction:
- Find promoters in a genomic region (
promoter) - Predict splice donor/acceptor sites (
splice) - Score enhancer activity — developmental & housekeeping (
enhancer) - Annotate chromatin state across hundreds of tracks (
chromatin) - Predict expression as log(TPM+1) from a sequence + cell-type context (
expression) - Annotate genes/transcripts de novo, no reference needed (
annotation) - Find the genes in a region and predict each one's expression (composite)
Not for local alignment, variant calling, or file I/O — use a local tool (BioPython, bcftools) for those. GI is for model inference from sequence.
Research and development use. Not for clinical or diagnostic decisions.
Two ways to call GI
Hosted MCP server (keyless; preferred on MCP hosts)
GI hosts an MCP server at https://mcp.genomicintelligence.ai/mcp (Streamable
HTTP). When your agent host supports MCP, prefer it: it works keyless against
a rate- and concurrency-limited public demo tier, and an optional gi_ bearer
key raises those limits. It exposes acquisition tools that return a sequence handle
(sequence_ref) and predict_* tools that take that handle, so large sequences
stay out of the context. See MCP workflow below and
references/mcp.md.
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.
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.
- 24d ago Changed · +50 lines · +1 tokens per session cdb027004996
- 25d ago Changed · +3 lines a65647d4a0d7
- 1mo ago Changed · +120 lines bc3ec5c3fd64
- 1mo ago First seen · 244 lines · 150 tokens per session scan A a76747245399
genomic-intelligence is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,587 stars, last pushed 7d ago), licensed MIT. It adds 151 tokens to every session and 6,237 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
bio-prefect-dask-nextflow
Choose and scaffold Prefect+Dask or Nextflow pipelines. Use when designing a local, distributed, or Slurm-backed bioinformatics workflow.
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…
rdkit-qsar-pharmacophore
Computes 2048-bit ECFP4 Morgan fingerprints from SMILES, trains LightGBM regressors for pIC50 prediction, and extracts SHAP feature attributions.
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…
bulk-rnaseq-counts-to-de-deseq2
Run differential expression analysis on bulk RNA-seq count data with DESeq2 (R). Covers DESeqDataSet construction from a count matrix, tximport (Salmon/Kallisto), featureCounts, or SummarizedExperiment; pre-filtering; design formulas (simple, batch, paired, interaction, multi-factor, LRT); result extraction by…
seurat-skill
Comprehensive Seurat v5 (R) guide for single-cell RNA-seq and multimodal analysis. Covers installation, standard workflows (Normalize/SCTransform), clustering, integration (CCA/RPCA/Harmony), differential expression (FindMarkers/FindAllMarkers), visualization (DimPlot/FeaturePlot/VlnPlot/DoHeatmap), spatial…