binder-design-tool-selection

binder-design-tool-selection is a skill for Claude Code, Codex from zongtingwei/Bioclaw_Skills_Hub. It costs 91 tokens per session (1,476 once invoked), scanned A, original, MIT.

A guide for choosing among protein-binder design tools such as BoltzGen, BindCraft, and RFdiffusion. A protein binder is a designed protein intended to attach to a target.

In plain words
What is it for?
Planning binder-design campaigns and selecting a tool based on the target type, desired diversity, validation needs, and available computing resources.
Why use it?
It explains which approach fits goals such as ligand binding, structural variety, built-in checking, or antibody and nanobody design.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Planning binder-design campaigns and selecting a tool based on the target type, desired diversity, validation needs, and available computing resources.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zongtingwei/bioclaw_skills_hub/binder-design-tool-selection
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add zongtingwei/Bioclaw_Skills_Hub --skill binder-design-tool-selection
Clone the repo
git clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_Hub

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.

agentmods badge for binder-design-tool-selection

README.md
[![agentmods](https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/binder-design-tool-selection/github.svg)](https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/binder-design-tool-selection)
Your own site
<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/binder-design-tool-selection"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/binder-design-tool-selection/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.

agentmods 80×15 button for binder-design-tool-selection

Your own site · 80×15
<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/binder-design-tool-selection"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/binder-design-tool-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,476 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.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00091 $0.01476
Opus 5 $0.00046 $0.00738
Sonnet 5 $0.00018 $0.00295
Haiku 4.5 $0.00009 $0.00148

Measured 13d ago against content hash c11ae8f15623, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

binder-design-tool-selection 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 13d 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/protein-design/skills/binder-design-tool-selection/SKILL.md · 207 lines

How it starts

The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Binder Design Tool Selection

Plain-language role: Use this skill to choose the right binder-design-tool-selection method, not to run the design model itself.

Decision tree

De novo binder design?
│
├─ Standard target → BoltzGen (recommended)
│   All-atom output (no separate ProteinMPNN step needed)
│   Better for ligand/small molecule binding
│   Single-step design (backbone + sequence + side chains)
│
├─ Need diversity/exploration → RFdiffusion + ProteinMPNN
│   Maximum backbone diversity
│   Two-step: backbone then sequence
│
├─ Integrated validation → BindCraft
│   Built-in AF2 validation
│   End-to-end pipeline
│
├─ Ligand binding → BoltzGen ✓
│   All-atom diffusion handles ligand context
│
├─ Peptide/nanobody → Germinal
│   VHH/nanobody design
│   Germline-aware optimization
│
└─ Antibody/Nanobody
    +-- VHH design --> germinal skill

Tool comparison

Tool Strengths Weaknesses Best For
BoltzGen All-atom, single-step, ligand-aware Higher GPU requirement Standard (recommended)
BindCraft End-to-end, built-in AF2 validation Less diverse Production campaigns
RFdiffusion High diversity, fast Requires ProteinMPNN Exploration, diversity
Germinal Nanobody/VHH design Specialized Antibody optimization

BoltzGen provides all-atom design with built-in side-chain packing:

Target → BoltzGen → Validate → Filter
 (pdb)  (all-atom)   (chai1-structure-prediction)     (qc)

1. Target preparation

# 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 (Recommended)

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

Read the full file on GitHub · 207 lines

Files

What ships with it

2 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.

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.

  1. 13d ago First seen · 207 lines · 91 tokens per session scan A c11ae8f15623

Subscribe to this mod's changes

binder-design-tool-selection is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (26 stars, last pushed 5mo ago), licensed MIT. It adds 91 tokens to every session and 1,476 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.

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