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Query Uniprot

Community
BioTender-max
query-uniprot

Query UniProt protein database. Use when user asks about protein sequences, functions, annotations, domains, or protein identifiers. Triggers on "uniprot", "protein function", "protein sequence", "gene product", "protein info".

Overview

PublisherBioTender-max
Repositoryawesome-bio-agent-skills
Skill namequery-uniprot
Stars
204
Forks
36
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BioTender-max on GitHub. Read the source before you install it.

Installation

Install the Query Uniprot AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/query-uniprot
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BioTender-max/awesome-bio-agent-skills.git /tmp/awesome-bio-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-bio-agent-skills/skills/bioclaw/query-uniprot .claude/skills/query-uniprot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Query Uniprot in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Query Uniprot on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Query Uniprot is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

UniProt Protein Database Query

Query the UniProt REST API for protein information.

When to Use

  • User asks about a protein's function, sequence, or annotation
  • User provides a gene name and wants protein info
  • User needs protein accession IDs
  • User asks "what does gene X do" (protein level)

How to Execute

python
import requests
import json

BASE_URL = "https://rest.uniprot.org"

# 1. Search by gene name (default: human, reviewed/Swiss-Prot)
def search_uniprot(gene_name, organism_id=9606, max_results=5):
    url = f"{BASE_URL}/uniprotkb/search"
    params = {
        "query": f"gene_exact:{gene_name} AND organism_id:{organism_id} AND reviewed:true",
        "format": "json",
        "size": max_results,
        "fields": "accession,id,gene_names,protein_name,organism_name,length,cc_function,ft_domain,sequence"
    }
    r = requests.get(url, params=params)
    r.raise_for_status()
    return r.json()

# 2. Get by accession ID
def get_uniprot_entry(accession):
    url = f"{BASE_URL}/uniprotkb/{accession}.json"
    r = requests.get(url)
    r.raise_for_status()
    return r.json()

# 3. Get FASTA sequence
def get_fasta(accession):
    url = f"{BASE_URL}/uniprotkb/{accession}.fasta"
    r = requests.get(url)
    r.raise_for_status()
    return r.text

# Example usage
data = search_uniprot("TP53")
for entry in data.get("results", []):
    acc = entry["primaryAccession"]
    name = entry.get("proteinDescription", {}).get("recommendedName", {}).get("fullName", {}).get("value", "N/A")
    gene = entry.get("genes", [{}])[0].get("geneName", {}).get("value", "N/A")
    length = entry.get("sequence", {}).get("length", "N/A")
    
    # Extract function
    functions = [c["texts"][0]["value"] for c in entry.get("comments", []) if c["commentType"] == "FUNCTION"]
    func_text = functions[0][:200] if functions else "N/A"
    
    print(f"Accession: {acc}")
    print(f"Protein: {name}")
    print(f"Gene: {gene}")
    print(f"Length: {length} aa")
    print(f"Function: {func_text}")

Common Search Patterns

  • By gene: gene_exact:BRCA1 AND organism_id:9606
  • By keyword: keyword:kinase AND organism_id:9606
  • By disease: cc_disease:cancer AND organism_id:9606
  • By GO term: go:apoptosis AND organism_id:9606
  • Species IDs: Human=9606, Mouse=10090, Rat=10116, Zebrafish=7955, Fly=7227, Yeast=559292

Output Format

Present: Accession, protein name, gene, organism, length, function summary, and UniProt link.

Follow-up Suggestions

  • "Want me to get the AlphaFold structure for this protein?"
  • "Should I check protein-protein interactions on STRING?"
  • "Want me to BLAST this protein sequence?"

Frequently asked questions

What does the Query Uniprot AI skill do?

Query UniProt protein database. Use when user asks about protein sequences, functions, annotations, domains, or protein identifiers. Triggers on "uniprot", "protein function", "protein sequence", "gene product", "protein info".

Why use Query Uniprot on TypingMind?

Because you install it once and use it with any model. Query Uniprot is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Query Uniprot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/query-uniprot. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Query Uniprot?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Query Uniprot?

As many as you like. As long as a model supports skills, you can use Query Uniprot with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Query Uniprot AI skill free?

It is published on GitHub by BioTender-max. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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