Bindcraft

What it does

When your agent uses it

• Designing protein binders with built-in AF2 validation
• Running production-quality binder campaigns
• Using different design protocols (fast
• Need joint backbone and sequence optimization

Example prompts

• “/bindcraft”

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:

  • modal
  • git
  • python

  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

Bindcraft loads about 1.3k tokens when it runs, and up to ~2.5k if it reads all its reference files. Until then it costs about 119 tokens; SKILL.md has 277 words of instructions outside code blocks.

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

~119

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

~1.3k

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

~2.5k

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). 277 words, ~1,321 tokens.

name

bindcraft

description

End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.

license

MIT

category

design-tools

tags

structure-design, sequence-design, binder, pipeline

proteinbase_slug

bindcraft

proteinbase_url

https://proteinbase.com/design-methods/bindcraft

biomodals_script

modal_bindcraft.py

cd biomodals
modal run modal_bindcraft.py \
  --input-pdb target.pdb \
  --target-chains A \
  --target-hotspot-residues "45,67,89" \
  --lengths "70,100" \
  --number-of-final-designs 50

git clone https://github.com/martinpacesa/BindCraft.git
cd BindCraft

# BindCraft is configured with JSON files, not flags
python -u ./bindcraft.py \
  --settings ./settings_target/mytarget.json \
  --filters ./settings_filters/default_filters.json \
  --advanced ./settings_advanced/default_4stage_multimer.json

output/
├── design_0/
│   ├── binder.pdb         # Final design
│   ├── complex.pdb        # Binder + target
│   ├── metrics.json       # QC scores
│   └── trajectory/        # Optimization trajectory
├── design_1/
│   └── ...
└── summary.csv            # All metrics

{
  "plddt": 0.89,
  "ptm": 0.78,
  "iptm": 0.62,
  "pae": 8.5,
  "rmsd": 1.2,
  "sequence": "MKTAYIAK..."
}

$ modal run modal_bindcraft.py --input-pdb target.pdb --target-chains A --target-hotspot-residues "45,67,89" --number-of-final-designs 50
[INFO] Loading BindCraft model...
[INFO] Target: target.pdb (chain A)
[INFO] Hotspots: 45, 67, 89
[INFO] Generating designs...

Design 1/50:
  Length: 78 AA
  pLDDT: 0.89, ipTM: 0.62
  Saved: output/design_0/

Design 50/50:
  Length: 85 AA
  pLDDT: 0.86, ipTM: 0.58
  Saved: output/design_49/

[INFO] Campaign complete. Summary: output/summary.csv
Pass rate: 32/50 (64%) with ipTM > 0.5

Should I use BindCraft?
│
├─ What type of design?
│  ├─ Production-quality binders → BindCraft ✓
│  ├─ High diversity exploration → RFdiffusion
│  └─ All-atom precision → BoltzGen
│
├─ What matters most?
│  ├─ Experimental success rate → BindCraft ✓
│  ├─ Speed / diversity → RFdiffusion + ProteinMPNN
│  ├─ AF2 gradient optimization → ColabDesign
│  └─ All-atom control → BoltzGen
│
└─ Compute resources?
   ├─ Have L40S/A100 → BindCraft ✓
   └─ Only A10G → RFdiffusion + ProteinMPNN

find output -name "binder.pdb" | wc -l  # Should match num_designs

© 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 2 other files (references) in skills/bindcraft of adaptyvbio/protein-design-skills.

• SKILL.md
• references/protocols.md
• references/troubleshooting.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/bindcraftskills/ai-ml/bindcraft-adaptyvbio-protein-design-skill+1 more
• NeverSight/learn-skills.devdata/skills-md/adaptyvbio/protein-design-skills/bindcraft
• FreedomIntelligence/OpenClaw-Medical-Skillsskills/bindcraft
• BioTender-max/awesome-bio-agent-skillsskills/adaptyv/bindcraft