Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontooluniverse-protein-structure-retrievalExecute the skills CLI command in your project's root directory to begin installation:
Fetches tooluniverse-protein-structure-retrieval from mims-harvard/tooluniverse and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate tooluniverse-protein-structure-retrieval. Access via /tooluniverse-protein-structure-retrieval in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.
IMPORTANT: Always use English terms in tool calls. Respond in the user's language.
LOOK UP DON'T GUESS: Never assume PDB IDs, resolution, or availability. Always query RCSB/PDBe and AlphaFold to confirm.
Not all structures are equal. X-ray <2 A is high-quality for drug design. Cryo-EM 3-4 A is good for fold but not side chains. AlphaFold is excellent for well-folded domains but unreliable for disordered regions. Always check pLDDT (AlphaFold) or resolution (experimental) before drawing conclusions.
Phase 0: Clarify (if needed) → Phase 1: Disambiguate Protein → Phase 2: Retrieve Structures → Phase 3: Report
Ask ONLY if: protein name ambiguous (e.g., "kinase"), organism not specified, unclear if experimental vs AlphaFold needed. Skip for: specific PDB IDs, UniProt accessions, unambiguous protein+organism.
# By PDB ID: direct retrieval
# By UniProt: get AlphaFold + search experimental structures
af_structure = tu.tools.alphafold_get_prediction(uniprot_id=uniprot_id)
# By protein name: search
result = tu.tools.PDBeSearch_search_structures(protein_name=protein_name)
Retrieve silently. Do NOT narrate the process.
pdb_id = "4INS"
# Search, metadata, quality, ligands, similar structures
result = tu.tools.PDBeSearch_search_structures(protein_name=name)
metadata = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id)
exp = tu.tools.RCSBData_get_entry(pdb_id=pdb_id)
quality = tu.tools.PDBeValidation_get_quality_scores(pdb_id=pdb_id)
ligands = tu.tools.PDBe_KB_get_ligand_sites(pdb_id=pdb_id)
similar = tu.tools.PDBeSIFTS_get_all_structures(pdb_id=pdb_id, cutoff=2.0)
# PDBe additional data
summary = tu.tools.pdbe_get_entry_summary(pdb_id=pdb_id)
molecules = tu.tools.pdbe_get_entry_molecules(pdb_id=pdb_id)
# AlphaFold (when no experimental structure, or for comparison)
af = tu.tools.alphafold_get_prediction(uniprot_id=uniprot_id)
| Primary | Fallback |
|---|---|
| RCSB search | PDBe search |
| get_protein_metadata | pdbe_get_entry_summary |
| Experimental structure | AlphaFold prediction |
| get_protein_ligands | PDBe_KB_get_ligand_sites |
Present as a Structure Profile Report. Hide search process. Include:
| Tier | Criteria |
|---|---|
| Excellent | X-ray <1.5A, complete, R-free <0.22 |
| High | X-ray <2.0A OR Cryo-EM <3.0A |
| Good | X-ray 2.0-3.0A OR Cryo-EM 3.0-4.0A |
| Moderate | X-ray >3.0A OR NMR ensemble |
| Low | >4.0A, incomplete, or problematic |
<1.5A: atomic detail, H-bond analysis. 1.5-2.0A: drug design. 2.0-2.5A: structure-based design. 2.5-3.5A: overall architecture. >3.5A: domain arrangement only.
90: very high, experimental-like. 70-90: good backbone. 50-70: uncertain/flexible. <50: likely disordered.
| Error | Response |
|---|---|
| "PDB ID not found" | Verify 4-char format, check if obsoleted |
| "No structures" | Offer AlphaFold, suggest similar proteins |
| "Download failed" | Retry once, provide alternative link |
| "Resolution unavailable" | Likely NMR/model, note in assessment |
RCSB PDB: PDBeSearch_search_structures (search), get_protein_metadata_by_pdb_id (basic info), RCSBData_get_entry (details), PDBeValidation_get_quality_scores (quality), PDBe_KB_get_ligand_sites (ligands), PDBeSIFTS_get_all_structures (homologs)
PDBe: pdbe_get_entry_summary (overview), pdbe_get_entry_molecules (entities), pdbe_get_experiment_info (experimental), PDBe_KB_get_ligand_sites (pockets)
AlphaFold: alphafold_get_prediction (get prediction), alphafold_get_summary (search)
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
I recommend tooluniverse-protein-structure-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in tooluniverse-protein-structure-retrieval — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
tooluniverse-protein-structure-retrieval is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
tooluniverse-protein-structure-retrieval is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend tooluniverse-protein-structure-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tooluniverse-protein-structure-retrieval fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: tooluniverse-protein-structure-retrieval is focused, and the summary matches what you get after install.
Registry listing for tooluniverse-protein-structure-retrieval matched our evaluation — installs cleanly and behaves as described in the markdown.
We added tooluniverse-protein-structure-retrieval from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in tooluniverse-protein-structure-retrieval — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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