skills/uniprot/SKILL.md
Protein sequence, function, and annotation lookup. Query MUST be a bare gene symbol or protein name — 1 to 3 words maximum. Valid examples: 'KRAS', 'EGFR', 'BTK', 'TP53', 'Bruton tyrosine kinase', 'P01116'. If the topic is 'sotorasib KRAS G12C', the correct query is 'KRAS'. If the topic is 'imatinib BCR-ABL resistance', the correct query is 'BCR-ABL'. Strip the drug name, mutation label, and all mechanism words — pass only the protein or gene name.
npx skillsauth add lamm-mit/scienceclaw uniprotInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Query the UniProt protein database to retrieve protein sequences, annotations, functional information, and cross-references.
UniProt is the world's most comprehensive protein sequence and functional annotation database. This skill provides access to:
python3 {baseDir}/scripts/uniprot_fetch.py --accession P53_HUMAN
python3 {baseDir}/scripts/uniprot_fetch.py --accession P04637
python3 {baseDir}/scripts/uniprot_fetch.py --search "insulin human"
python3 {baseDir}/scripts/uniprot_fetch.py --accession P53_HUMAN --format fasta
python3 {baseDir}/scripts/uniprot_fetch.py --accession P53_HUMAN --format detailed
| Parameter | Description | Default |
|-----------|-------------|---------|
| --accession | UniProt accession or entry name | - |
| --search | Search query | - |
| --organism | Filter by organism (e.g., "human", "9606") | - |
| --reviewed | Only Swiss-Prot (reviewed) entries | False |
| --max-results | Maximum results for search | 10 |
| --format | Output format: summary, detailed, fasta, json | summary |
| --include-features | Include sequence features | False |
| --include-xrefs | Include cross-references | False |
python3 {baseDir}/scripts/uniprot_fetch.py --accession P53_HUMAN --format detailed
python3 {baseDir}/scripts/uniprot_fetch.py --search "kinase" --organism human --reviewed --max-results 20
python3 {baseDir}/scripts/uniprot_fetch.py --accession "P53_HUMAN,BRCA1_HUMAN,EGFR_HUMAN" --format fasta
python3 {baseDir}/scripts/uniprot_fetch.py --search "gene:TP53 AND organism_id:9606"
python3 {baseDir}/scripts/uniprot_fetch.py --accession P53_HUMAN --include-xrefs
Standard FASTA format sequence output.
Full UniProt entry in JSON format.
UniProt entries contain cross-references to:
UniProt is a protein database, not a drug/chemistry database. Queries must target proteins by name, gene, or accession. Drug or chemistry concepts will return zero results.
| ❌ Fails (not a protein query) | ✅ Works | |---|---| | "KRAS covalent inhibitors" | "KRAS_HUMAN" or "P01116" | | "BTK warhead optimization" | "BTK" or "BTK_HUMAN" or "Q06187" | | "covalent inhibitor design" | "Bruton tyrosine kinase" | | "BBB penetration ADMET" | "ABCB1 human" or "MDR1" | | "kinase inhibitor selectivity" | "EGFR kinase" or "EGFR_HUMAN" |
Rule: If your query describes a drug, chemical process, mechanism, or assay — use PubChem or TDC instead. UniProt answers: "What is this protein and what does it do?"
For KRAS covalent inhibitor research, the correct two-step workflow is:
--search "KRAS_HUMAN" or --accession P01116 → get KRAS protein structure, active site residues (Cys12, Gly12), domainstools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.