New: 97,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Binder Design

by adaptyvbio in adaptyvbio/protein-design-skills

Guidance for choosing the right protein binder design tool. An agent skill from adaptyvbio/protein-design-skills.

MITAuto-check passedResearch & Science

Install Binder Design

skills CLI
$ npx skills add adaptyvbio/protein-design-skills --skill binder-design -a claude-code

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

GitHub CLI
$ gh skill install adaptyvbio/protein-design-skills binder-design --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/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/binder-design .claude/skills/binder-design && 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/

Facts

Skill name
binder-design
GitHub stars
163
Used in
3 other repos
Token cost
~1.8k tokens
SKILL.md length
697 words
Files
2 (incl. references)
Skills in repo
24
Repo updated
First seen
Licence
MIT

At a glance

Guidance for choosing the right protein binder design tool. An agent skill from adaptyvbio/protein-design-skills.

  • Works in 6 steps: Target preparation → Hotspot selection → Design with BoltzGen → …
  • Deciding between BoltzGen
  • SKILL.md covers Which tool wins, Tool comparison, Compute cost per design and Compute vs effort tradeoff, plus 4 more sections
  • Calls modal and python

What it does

Binder Design is an agent skill from adaptyvbio/protein-design-skills. Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/tool-comparison.md`).

It sits in Research & Science, covering Protein structure and design. The repository describes itself as: Claude Code skills for protein design. The licence is MIT.

When your agent uses it

  • Deciding between BoltzGen
  • Planning a binder design campaign
  • Understanding trade-offs between different approaches
  • Selecting tools for specific target types

Example prompts

  • “/binder-design”

Requirements

  • Python 3

Workflow steps

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

  1. Target preparation
  2. Hotspot selection
  3. Design with BoltzGen
  4. Alternative: RFdiffusion Pipeline
  5. Validation
  6. Filtering

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
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Binder Design loads about 1.8k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 100 tokens; SKILL.md has 697 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~100
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.7k

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 adaptyvbio/protein-design-skills at commit 59dd633, republished under its MIT licence (© adaptyvbio). 697 words, ~1,819 tokens.

Download SKILL.mdSave it as .claude/skills/binder-design/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
binder-design
description
Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
license
MIT
category
orchestration
tags
guidance, tool-selection, workflow

Binder Design Tool Selection

Which tool wins

No single tool is best for every target. Hit-rate is strongly target-dependent, so choose by target type, what you want to control, and available compute.

The clearest signal comes from head-to-head competitions where many methods design against the same target. On the Adaptyv Nipah de novo target, the public results show:

MethodTestedBindersHit-rate
Mosaic (gradient, multi-model)9889%
ProteinMPNN hybrid28725%
RFdiffusion601322%
BindCraft9877%
BoltzGen18263%

Mosaic had the highest hit-rate here, but on a small, expert-tuned sample. The ranking shifts on other targets, and that target-dependence is true of every method (BoltzGen, Boltz, BindCraft, Mosaic). You cannot know a priori which will win on a new target, so this is not a fixed leaderboard.

Because of that, choose a starting point by cost and effort to a binder, not by assuming a method has the best hit-rate. BoltzGen is the suggested default because it is turnkey and all-atom, so it gets you testable designs fastest with the least setup. Mosaic is the high-ceiling option when you can invest time tuning the objective. On a hard or important target, running more than one method in parallel is reasonable.

De novo binder design?
│
├─ Lowest cost/effort to testable designs → BoltzGen (default)
├─ Hard/important target, can invest tuning → Mosaic (gradient, multi-model)
├─ Ligand / small-molecule binding → BoltzGen (all-atom)
├─ Diversity / exploration → RFdiffusion + ProteinMPNN
├─ End-to-end with built-in validation → BindCraft
└─ Antibody / nanobody (VHH) → germinal skill (also mber, iggm in biomodals)

Tool comparison

ToolStrengthsWeaknessesBest for
BoltzGenAll-atom, single-step, turnkeyOne model in the loop; mid-range cost per designLowest-effort default, ligand binding
MosaicComposable multi-model objective, won hard head-to-headsNeeds tuning, local JAX onlyHard or important targets, expert use
BindCraftEnd-to-end, built-in AF2 validationLess diverseProduction campaigns
RFdiffusionHigh diversityRequires ProteinMPNN; not in biomodalsExploration, diversity
GerminalAntibody and nanobody formatsFinickyscFv / VHH design

Compute cost per design

Adaptyv's own tests of these models showed the following compute cost per accepted design, averaged across 7 targets (it varies several-fold by target):

MethodCost per design
RSO~$0.15
RFdiffusion~$0.25
Mosaic~$0.55
ESMFold2 inversion~$0.85
mBER~$1.40
Germinal~$1.60
BoltzGen~$1.80
BindCraft~$2.90

Per-design compute cost is not the same as cost to a binder, which also depends on the hit-rate on your target. The gradient methods (RSO, Mosaic) are cheap per design but need setup and tuning; BoltzGen and BindCraft cost more per design but are turnkey, so their advantage is low human effort rather than lowest compute cost.

