Agent skill

Alphafold API

by wentorai in wentorai/research-plugins

Query AlphaFold protein structure predictions by UniProt accession

Install Alphafold API

skills CLI

$ npx skills add wentorai/research-plugins --skill alphafold-api -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins alphafold-api --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/domains/biomedical/alphafold-api .claude/skills/alphafold-api && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "alphafold-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/biomedical/alphafold-api into .claude/skills/alphafold-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold-api", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/wentorai/research-plugins/tree/main/skills/domains/biomedical/alphafold-api

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add wentorai/research-plugins --skill alphafold-api -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins alphafold-api --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/domains/biomedical/alphafold-api .agents/skills/alphafold-api && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "alphafold-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/biomedical/alphafold-api into .agents/skills/alphafold-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold-api", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add wentorai/research-plugins --skill alphafold-api -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins alphafold-api --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/domains/biomedical/alphafold-api .cursor/skills/alphafold-api && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "alphafold-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/biomedical/alphafold-api into .cursor/skills/alphafold-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold-api", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/wentorai/research-plugins.git --path skills/domains/biomedical/alphafold-api

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add wentorai/research-plugins --skill alphafold-api -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins alphafold-api --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/domains/biomedical/alphafold-api .gemini/skills/alphafold-api && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "alphafold-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/biomedical/alphafold-api into .gemini/skills/alphafold-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold-api", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install wentorai/research-plugins alphafold-api

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add wentorai/research-plugins --skill alphafold-api -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/domains/biomedical/alphafold-api .github/skills/alphafold-api && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "alphafold-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/biomedical/alphafold-api into .github/skills/alphafold-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold-api", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add wentorai/research-plugins --skill alphafold-api -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins alphafold-api --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/domains/biomedical/alphafold-api .opencode/skills/alphafold-api && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "alphafold-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/biomedical/alphafold-api into .opencode/skills/alphafold-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "alphafold-api", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: alphafold-api
GitHub stars: 298
Used in: 1 other repo
Token cost: ~2k tokens
SKILL.md length: 411 words
Files: 1
Skills in repo: 405
Repo updated: Jun 19, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Query AlphaFold protein structure predictions by UniProt accession

Works in 4 steps: Get Prediction by UniProt Accession → Per-Residue Confidence Scores (pLDDT) → UniProt Summary (3D-Beacons Format) → …
Tasks that involve Protein structure and design
SKILL.md covers Overview, Authentication, Core Endpoints and Key Response Fields, plus 4 more sections
Calls curl; reaches alphafold.ebi.ac.uk and ftp.ebi.ac.uk

What it does

Alphafold API is an agent skill from wentorai/research-plugins. Query AlphaFold protein structure predictions by UniProt accession

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Protein structure and design. It works with AlphaFold and UniProt. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

Tasks that involve Protein structure and design

Example prompts

“/alphafold-api”

Requirements

Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

Get Prediction by UniProt Accession
Per-Residue Confidence Scores (pLDDT)
UniProt Summary (3D-Beacons Format)
Download Structure Files

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- curl
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- alphafold.ebi.ac.uk
- ftp.ebi.ac.uk
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Alphafold API loads about 2k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 411 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~20
When it runs · the whole SKILL.md, loaded when a task matches: ~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 411 words, ~2,012 tokens.

Download SKILL.mdSave it as .claude/skills/alphafold-api/SKILL.md (or your agent's skills folder).

name: alphafold-api
description: Query AlphaFold protein structure predictions by UniProt accession

AlphaFold Protein Structure Database API

Overview

The AlphaFold DB, maintained by EMBL-EBI and DeepMind, provides open access to over 200 million protein structure predictions. The REST API enables programmatic lookup of predicted structures, confidence metrics (pLDDT, PAE), and downloadable structure files (PDB, mmCIF, BinaryCIF) keyed on UniProt accessions. Free, no authentication required.

Authentication

None. All endpoints are publicly accessible without API keys or tokens.

