Alphafold

What it does

When your agent uses it

• Validating designed sequences fold correctly
• Predicting binder-target complex structures
• Calculating confidence metrics (pLDDT
• Self-consistency validation of designs

Example prompts

• “/alphafold”

Requirements

• Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 59dd633. 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:

  • python
  • modal
  • git
  • uv

  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:

  • github.com

  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 loads about 1.2k tokens when it runs, and up to ~2.1k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 231 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~108

When it runs · the whole SKILL.md, loaded when a task matches

~1.2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~2.1k

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

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 adaptyvbio/protein-design-skills at commit 59dd633, republished under its MIT licence (© adaptyvbio). 231 words, ~1,183 tokens.

name

alphafold

description

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 with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.

license

MIT

category

design-tools

tags

structure-prediction, validation, reference

biomodals_script

modal_alphafold.py

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

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

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

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

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']

$ 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/

Should I use AlphaFold?
│
├─ What are you predicting?
│  ├─ Single protein → ESMFold (faster)
│  ├─ Protein-protein complex → AlphaFold/ColabFold ✓
│  ├─ Protein + ligand → Chai or Boltz
│  └─ Batch of sequences → ColabFold ✓
│
├─ What do you need?
│  ├─ Highest accuracy → AlphaFold/ColabFold ✓
│  ├─ Fast screening → ESMFold
│  └─ MSA-free prediction → Chai or ESMFold
│
└─ Which AF2 option?
   ├─ Local installation → Full control, slow setup
   ├─ ColabFold → Easier, MSA server
   └─ Modal → Recommended for batch

find output -name "ranked_0.pdb" | wc -l  # Should match input count

© adaptyvbio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/alphafold of adaptyvbio/protein-design-skills.

• SKILL.md
• references/multimer.md

Open the folder on GitHubat commit 59dd633

Used in 4 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in adaptyvbio/protein-design-skills, which our catalogue first saw on October 7, 2026.

• majiayu000/claude-skill-registryskills/ai-ml/alphafoldskills/ai-ml/alphafold-adaptyvbio-protein-design-skill+1 more
• NeverSight/learn-skills.devdata/skills-md/adaptyvbio/protein-design-skills/alphafold
• FreedomIntelligence/OpenClaw-Medical-Skillsskills/alphafold
• BioTender-max/awesome-bio-agent-skillsskills/adaptyv/alphafold