UniProt Database

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/uniprot-database

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

UniProt Database

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Capabilities, Python Implementation, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Searching for protein entries by name, gene symbol, accession, or organism
• Retrieving protein sequences in FASTA or other formats
• Mapping identifiers between UniProt and external databases (Ensembl, RefSeq, PDB, etc.)
• Accessing protein annotations including GO terms, domains, and functional descriptions
• Batch retrieving multiple protein entries efficiently
• Querying reviewed (Swiss-Prot) vs. unreviewed (TrEMBL) protein data

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Overview

UniProt is the world's leading comprehensive protein sequence and functional information resource. Search proteins by name, gene, or accession, retrieve sequences in FASTA format, perform ID mapping across databases, access Swiss-Prot/TrEMBL annotations via REST API for protein analysis.

Imported: Core Capabilities

1. Searching for Proteins

Search UniProt using natural language queries or structured search syntax.

Common search patterns:

# Search by protein name
query = "insulin AND organism_name:\"Homo sapiens\""

# Search by gene name
query = "gene:BRCA1 AND reviewed:true"

# Search by accession
query = "accession:P12345"

# Search by sequence length
query = "length:[100 TO 500]"

# Search by taxonomy
query = "taxonomy_id:9606"  # Human proteins

# Search by GO term
query = "go:0005515"  # Protein binding

Use the API search endpoint:

https://rest.uniprot.org/uniprotkb/search?query={query}&format={format}

Supported formats: JSON, TSV, Excel, XML, FASTA, RDF, TXT

2. Retrieving Individual Protein Entries

Retrieve specific protein entries by accession number.

Accession number formats:

• Classic: P12345, Q1AAA9, O15530 (6 characters: letter + 5 alphanumeric)
• Extended: A0A022YWF9 (10 characters for newer entries)

Retrieve endpoint:

https://rest.uniprot.org/uniprotkb/{accession}.{format}

Example:

https://rest.uniprot.org/uniprotkb/P12345.fasta

3. Batch Retrieval and ID Mapping

Map protein identifiers between different database systems and retrieve multiple entries efficiently.

ID Mapping workflow:

1. Submit mapping job to:

   https://rest.uniprot.org/idmapping/run

2. Check job status:

   https://rest.uniprot.org/idmapping/status/{jobId}

3. Retrieve results:

   https://rest.uniprot.org/idmapping/results/{jobId}

Supported databases for mapping:

• UniProtKB AC/ID
• Gene names
• Ensembl, RefSeq, EMBL
• PDB, AlphaFoldDB
• KEGG, GO terms
• And many more (see

  /references/id_mapping_databases.md

  )

Limitations:

• Maximum 100,000 IDs per job
• Results stored for 7 days

4. Streaming Large Result Sets

For large queries that exceed pagination limits, use the stream endpoint:

https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}

The stream endpoint returns all results without pagination, suitable for downloading complete datasets.

5. Customizing Retrieved Fields

Specify exactly which fields to retrieve for efficient data transfer.

Common fields:

• accession

  - UniProt accession number

• id

  - Entry name

• gene_names

  - Gene name(s)

• organism_name

  - Organism

• protein_name

  - Protein names

• sequence

  - Amino acid sequence

• length

  - Sequence length

• go_*

  - Gene Ontology annotations

• cc_*

  - Comment fields (function, interaction, etc.)

• ft_*

  - Feature annotations (domains, sites, etc.)

Example:

https://rest.uniprot.org/uniprotkb/search?query=insulin&fields=accession,gene_names,organism_name,length,sequence&format=tsv

See

/references/api_fields.md

Examples

Example 1: Ask for the upstream workflow directly

Use @uniprot-database to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @uniprot-database against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @uniprot-database for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @uniprot-database using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Query Syntax Examples

Boolean operators:

kinase AND organism_name:human
(diabetes OR insulin) AND reviewed:true
cancer NOT lung

Field-specific searches:

gene:BRCA1
accession:P12345
organism_id:9606
taxonomy_name:"Homo sapiens"
annotation:(type:signal)

Range queries:

length:[100 TO 500]
mass:[50000 TO 100000]

Wildcards:

gene:BRCA*
protein_name:kinase*

See

/references/query_syntax.md

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Use reviewed entries when possible: Filter with reviewed:true for Swiss-Prot (manually curated) entries
• Specify format explicitly: Choose the most appropriate format (FASTA for sequences, TSV for tabular data, JSON for programmatic parsing)
• Use field selection: Only request fields you need to reduce bandwidth and processing time
• Handle pagination: For large result sets, implement proper pagination or use the stream endpoint
• Cache results: Store frequently accessed data locally to minimize API calls
• Rate limiting: Be respectful of API resources; implement delays for large batch operations
• Check data quality: TrEMBL entries are computational predictions; Swiss-Prot entries are manually reviewed

Imported Operating Notes

Imported: Best Practices

1. Use reviewed entries when possible: Filter with

   reviewed:true

   for Swiss-Prot (manually curated) entries
2. Specify format explicitly: Choose the most appropriate format (FASTA for sequences, TSV for tabular data, JSON for programmatic parsing)
3. Use field selection: Only request fields you need to reduce bandwidth and processing time
4. Handle pagination: For large result sets, implement proper pagination or use the stream endpoint
5. Cache results: Store frequently accessed data locally to minimize API calls
6. Rate limiting: Be respectful of API resources; implement delays for large batch operations
7. Check data quality: TrEMBL entries are computational predictions; Swiss-Prot entries are manually reviewed

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/uniprot-database

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @trpc-fullstack

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @trust-calibrator

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @turborepo-caching

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @tutorial-engineer

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Resources

scripts/

uniprot_client.py

references/

• api_fields.md

  - Complete list of available fields for customizing queries

• id_mapping_databases.md

  - Supported databases for ID mapping operations

• query_syntax.md

  - Comprehensive query syntax with advanced examples

• api_examples.md

  - Code examples in multiple languages (Python, curl, R)

Imported: Additional Resources

• API Documentation: https://www.uniprot.org/help/api
• Interactive API Explorer: https://www.uniprot.org/api-documentation
• REST Tutorial: https://www.uniprot.org/help/uniprot_rest_tutorial
• Query Syntax Help: https://www.uniprot.org/help/query-fields
• SPARQL Endpoint: https://sparql.uniprot.org/ (for advanced graph queries)

Imported: Python Implementation

For programmatic access, use the provided helper script

scripts/uniprot_client.py

• search_proteins(query, format)

  - Search UniProt with any query

• get_protein(accession, format)

  - Retrieve single protein entry

• map_ids(ids, from_db, to_db)

  - Map between identifier types

• batch_retrieve(accessions, format)

  - Retrieve multiple entries

• stream_results(query, format)

  - Stream large result sets

Alternative Python packages:

• Unipressed: Modern, typed Python client for UniProt REST API
• bioservices: Comprehensive bioinformatics web services client

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.