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 genome-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/genome-analysis)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/genome-analysis"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/genome-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/genome-analysis"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/genome-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 | $0.00052 | $0.00712 |
| Opus 5 | $0.00026 | $0.00356 |
| Sonnet 5 | $0.00010 | $0.00142 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
genome-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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Trigger
Activate this skill when the user mentions any of the following:
- BLAST, sequence alignment, homology search
- Gene expression, RNA-seq, differential expression, DESeq2, edgeR
- GWAS, SNP, variant calling, VCF files
- Genome assembly, annotation, scaffolding
- Phylogenomics, comparative genomics, synteny
- Genotyping, haplotype analysis, linkage disequilibrium
Step-by-Step Methodology
- Clarify the organism and genome build - Confirm species, reference genome version (e.g., GRCh38 for human, GRCm39 for mouse), and data type (WGS, WES, RNA-seq, microarray).
- Data ingestion and QC - Check raw data quality (FastQC metrics, read depth, coverage). Flag low-quality samples before proceeding.
- Alignment / Assembly - For alignment tasks, specify the aligner (BWA-MEM2, STAR for RNA-seq, minimap2 for long reads). For de novo assembly, recommend assemblers (SPAdes, Flye, hifiasm).
- Variant calling / Expression quantification - Use GATK HaplotypeCaller or DeepVariant for variants; featureCounts or Salmon for transcript quantification.
- Statistical analysis - Apply appropriate multiple-testing correction (Bonferroni, BH-FDR). For GWAS, use mixed models (BOLT-LMM, SAIGE) to handle population structure.
- Annotation and interpretation - Annotate variants with VEP/ANNOVAR; enrich gene lists with GO, KEGG, Reactome pathways.
- Visualization - Generate Manhattan plots (GWAS), volcano plots (DE), circos plots (structural variants), or heatmaps (expression clusters).
Key Databases and Tools
- NCBI GenBank / RefSeq - Reference sequences and annotations
- Ensembl / UCSC Genome Browser - Genome browsing and tracks
- BLAST (NCBI) - Sequence similarity search
- UniProt - Protein function annotation
- ClinVar / gnomAD - Clinical variant interpretation
- KEGG / Reactome / Gene Ontology - Pathway and functional enrichment
- GEO / ArrayExpress - Public expression datasets
Output Format
- Provide results in structured tables (gene, log2FC, p-value, adjusted p-value).
- Include publication-quality figure descriptions with axis labels and legends.
- Report genome coordinates in standard notation (chr:start-end, 1-based).
- Always state the reference genome build used.
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 · 53 lines · 52 tokens per session scan A 6331c5a4ed4f
genome-analysis is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 712 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.