A skill for checking whether designed protein sequences are likely to fold into the intended three-dimensional structures using AlphaFold2, a protein-structure prediction system.

Validating protein designs, predicting binder–target complexes, comparing predicted structures with design intent, and checking multi-chain complexes. It requires suitable computing resources such as a compatible GPU or a configured Modal setup.

It provides structure predictions and confidence measures that help identify designs needing further review before laboratory testing.

alphafold — Skill for Claude Code and Codex by adaptyvbio

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills

agentmods

npx agentmods add skills/adaptyvbio/protein-design-skills/alphafold

Made for: Claude Code, Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/alphafold/github.svg)](https://agentmods.dev/skills/adaptyvbio/protein-design-skills/alphafold)

Your own site

<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/alphafold"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/alphafold/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/alphafold"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/alphafold.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,435 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00100	$0.01435
Opus 5	$0.00050	$0.00718
Sonnet 5	$0.00020	$0.00287
Haiku 4.5	$0.00010	$0.00144

Measured 11d ago against content hash 8a4a7e406273, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

alphafold scanned grade A with 0 findings 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 11d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/alphafold/SKILL.md · 184 lines

How it starts

The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AlphaFold2 Structure Validation

Prerequisites

Requirement	Minimum	Recommended
Python	3.8+	3.10
CUDA	11.0+	12.0+
GPU VRAM	32GB	40GB (A100)
RAM	32GB	64GB
Disk	100GB	500GB (for databases)

How to run

First time? See Getting started to set up Modal and biomodals.

Option 1: Modal (AlphaFold-Multimer)

cd biomodals
modal run modal_alphafold.py \
  --input-fasta sequences.fasta \
  --out-dir output/

GPU: A100 (40GB) | Timeout: 3600s default

Option 2: Local installation

git clone https://github.com/google-deepmind/alphafold.git
cd alphafold

python run_alphafold.py \
  --fasta_paths=query.fasta \
  --output_dir=output/ \
  --model_preset=monomer \
  --max_template_date=2026-01-01

Option 3: ESMFold2 (fast single-sequence)

printf '>protein|A\nMKTAYIAKQRQISFVK...\n' > seq.faa
uv run --with modal modal run modal_esmfold2.py --input-faa seq.faa

Key parameters

Parameter	Default	Options	Description
`--model_preset`	monomer	monomer/multimer	Model type
`--num_recycle`	3	1-20	Recycling iterations
`--max_template_date`	-	YYYY-MM-DD	Template cutoff
`--use_templates`	True	True/False	Use template search

Output format

output/
├── ranked_0.pdb           # Best model
├── ranked_1.pdb           # Second best
├── ranking_debug.json     # Confidence scores
├── result_model_1.pkl     # Full results
├── msas/                  # MSA files
└── features.pkl           # Input features

Extracting metrics

import pickle

with open('result_model_1.pkl', 'rb') as f:
    result = pickle.load(f)

plddt = result['plddt']
ptm = result['ptm']
iptm = result.get('iptm', None)  # Multimer only
pae = result['predicted_aligned_error']

Sample output

Successful run

$ python run_alphafold.py --fasta_paths complex.fasta --model_preset multimer
[INFO] Running MSA search...
[INFO] Running model 1/5...
[INFO] Running model 5/5...
[INFO] Relaxing structures...

Results:
  ranked_0.pdb:
    pLDDT: 87.3 (mean)
    pTM: 0.78
    ipTM: 0.62
    PAE (interface): 8.5

Saved to output/

Read the full file on GitHub · 184 lines

Files

What ships with it

1 file 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.

references/multimer.md 3.5 KB

Changes

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.

11d ago First seen · 184 lines · 100 tokens per session scan A 8a4a7e406273

Subscribe to this mod's changes

alphafold is a skill published in the GitHub repository adaptyvbio/protein-design-skills (158 stars, last pushed 3mo ago), licensed MIT. It adds 100 tokens to every session and 1,435 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Claude Code Data and AI Scientific Structure Prediction agent-skills design-tools protein-design protein-engineering reference validation

alphafold

Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction…

FreedomIntelligence/OpenClaw-Medical-Skills · 100 tokens

alphafold2-multimer

AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5)…

zongtingwei/Bioclaw_Skills_Hub · 117 tokens

chai

Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2.…

BioTender-max/ProteinClaw · 107 tokens

alphafold

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

AlphaFold2 Structure Validation

Prerequisites

How to run

Option 1: Modal (AlphaFold-Multimer)

Option 2: Local installation

Option 3: ESMFold2 (fast single-sequence)

Key parameters

Output format

Extracting metrics

Sample output

Successful run

What ships with it

What this file has done since we first saw it

Other skills, from other repositories

alphafold

alphafold2-multimer

alphafold2-multimer

alphafold

alphafold

chai