Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/naity/fm4life/alphafold)<a href="https://agentmods.dev/skills/naity/fm4life/alphafold"><img src="https://agentmods.dev/badge/skills/naity/fm4life/alphafold.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00141 | $0.02634 |
| Opus 5 | $0.00071 | $0.01317 |
| Sonnet 5 | $0.00028 | $0.00527 |
| Haiku 4.5 | $0.00014 | $0.00263 |
Grade A, and why
alphafold scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(url) How it starts
The opening of the file, as written. The whole thing — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaFold: Protein Structure Prediction
Overview
AlphaFold is DeepMind's protein structure prediction system. Most users don't need to install AlphaFold locally — there are faster, lighter alternatives that cover the majority of use cases:
| Need | Best approach |
|---|---|
| Known UniProt protein | AFDB API — fetch precomputed structure instantly |
| Novel protein sequence | ColabFold — no local databases needed |
| Novel protein, batch/HPC | Local AF2 (Docker) — full pipeline |
| Protein + DNA/RNA/ligand | AlphaFold3 (web server or local) |
| Fast, no MSA | ESMFold (see skills/esm2) |
Approach 1: AlphaFold Database (AFDB) API
The AFDB covers >200 million proteins from UniRef90 with precomputed structures. If your protein has a UniProt accession, fetch it in seconds — no GPU, no installation.
import requests
def fetch_afdb_structure(uniprot_id: str, output_dir: str = ".") -> dict:
"""
Fetch AlphaFold structure for a UniProt ID.
Returns metadata dict with paths to downloaded files.
"""
from pathlib import Path
# Get prediction metadata
url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}"
response = requests.get(url)
response.raise_for_status()
prediction = response.json()[0] # list with one entry
out_dir = Path(output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
downloaded = {}
# Download PDB
pdb_url = prediction["pdbUrl"]
pdb_path = out_dir / f"{uniprot_id}.pdb"
pdb_path.write_bytes(requests.get(pdb_url).content)
downloaded["pdb"] = str(pdb_path)
# Download PAE JSON (pairwise confidence)
if pae_url := prediction.get("paeDocUrl"):
pae_path = out_dir / f"{uniprot_id}_pae.json"
pae_path.write_bytes(requests.get(pae_url).content)
downloaded["pae"] = str(pae_path)
print(f"Downloaded {uniprot_id}: {pdb_path}")
print(f" pLDDT (mean): {prediction.get('globalMetricValue', 'N/A'):.1f}")
print(f" Model: {prediction.get('modelCreatedDate', 'N/A')}")
return downloaded
# Example
files = fetch_afdb_structure("P00533") # EGFR
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 260 lines · 141 tokens per session scan A 891bd6ac029b
alphafold is a skill published in the GitHub repository naity/FM4Life (2 stars, last pushed 5mo ago), licensed MIT. It adds 141 tokens to every session and 2,634 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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