Agent skill · Data & Analytics

tooluniverse-protein-structure-retrieval

Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-protein-structure-retrieval --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 11 KB
Bundled scripts: none
Path: skills/openclaw/tooluniverse-protein-structure-retrieval/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Protein Structure Data Retrieval Retrieve protein structures with proper disambiguation, quality assessment, and comprehensive metadata. **IMPORTANT**: Always use English terms in tool calls (protein names, organism names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language. ## Workflow Overview ``` Phase 0: Clarify (if needed) ↓ Phase 1: Disambiguate Protein Identity ↓ Phase 2: Retrieve Structures (Internal) ↓ Phase 3: Report Structure Profile ``` --- ## Phase 0: Clarification (When Needed) Ask the user ONLY if: - Protein name matches multiple genes/families (e.g., "kinase" → which kinase?) - Organism not specified for conserved proteins - Intent unclear: need experimental structure vs AlphaFold prediction? Skip clarification for: - Specific PDB IDs (4-character codes) - UniProt accessions - Unambiguous protein names with organism --- ## Phase 1: Protein Disambiguation ### 1.1 Resolve Protein Identity ```python from tooluniverse import ToolUniverse tu = ToolUniverse() tu.load_tools() # Strategy depends on input type if user_provided_pdb_id: # Direct structure retrieval pdb_id =

What's inside
Steps it walks through
  1. Workflow Overview
  2. Phase 0: Clarification (When Needed)
  3. Phase 1: Protein Disambiguation
  4. 1.1 Resolve Protein Identity
  5. 1.2 Identity Resolution Checklist
  6. 1.3 Handle Naming Collisions
  7. Phase 2: Data Retrieval (Internal)
  8. 2.1 Search Structures
  9. 2.2 Get Structure Details
  10. 2.3 PDBe Additional Data
  11. 2.4 AlphaFold Predictions
  12. Fallback Chains
  13. Phase 3: Report Structure Profile
  14. Output Structure
Ships with 2 files
  • CHECKLIST.md
  • examples.md
More from awesome-bio-agent-skills
All skills →
About this skill
What does the tooluniverse-protein-structure-retrieval skill do?

Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-protein-structure-retrieval --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going