Show full SKILL.md (315 more words)Show less

Compute vs effort tradeoff

  • Lowest human effort: BoltzGen needs no tuning and runs through biomodals. Good first pass and good for ligand binding.
  • Highest ceiling on a hard target: Mosaic, given time to design and tune the objective. It runs locally on a JAX GPU rather than through biomodals, and is cheap per design.
  • Whatever the generator, validate with boltz or chai and rank with ipsae.

Other biomodals-backed options: modal_rso.py (Rejection Sampling Optimization, an AlphaFold-based gradient method) for minibinders, and modal_mber.py for VHH nanobodies.

Example pipeline: BoltzGen → Chai → QC

BoltzGen provides all-atom design with built-in side-chain packing. This is one turnkey path; swap in Mosaic, RFdiffusion, or BindCraft depending on the target.

Target → BoltzGen → Validate → Filter
 (pdb)  (all-atom)   (chai)     (qc)
1. Target preparation
bash
# Fetch structure from PDB
# Use pdb skill for guidance
  • Trim to binding region + 10A buffer
  • Remove waters and ligands
  • Renumber chains if needed
2. Hotspot selection
  • Choose 3-6 exposed residues
  • Prefer charged/aromatic residues
  • Cluster spatially (within 10-15A)
3. Design with BoltzGen

First, create a YAML config file (e.g., binder.yaml):

yaml
entities:
  - protein:
      id: B
      sequence: 70..100

  - file:
      path: target.cif
      include:
        - chain:
            id: A
      binding_types:
        - chain:
            id: A
            binding: 45,67,89

Then run:

bash
modal run modal_boltzgen.py \
  --input-yaml binder.yaml \
  --protocol protein-anything \
  --num-designs 50

Why BoltzGen?

  • All-atom output (no separate ProteinMPNN step needed)
  • Better for ligand/small molecule binding
  • Single-step design (backbone + sequence + side chains)
4. Alternative: RFdiffusion Pipeline

For maximum diversity or when backbone-only is preferred:

bash
# Step 1: Backbone generation (RFdiffusion, run from the official repo)
python run_inference.py \
  inference.input_pdb=target.pdb \
  contigmap.contigs=[A1-150/0 70-100] \
  ppi.hotspot_res=[A45,A67,A89] \
  inference.num_designs=500

# Step 2: Sequence design
modal run modal_ligandmpnn.py \
  --input-pdb backbone.pdb \
  --params-str "--number_of_batches 16 --temperature 0.1"
5. Validation
bash
modal run modal_chai1.py \
  --input-faa sequences.fasta \
  --out-dir predictions/
6. Filtering

Apply standard thresholds:

  • pLDDT > 0.80
  • ipTM > 0.50
  • PAE_interface < 10
  • scRMSD < 2.0 A

See protein-qc skill for details.

Number of designs

StageCountPurpose
Backbone generation500-1000Diversity
Sequences per backbone8-16Sequence space
AF2 predictionsAllValidation
After filtering50-200Candidates
Experimental testing10-50Final selection

Common mistakes

Wrong hotspots
  • Using buried residues
  • Too many hotspots (over-constrain)
  • Wrong chain/residue numbers
Insufficient diversity
  • Too few designs generated
  • Low temperature in ProteinMPNN
  • Not exploring multiple backbones
Poor target preparation
  • Including full protein instead of binding region
  • Missing important structural features
  • Wrong protonation states

Timeline guide

StepCompute Time
RFdiffusion (500 designs)2-4 hours
ProteinMPNN (8000 sequences)1-2 hours
AF2 prediction (8000 sequences)12-24 hours
Filtering and analysis1-2 hours

Total: 1-2 days of compute

© 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/binder-design of adaptyvbio/protein-design-skills.

  • SKILL.md
  • references/tool-comparison.md

Open the folder on GitHubat commit 59dd633

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in adaptyvbio/protein-design-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Binder Design 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.

Binder Design compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Binder Design this skilladaptyvbio/protein-design-skills1633 repos~1.8kAutomated safety check: PassMIT
Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills3.2k2 repos~1.2kAutomated safety check: PassApache-2.0
Pymol VisualizationChatMol/ChatMol372—~1.2kAutomated safety check: PassMIT
Complexa Binder DesignNVIDIA-BioNeMo/bionemo-agent-toolkit478—~3.1kAutomated safety check: NotesApache-2.0
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Biopipelineslocbp-uzh/biopipelines109—~2.4kAutomated safety check: PassMIT

Similar skills

  • Alphafold Database Fetch And Analyze

    google-deepmind/science-skills

    Retrieve and analyze AlphaFold predicted structures for a protein.

    3.2k GitHub starsUsed in 2 repos~1.2k tokens
    Research & ScienceAuto-check passed

  • Pymol Visualization

    ChatMol/ChatMol

    Generate publication-quality molecular visualization images using PyMOL.

