Uniprot Database

Organization

ai4protein

uniprot_database

UniProt — protein sequence, function, taxonomy, cross-references. Search proteins by query, retrieve a UniProt entry, map IDs between databases (PDB↔UniProt etc.), pull FASTA sequence, fetch metadata, run SPARQL against sparql.uniprot.org. Use whenever the user mentions a UniProt accession (e.g. P04637), asks for protein function/sequence/family info, or needs cross-DB ID mapping. Don't use for AlphaFold structures (use alphafold_database) or PDB structures (use rcsb_database).

Overview

Publisher	ai4protein
Repository	`VenusFactory2`
Skill name	`uniprot_database`
Stars	255
Forks	37
Bundled files	Instructions only
License	Unknown
Links	Skill source Repository Homepage

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 ai4protein on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/ai4protein/VenusFactory2/tree/main/src/agent/skills/uniprot_database
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.

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/ai4protein/VenusFactory2.git /tmp/VenusFactory2
mkdir -p .claude/skills
cp -r /tmp/VenusFactory2/src/agent/skills/uniprot_database .claude/skills/uniprot_database

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Uniprot Database 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 Uniprot Database 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 Uniprot Database 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 Database

Overview

UniProt KnowledgeBase (UniProtKB) is the central protein-knowledge resource. This skill exposes 6 tools spanning text search, single-entry retrieval, ID mapping, sequence/metadata download, and SPARQL queries.

Project Tools (VenusFactory2)

Tool	Args	Returns	Description
download_uniprot_search_by_query	`query` (UniProt query syntax, e.g. `organism_id:9606 AND reviewed:true`), `out_path`, optional `format`	rich JSON envelope	Search UniProtKB; results saved to file.
download_uniprot_retrieve_by_id	`uniprot_id` (e.g. `P04637`), `out_path`, optional `frmt` (`fasta` default)	rich JSON envelope	Retrieve one entry in the chosen format.
download_uniprot_mapping	`fr` (from db, e.g. `PDB`), `to` (to db, e.g. `UniProtKB_AC-ID`), `query` (comma-separated IDs), `out_path`	rich JSON envelope	Cross-database ID mapping (PDB↔UniProt, gene name↔accession, etc.).
download_uniprot_seq_by_id	`uniprot_id`, `out_path`	rich JSON envelope; FASTA file	Sequence FASTA only.
download_uniprot_meta_by_id	`uniprot_id`, `out_path`	rich JSON envelope; JSON file	Full metadata (function, taxonomy, GO, xrefs, etc.).
download_uniprot_sparql_by_query	`query` (SPARQL string), `out_dir` (required), `timeout` (default `120`s)	rich JSON envelope; SPARQL JSON file at `file_info.file_path`; `biological_metadata.row_count` + `head_vars`	Run an arbitrary SPARQL query against https://sparql.uniprot.org/sparql.

When to Use Which Tool

Goal	Tool
You have a UniProt accession, want sequence	`download_uniprot_seq_by_id`
You have a UniProt accession, want everything else	`download_uniprot_meta_by_id`
You have a gene name / PDB ID, want UniProt	`download_uniprot_mapping`
You have a free-text query, want a list	`download_uniprot_search_by_query`
You need a complex cross-resource graph query (e.g. all enzymes in pathway X catalyzing reaction Y)	`download_uniprot_sparql_by_query`

SPARQL Quick Examples

Top 5 proteins by name:

sparql
PREFIX up: <http://purl.uniprot.org/core/>
SELECT ?p ?name WHERE {
  ?p a up:Protein .
  ?p up:recommendedName / up:fullName ?name .
} LIMIT 5

Proteins in human, with a specific GO term:

sparql
PREFIX up: <http://purl.uniprot.org/core/>
PREFIX taxon: <http://purl.uniprot.org/taxonomy/>
SELECT ?protein ?name
WHERE {
  ?protein a up:Protein .
  ?protein up:organism taxon:9606 .
  ?protein up:classifiedWith <http://purl.obolibrary.org/obo/GO_0003700> .
  ?protein up:recommendedName / up:fullName ?name .
} LIMIT 20

Common Mistakes

Passing an unreviewed entry's accession when you need curated info: filter with reviewed:true in your search.
Mapping in the wrong direction: fr=PDB to=UniProtKB_AC-ID for "PDB → UniProt"; flip for the reverse.
SPARQL timeout on broad queries: add LIMIT N; default endpoint timeout is harsh. Bump timeout parameter if your query is legitimately heavy.

References

UniProt REST API
UniProt query syntax
UniProt SPARQL endpoint — interactive query editor with examples
ID mapping db list

Frequently asked questions

What does the Uniprot Database AI skill do?

Why use Uniprot Database on TypingMind?

Because you install it once and use it with any model. Uniprot Database 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 Uniprot Database in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ai4protein/VenusFactory2/tree/main/src/agent/skills/uniprot_database. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Uniprot Database?

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 Uniprot Database?

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

Is the Uniprot Database AI skill free?

Yes. It is published on GitHub by ai4protein under the Unknown license. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

UniProt Database

Overview

Project Tools (VenusFactory2)

When to Use Which Tool

SPARQL Quick Examples

Common Mistakes

References

Frequently asked questions

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