Core Endpoints

Base URL: https://alphafold.ebi.ac.uk/api

1. Get Prediction by UniProt Accession

Retrieves all AlphaFold models for a given UniProt accession or model ID.

bash

curl "https://alphafold.ebi.ac.uk/api/prediction/P04637"

Response (first entry, abbreviated):

json

[
  {
    "entryId": "AF-P04637-F1",
    "uniprotAccession": "P04637",
    "uniprotId": "P53_HUMAN",
    "uniprotDescription": "Cellular tumor antigen p53",
    "gene": "TP53",
    "organismScientificName": "Homo sapiens",
    "taxId": 9606,
    "globalMetricValue": 75.06,
    "fractionPlddtVeryHigh": 0.527,
    "fractionPlddtConfident": 0.071,
    "fractionPlddtLow": 0.104,
    "fractionPlddtVeryLow": 0.298,
    "latestVersion": 6,
    "modelCreatedDate": "2025-08-01T00:00:00Z",
    "sequenceStart": 1,
    "sequenceEnd": 393,
    "pdbUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb",
    "cifUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif",
    "bcifUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.bcif",
    "paeImageUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.png",
    "paeDocUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.json",
    "plddtDocUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-confidence_v6.json",
    "amAnnotationsUrl": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-aa-substitutions.csv"
  }
]

2. Per-Residue Confidence Scores (pLDDT)

Download the per-residue pLDDT confidence JSON linked in plddtDocUrl:

bash

curl "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-confidence_v6.json"

Response (truncated):

json

{
  "residueNumber": [1, 2, 3, 4, 5],
  "confidenceScore": [40.66, 44.53, 49.97, 48.59, 44.88],
  "confidenceCategory": ["D", "D", "D", "D", "D"]
}

Categories: A (Very High, >90), B (Confident, 70-90), C (Low, 50-70), D (Very Low, <50).

3. UniProt Summary (3D-Beacons Format)

Returns model metadata following the 3D-Beacons data standard:

bash

curl "https://alphafold.ebi.ac.uk/api/uniprot/summary/P04637.json"

Response (abbreviated):

json

{
  "uniprot_entry": {
    "ac": "P04637",
    "id": "P53_HUMAN",
    "sequence_length": 393
  },
  "structures": [
    {
      "summary": {
        "model_identifier": "AF-P04637-F1",
        "model_url": "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif",
        "provider": "AlphaFold DB",
        "confidence_type": "pLDDT",
        "confidence_avg_local_score": 75.06,
        "coverage": 1.0
      }
    }
  ]
}

4. Download Structure Files

Structure files are available at the URLs returned in prediction responses:

bash

# PDB format
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.pdb"

# mmCIF format
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-model_v6.cif"

# Predicted Aligned Error (PAE) matrix
curl -O "https://alphafold.ebi.ac.uk/files/AF-P04637-F1-predicted_aligned_error_v6.json"

Key Response Fields

Field	Type	Description
`entryId`	string	AlphaFold model ID (e.g., `AF-P04637-F1`)
`uniprotAccession`	string	UniProt accession code
`gene`	string	Gene symbol
`globalMetricValue`	float	Average pLDDT score (0-100)
`fractionPlddtVeryHigh`	float	Fraction of residues with pLDDT > 90
`fractionPlddtConfident`	float	Fraction with pLDDT 70-90
`fractionPlddtLow`	float	Fraction with pLDDT 50-70
`fractionPlddtVeryLow`	float	Fraction with pLDDT < 50
`pdbUrl`	string	Direct download URL for PDB file
`cifUrl`	string	Direct download URL for mmCIF file
`paeDocUrl`	string	URL for predicted aligned error JSON
`plddtDocUrl`	string	URL for per-residue confidence JSON
`latestVersion`	int	Model version number

Show full SKILL.md (169 more words)Show less

Rate Limits

The AlphaFold DB API has no published per-request rate limits. EMBL-EBI's general fair use policy applies: usage that degrades service for others may result in blocking. For bulk downloads (entire proteomes), use the FTP archive at https://ftp.ebi.ac.uk/pub/databases/alphafold/ rather than repeated API calls.

Python Example

python

import requests


def get_alphafold_prediction(uniprot_id: str) -> dict:
    """Fetch AlphaFold structure prediction for a UniProt accession."""
    url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}"
    resp = requests.get(url)
    resp.raise_for_status()
    entries = resp.json()
    # Return the canonical (first) entry
    return entries[0] if entries else None


def get_confidence_scores(prediction: dict) -> dict:
    """Download per-residue pLDDT confidence scores."""
    resp = requests.get(prediction["plddtDocUrl"])
    resp.raise_for_status()
    return resp.json()


def download_structure(prediction: dict, fmt: str = "pdb",
                       output_dir: str = ".") -> str:
    """Download structure file in pdb, cif, or bcif format."""
    url_key = {"pdb": "pdbUrl", "cif": "cifUrl", "bcif": "bcifUrl"}[fmt]
    url = prediction[url_key]
    filename = url.split("/")[-1]
    path = f"{output_dir}/{filename}"

    resp = requests.get(url)
    resp.raise_for_status()
    with open(path, "wb") as f:
        f.write(resp.content)
    return path


