tooluniverse-protein-structure-retrieval

mims-harvard/tooluniverse · updated Apr 8, 2026

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$npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-protein-structure-retrieval
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summary

Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.

skill.md

Protein Structure Data Retrieval

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.

Domain Reasoning

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.

Workflow

Phase 0: Clarify (if needed) → Phase 1: Disambiguate Protein → Phase 2: Retrieve Structures → Phase 3: Report

Phase 0: Clarification (When Needed)

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.


Phase 1: Protein Disambiguation

# 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)

Identity Checklist

  • Protein name/gene identified, organism confirmed
  • UniProt accession (if available), isoform/variant specified (if relevant)

Phase 2: Data Retrieval (Internal)

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)

Fallback Chains

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

Phase 3: Report Structure Profile

Present as a Structure Profile Report. Hide search process. Include:

  1. Search Summary: query, organism, experimental + AlphaFold structure counts
  2. Best Structure: PDB ID, UniProt, organism, method, resolution, date, quality assessment
  3. Experimental Details: method, resolution, R-factor, R-free, space group
  4. Composition: chains, residues (coverage%), ligands, waters, metals
  5. Bound Ligands: ligand ID, name, type, binding site
  6. Binding Site Details (for drug discovery): location, key residues, druggability
  7. Alternative Structures: ranked by quality with resolution, method, ligands
  8. AlphaFold Prediction: UniProt, model version, pLDDT confidence distribution, use cases
  9. Structure Comparison: resolution, completeness, ligands across structures
  10. Download Links: PDB/mmCIF/AlphaFold formats, database URLs

Quality Assessment

Experimental Structures

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

Resolution Use Cases

<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.

AlphaFold Confidence (pLDDT)

90: very high, experimental-like. 70-90: good backbone. 50-70: uncertain/flexible. <50: likely disordered.


Error Handling

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

Tool Reference

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)

how to use tooluniverse-protein-structure-retrieval

How to use tooluniverse-protein-structure-retrieval on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tooluniverse-protein-structure-retrieval
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-protein-structure-retrieval

The skills CLI fetches tooluniverse-protein-structure-retrieval from GitHub repository mims-harvard/tooluniverse and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tooluniverse-protein-structure-retrieval

Reload or restart Cursor to activate tooluniverse-protein-structure-retrieval. Access the skill through slash commands (e.g., /tooluniverse-protein-structure-retrieval) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.738 reviews
  • Ganesh Mohane· Dec 12, 2024

    I recommend tooluniverse-protein-structure-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sakshi Patil· Nov 3, 2024

    Useful defaults in tooluniverse-protein-structure-retrieval — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chaitanya Patil· Oct 22, 2024

    tooluniverse-protein-structure-retrieval is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Diya Flores· Sep 25, 2024

    tooluniverse-protein-structure-retrieval is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Yuki Taylor· Sep 25, 2024

    I recommend tooluniverse-protein-structure-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Layla Malhotra· Sep 9, 2024

    tooluniverse-protein-structure-retrieval fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Rahul Santra· Sep 1, 2024

    Solid pick for teams standardizing on skills: tooluniverse-protein-structure-retrieval is focused, and the summary matches what you get after install.

  • Amina Choi· Aug 28, 2024

    Registry listing for tooluniverse-protein-structure-retrieval matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Pratham Ware· Aug 20, 2024

    We added tooluniverse-protein-structure-retrieval from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Nikhil Lopez· Aug 16, 2024

    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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