protein-binder-design

protein-binder-design is a skill for Claude Code, Codex from synthetic-sciences/openscience. It costs 35 tokens per session (772 once invoked), scanned A, original, Apache-2.0.

An end-to-end workflow for designing new protein binders with NVIDIA's BioNeMo tools. A protein binder is a designed protein intended to attach to a chosen target protein.

In plain words
What is it for?
Use it to prepare targets, generate binder backbones and sequences, predict and score protein complexes, compare candidates, rank them, and produce structures and a report.
Why use it?
It organizes target preparation, structure and sequence design, independent prediction, scoring, diversity checks, ranking, and reporting into one reproducible campaign. It also records the software versions and adapts when required hosted access is unavailable.

Skill for Claude CodeCodex

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

About the project

synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.

synthetic-sciences/openscience · 3,491 stars · on GitHub · openscience.sh

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.

agentmods
npx agentmods add skills/synthetic-sciences/openscience/protein-binder-design
Any agent
npx skills add synthetic-sciences/openscience --skill protein-binder-design
Clone the repo
git clone --depth 1 https://github.com/synthetic-sciences/openscience

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 protein-binder-design

README.md
[![agentmods](https://agentmods.dev/badge/skills/synthetic-sciences/openscience/protein-binder-design.svg)](https://agentmods.dev/skills/synthetic-sciences/openscience/protein-binder-design)
Your own site
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/protein-binder-design"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/protein-binder-design.svg" alt="Measured on agentmods" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 772 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00035 $0.00772
Opus 5 $0.00017 $0.00386
Sonnet 5 $0.00007 $0.00154
Haiku 4.5 $0.00003 $0.00077

Measured 6d ago against content hash ea1f90c93e8a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

protein-binder-design 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 6d 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.

backend/cli/skills/biology/protein-binder-design/SKILL.md · 42 lines

How it starts

The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Protein binder design

Use this workflow for an end-to-end binder campaign: target preparation, backbone generation, sequence design, independent complex prediction, interface scoring, diversity analysis, ranking, structures, and a reproducible report.

The reviewed upstream source is NVIDIA BioNeMo Agent Toolkit commit 0e67a612e4045f007e38fa77adc8f3ebfc5616b6. Its canonical workflows are skills/bionemo-agent-toolkit/skills/protein-binder-design and skills/bionemo-agent-toolkit/skills/complexa-binder-design. Record that commit and every model/version actually used.

This adapted skill is self-contained; it has no local helper files or references to inspect. Resolve upstream details through the pinned public repository only when the active route needs them.

Start with capabilities

  1. Call compute_job with action: "targets" once. Respect the returned Modal network and credential capabilities.
  2. Use the configured Modal target for detached GPU work. Never call the Modal CLI/SDK directly and never place credentials in commands.
  3. If nvidia_nim is reported available by targets, the supported BioNeMo NIM funnel is RFdiffusion backbones -> ProteinMPNN sequences -> Boltz2 or validated OpenFold3 complexes -> self-consistency and interface ranking. Never submit an unavailable secret reference merely to probe it.
  4. If a reviewed NGC/private-image route is actually available, Proteina-Complexa may co-design sequence and structure before an independent Boltz2/OpenFold3 refold. Do not claim this route when the target reports no private-registry support.
  5. If NVIDIA credentials are absent, do not stop merely because the branded endpoints are unavailable. Inspect the pinned toolkit workflow, then test whether the corresponding public open-source RFdiffusion, ProteinMPNN, and Boltz releases can be installed and run on Modal. Proceed only when exact public sources and weights are accessible under their licenses. Label this clearly as an open-source adaptation of the BioNeMo workflow, not a hosted NIM run.
  6. If neither route can execute, return a precise preflight blocker and the smallest missing setup. Never fabricate structures or scores.

Read the full file on GitHub · 42 lines

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. 6d ago First seen · 42 lines · 35 tokens per session scan A ea1f90c93e8a

Subscribe to this mod's changes

protein-binder-design is a skill published in the GitHub repository synthetic-sciences/openscience (3,491 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 772 once invoked, about $0.0002 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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