    372 GitHub stars~1.2k tokensUpdated 6 mo ago
    Research & ScienceAuto-check passed

  • Complexa Binder Design

    NVIDIA-BioNeMo/bionemo-agent-toolkit

    Run a complete protein binder design campaign with NVIDIA Proteina-Complexa: resolve a target structure and hotspots from a name/sequence/PDB, co-design binder sequence+structure with reward-guided…

    478 GitHub stars~3.1k tokensUpdated today
    Research & ScienceAuto-check: notes

  • DiffDock Molecular Docking

    K-Dense-AI/scientific-agent-skills

    Predicts how small molecules bind to a protein with DiffDock, covering batch docking, pose ranking by confidence and checks on the results; not for binding affinity.

    48k GitHub starsUsed in 1 repo~3k tokens
    Research & ScienceAuto-check: notes

  • Biopipelines

    locbp-uzh/biopipelines

    Design and run computational protein and ligand workflows on a GPU: binder and enzyme design, de novo backbone generation, inverse folding and sequence redesign, structure prediction, protein-ligand…

    109 GitHub stars~2.4k tokensUpdated 7 days ago
    Research & ScienceAuto-check passed

  • Protein Binder Design

    NVIDIA-BioNeMo/bionemo-agent-toolkit

    Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills.

    478 GitHub stars~1.4k tokensUpdated today
    Research & ScienceAuto-check: notes

More from adaptyvbio/protein-design-skills

All 24 skills in this repo

  • Alphafold

    adaptyvbio/protein-design-skills

    Validate protein designs using AlphaFold2 structure prediction.

    163 GitHub starsUsed in 4 repos~1.2k tokens
    Auto-check passed

  • Bindcraft

    adaptyvbio/protein-design-skills

    End-to-end binder design using BindCraft hallucination. An agent skill from adaptyvbio/protein-design-skills.

    163 GitHub starsUsed in 4 repos~1.3k tokens
    Auto-check passed

  • Boltzgen

    adaptyvbio/protein-design-skills

    All-atom protein design using BoltzGen diffusion model. An agent skill from adaptyvbio/protein-design-skills.

    163 GitHub starsUsed in 4 repos~2k tokens
    Auto-check passed

  • Chai

    adaptyvbio/protein-design-skills

    Structure prediction using Chai-1, a foundation model for molecular structure.

    163 GitHub starsUsed in 4 repos~1.5k tokens
    Auto-check passed

  • Protein Design Workflow

    adaptyvbio/protein-design-skills

    End-to-end guidance for protein design pipelines. An agent skill from adaptyvbio/protein-design-skills.

    163 GitHub starsUsed in 4 repos~1.2k tokens
    Auto-check passed

  • Protein Qc

    adaptyvbio/protein-design-skills

    Quality control metrics and filtering thresholds for protein design.

    163 GitHub starsUsed in 4 repos~3.2k tokens
    Auto-check passed

Categories

Questions about Binder Design

What does Binder Design do?

Guidance for choosing the right protein binder design tool. An agent skill from adaptyvbio/protein-design-skills. Binder Design is an agent skill from adaptyvbio/protein-design-skills. Guidance for choosing the right protein binder design tool.

When should I use Binder Design?

Binder Design fits situations like: deciding between BoltzGen; planning a binder design campaign; understanding trade-offs between different approaches; selecting tools for specific target types.

How do I install Binder Design in Claude Code?

Run `npx skills add adaptyvbio/protein-design-skills --skill binder-design -a claude-code`. Or copy the skill folder (skills/binder-design in adaptyvbio/protein-design-skills) into .claude/skills/binder-design in your project. Claude Code loads it when a task matches its description.

How do I install Binder Design in Codex?

Run `npx skills add adaptyvbio/protein-design-skills --skill binder-design -a codex`. Or copy the skill folder (skills/binder-design in adaptyvbio/protein-design-skills) into .agents/skills/binder-design in your project. Codex loads it when a task matches its description.

Can I use Binder Design 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 adaptyvbio/protein-design-skills --skill binder-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/binder-design, .gemini/skills/binder-design, .github/skills/binder-design and .opencode/skills/binder-design in your project.

What does Binder Design need to run?

Going by SKILL.md and its folder, Binder Design needs the command-line tools its instructions call (modal and python). Our summary lists: Python 3.

Does Binder Design access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Binder Design 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 Binder Design use?

Binder Design is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Binder Design use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 923 tokens, read only when the agent opens those files.

What are the alternatives to Binder Design?

Skills that share tags, products or a category with Binder Design: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Pymol Visualization (ChatMol/ChatMol, 372 stars), Complexa Binder Design (NVIDIA-BioNeMo/bionemo-agent-toolkit, 478 stars) and DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Binder Design?

adaptyvbio (a GitHub organization) maintains it in adaptyvbio/protein-design-skills, which has 163 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on June 11, 2026.

Source: adaptyvbio/protein-design-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.