# Example: fetch p53 structure and assess quality
pred = get_alphafold_prediction("P04637")
print(f"Gene: {pred['gene']} ({pred['uniprotDescription']})")
print(f"Organism: {pred['organismScientificName']}")
print(f"Average pLDDT: {pred['globalMetricValue']}")
print(f"Very high confidence: {pred['fractionPlddtVeryHigh']:.1%}")

# Download per-residue scores
scores = get_confidence_scores(pred)
high_conf = [i+1 for i, c in enumerate(scores["confidenceCategory"])
             if c in ("A", "B")]
print(f"High-confidence residues: {len(high_conf)}/{len(scores['residueNumber'])}")

# Download PDB file
path = download_structure(pred, fmt="pdb")
print(f"Structure saved to: {path}")

Academic Use Cases

Drug target assessment: Check pLDDT scores in binding pockets before docking
Homology model comparison: Compare AlphaFold predictions with experimental PDB structures
Disorder prediction: Low pLDDT regions (<50) correlate with intrinsically disordered regions
Variant interpretation: Use AlphaMissense annotations (via amAnnotationsUrl) to assess pathogenicity
Structural coverage: Quickly check if a protein of interest has a predicted structure
Batch proteome analysis: Retrieve predictions for all proteins in a reference proteome

References

AlphaFold Protein Structure Database
AlphaFold DB API Documentation
AlphaFold DB FTP (Bulk Downloads)
Jumper, J. et al. (2021). "Highly accurate protein structure prediction with AlphaFold." Nature 596, 583-589.
Varadi, M. et al. (2022). "AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models." Nucleic Acids Research 50(D1).

Files

Just SKILL.md in skills/domains/biomedical/alphafold-api of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

brycewang-stanford/Auto-Empirical-Research-Skillsskills/43-wentorai-research-plugins/skills/domains/biomedical/alphafold-api

Compare with similar skills

Alphafold API next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Alphafold API compared with similar skills
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Bio DB ToolsDrugClaw/DrugClaw	125	—	~1.4k	Automated safety check: Pass	Apache-2.0	6 mo ago
Ggetdavila7/claude-code-templates	32k	11 repos	~6.3k	Automated safety check: Pass	MIT	today
Alphafold Databasedavila7/claude-code-templates	32k	10 repos	~4k	Automated safety check: Pass	MIT	today
Foldseek Structural Searchgoogle-deepmind/science-skills	3.2k	1 repos	~1.3k	Automated safety check: Pass	Apache-2.0	23 days ago

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Questions about Alphafold API

What does Alphafold API do?

Query AlphaFold protein structure predictions by UniProt accession. Alphafold API is an agent skill from wentorai/research-plugins.

When should I use Alphafold API?

Alphafold API fits situations like: tasks that involve Protein structure and design.

How do I install Alphafold API in Claude Code?

Run `npx skills add wentorai/research-plugins --skill alphafold-api -a claude-code`. Or copy the skill folder (skills/domains/biomedical/alphafold-api in wentorai/research-plugins) into .claude/skills/alphafold-api in your project. Claude Code loads it when a task matches its description.

How do I install Alphafold API in Codex?

Run `npx skills add wentorai/research-plugins --skill alphafold-api -a codex`. Or copy the skill folder (skills/domains/biomedical/alphafold-api in wentorai/research-plugins) into .agents/skills/alphafold-api in your project. Codex loads it when a task matches its description.

Can I use Alphafold API in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill alphafold-api -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/alphafold-api, .gemini/skills/alphafold-api, .github/skills/alphafold-api and .opencode/skills/alphafold-api in your project.

What does Alphafold API need to run?

Going by SKILL.md and its folder, Alphafold API needs the command-line tools its instructions call (curl). Our summary lists: Python 3.

Does Alphafold API access the network?

SKILL.md names 2 domains. In commands or code: alphafold.ebi.ac.uk and ftp.ebi.ac.uk; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Alphafold API safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Alphafold API use?

Alphafold API is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Alphafold API use?

About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Alphafold API?

Skills that share tags, products or a category with Alphafold API: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Bio DB Tools (DrugClaw/DrugClaw, 125 stars), Gget (davila7/claude-code-templates, 32k stars) and Alphafold Database (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Alphafold API?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Alphafold API

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

AlphaFold Protein Structure Database API

Overview

Authentication

Core Endpoints

1. Get Prediction by UniProt Accession

2. Per-Residue Confidence Scores (pLDDT)

3. UniProt Summary (3D-Beacons Format)

4. Download Structure Files

Key Response Fields

Rate Limits

Python Example

Academic Use Cases

References

Files

Used in 1 other repository

Compare with similar skills

Similar skills

More from wentorai/research-plugins

Related

Questions about Alphafold API