Query Alphafold Agent Skill

Query AlphaFold protein structure predictions. Use when user asks about protein structure, 3D structure, protein folding, or structure prediction. Triggers on "alphafold", "protein structure", "3D structure", "folding", "pLDDT", "structure prediction".

703 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

132

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill query-alphafold

The instruction itself

6 sections, as written by the author

AlphaFold Structure Database Query

Query the AlphaFold EBI API for predicted protein structures.

When to Use

User asks about a protein's predicted 3D structure
User wants to download PDB/CIF structure files
User asks about structure confidence (pLDDT scores)
User wants to visualize protein structure

How to Execute

import requests
import json

BASE_URL = "https://alphafold.ebi.ac.uk/api"

# 1. Get prediction info
def get_alphafold_prediction(uniprot_id):
    url = f"{BASE_URL}/prediction/{uniprot_id}"
    r = requests.get(url)
    r.raise_for_status()
    return r.json()

# 2. Download structure file
def download_structure(uniprot_id, output_dir="/workspace/group", fmt="pdb", version="v4"):
    filename = f"AF-{uniprot_id}-F1-model_{version}.{fmt}"
    url = f"https://alphafold.ebi.ac.uk/files/{filename}"
    r = requests.get(url)
    r.raise_for_status()
    filepath = f"{output_dir}/{filename}"
    with open(filepath, 'wb') as f:
        f.write(r.content)
    return filepath

# 3. Get per-residue confidence (pLDDT)
def get_plddt(uniprot_id):
    url = f"{BASE_URL}/prediction/{uniprot_id}"
    r = requests.get(url)
    data = r.json()
    if isinstance(data, list) and data:
        cif_url = data[0].get("cifUrl", "")
        plddt_url = data[0].get("paeImageUrl", "")
        return {"cifUrl": cif_url, "paeImageUrl": plddt_url, "data": data[0]}
    return data

# Example
data = get_alphafold_prediction("P04637")  # TP53
if isinstance(data, list) and data:
    entry = data[0]
    print(f"UniProt: {entry.get('uniprotAccession')}")
    print(f"Gene: {entry.get('gene', 'N/A')}")
    print(f"Organism: {entry.get('organismScientificName', 'N/A')}")
    print(f"Model confidence: {entry.get('globalMetricValue', 'N/A')}")
    print(f"PDB URL: {entry.get('pdbUrl', 'N/A')}")
    print(f"CIF URL: {entry.get('cifUrl', 'N/A')}")

Endpoints

| Endpoint | URL | Use |

|----------|-----|-----|

| Prediction | /api/prediction/{uniprot_id} | Get model info & download URLs |

| Summary | /api/uniprot/summary/{uniprot_id}.json | Brief summary |

| Annotations | /api/annotations/{uniprot_id} | Per-residue annotations |

Download Formats

PDB: AF-{UNIPROT_ID}-F1-model_v4.pdb
CIF: AF-{UNIPROT_ID}-F1-model_v4.cif
PAE image: Available from prediction endpoint

Follow-up Suggestions

"Want me to analyze the structure confidence by region?"
"Should I compare this to the experimental PDB structure?"
"Want me to identify disordered regions?"

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13k tokens scripts

How to use it

Copy the folder

Take biotender-max/query-alphafold from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.