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 beita6969/ScienceClaw --skill genomics-analysisgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/genomics-analysis)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/genomics-analysis"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/genomics-analysis/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/beita6969/scienceclaw/genomics-analysis"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/genomics-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00050 | $0.00850 |
| Opus 5 | $0.00025 | $0.00425 |
| Sonnet 5 | $0.00010 | $0.00170 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
genomics-analysis 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Genomics Analysis (Meta Skill)
This meta-skill coordinates a complete genomics analysis pipeline by integrating gene database queries, sequence analysis, expression profiling, and pathway enrichment into a unified workflow. It combines three specialized skills to deliver comprehensive gene-level and systems-level biological insights.
Workflow
Step 1: Gene Information Retrieval
Query NCBI Entrez for comprehensive gene details including official nomenclature, genomic coordinates, transcript variants, and functional annotations. Retrieve orthologs across model organisms for evolutionary context. Pull known variants from ClinVar and dbSNP, noting pathogenic or pharmacogenomic associations. Collect linked references from PubMed for recent literature context.
Step 2: Sequence Analysis
Use BioPython to perform sequence-level analyses on retrieved gene and protein sequences:
- Multiple sequence alignment of orthologs to identify conserved regions
- Motif discovery in promoter regions or protein domains
- Domain architecture mapping against Pfam/InterPro signatures
- Codon usage analysis for expression optimization studies
- Variant impact prediction based on conservation scores
Step 3: Expression Analysis
Apply scanpy for expression data analysis, supporting both single-cell and bulk RNA-seq workflows:
- For single-cell: quality control, normalization, clustering, marker gene identification, cell type annotation
- For bulk: differential expression analysis, volcano plots, heatmaps
- Cross-dataset comparison when multiple conditions are available
- Identification of co-expressed gene modules
Step 4: Pathway Enrichment and Functional Annotation
Map differentially expressed or co-expressed genes to biological pathways:
- KEGG pathway mapping for metabolic and signaling context
- Gene Ontology enrichment (biological process, molecular function, cellular component)
- Reactome pathway analysis for detailed mechanistic understanding
- Network-based enrichment to identify hub genes and regulatory modules
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.
- 8d ago First seen · 88 lines · 50 tokens per session scan A aebaa151649d
genomics-analysis is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 850 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 2026-09-03.
Other skills, from other repositories
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
structure-prediction
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
biomcp
Search and retrieve biomedical data - genes, variants, clinical trials, diagnostic tests, articles, drugs, diseases, pathways, proteins, adverse events, pharmacogenomics, and phenotype-disease matching. Use for gene function, variant pathogenicity, trials, diagnostics, drug safety, pathway context, disease workups…
biomcp-research
Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.
biological-expert
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.