Agent skill

Protein Binder Design

by NVIDIA-BioNeMo in 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.

Apache-2.0Auto-check: notesResearch & Science

Install Protein Binder Design

skills CLI

$ npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill protein-binder-design -a claude-code

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

GitHub CLI

$ gh skill install NVIDIA-BioNeMo/bionemo-agent-toolkit protein-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/NVIDIA-BioNeMo/bionemo-agent-toolkit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bionemo-agent-toolkit/skills/protein-binder-design .claude/skills/protein-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/

Paste into Claude Code

Install the "protein-binder-design" agent skill from https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/skills/bionemo-agent-toolkit/skills/protein-binder-design into .claude/skills/protein-binder-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-binder-design", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/skills/bionemo-agent-toolkit/skills/protein-binder-design

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill protein-binder-design -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA-BioNeMo/bionemo-agent-toolkit protein-binder-design --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/bionemo-agent-toolkit/skills/protein-binder-design .agents/skills/protein-binder-design && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "protein-binder-design" agent skill from https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/skills/bionemo-agent-toolkit/skills/protein-binder-design into .agents/skills/protein-binder-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-binder-design", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill protein-binder-design -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA-BioNeMo/bionemo-agent-toolkit protein-binder-design --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/bionemo-agent-toolkit/skills/protein-binder-design .cursor/skills/protein-binder-design && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "protein-binder-design" agent skill from https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/skills/bionemo-agent-toolkit/skills/protein-binder-design into .cursor/skills/protein-binder-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-binder-design", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git --path skills/bionemo-agent-toolkit/skills/protein-binder-design

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill protein-binder-design -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA-BioNeMo/bionemo-agent-toolkit protein-binder-design --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/bionemo-agent-toolkit/skills/protein-binder-design .gemini/skills/protein-binder-design && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "protein-binder-design" agent skill from https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/skills/bionemo-agent-toolkit/skills/protein-binder-design into .gemini/skills/protein-binder-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-binder-design", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA-BioNeMo/bionemo-agent-toolkit protein-binder-design

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill protein-binder-design -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/bionemo-agent-toolkit/skills/protein-binder-design .github/skills/protein-binder-design && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "protein-binder-design" agent skill from https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/skills/bionemo-agent-toolkit/skills/protein-binder-design into .github/skills/protein-binder-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-binder-design", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill protein-binder-design -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA-BioNeMo/bionemo-agent-toolkit protein-binder-design --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/bionemo-agent-toolkit/skills/protein-binder-design .opencode/skills/protein-binder-design && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "protein-binder-design" agent skill from https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/skills/bionemo-agent-toolkit/skills/protein-binder-design into .opencode/skills/protein-binder-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "protein-binder-design", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: protein-binder-design
GitHub stars: 479
Token cost: ~1.4k tokens
SKILL.md length: 606 words
Files: 15 (incl. scripts, references, assets)
Skills in repo: 14
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

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

Works in 6 steps: Target prep — get the target PDB +… → Backbones (rfdiffusion-nim) — binder… → Sequences (proteinmpnn-nim) — k… → …
Minibinder design
SKILL.md covers Composed skills, Pipeline, Handoff contracts (the fragile… and Run manifest (reproducibility…, plus 6 more sections
Runs Python scripts from its folder; reaches health.api.nvidia.com; needs NVIDIA_API_KEY

What it does

Protein Binder Design is an agent skill from 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. Use for binder design, minibinder design, de novo binders, RFdiffusion + ProteinMPNN + Boltz2/OpenFold3 pipelines, epitope/hotspot-targeted design, in-silico binder validation, and ranking designs by interface confidence.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including scripts, reference files and assets (for example `README.md`, `assets/targets.json` and `evals/evals.json`). Compatibility notes: numpy=1.24; requests=2.28

It sits in Research & Science, covering Protein structure and design. The repository describes itself as: Turn any agent into a life science expert with NVIDIA BioNeMo skills. The licence is Apache-2.0.

When your agent uses it

Minibinder design
De novo binders
RFdiffusion + ProteinMPNN + Boltz2/OpenFold3 pipelines
Epitope/hotspot-targeted design

Example prompts

“/protein-binder-design”

Requirements

Python 3
Docker
A credential in NVIDIA_API_KEY
Compatibility (from SKILL.md): numpy>=1.24; requests>=2.28
Pre-approved tools (allowed-tools): Bash, Read, Write, AskUserQuestion

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

Target prep — get the target PDB + epitope; map epitope/hotspot author
Backbones (rfdiffusion-nim) — binder contig + hotspot_res; N backbones.
Sequences (proteinmpnn-nim) — k sequences per backbone; drop the
Co-fold + score (boltz2-nim / openfold3-nim) — co-fold binder+target;
Self-consistency — CA-RMSD between the RFdiffusion backbone and the
Filter + rank — apply thresholds; rank survivors; write manifest + CSV.

What it can do on your machine

Read from SKILL.md and the folder at commit 4f1b6a4. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- Bash
- Read
- Write
- AskUserQuestion
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 5 files in scripts/ (Python), which the agent can run.
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:
- health.api.nvidia.com
Also links to:
- build.nvidia.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- NVIDIA_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
numpy>=1.24; requests>=2.28
From compatibility in the SKILL.md frontmatter.

Context cost

Protein Binder Design loads about 1.4k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 606 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~92
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~5.2k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
```
allowed-tools: Bash, Read, Write, AskUserQuestion
```

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); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA-BioNeMo/bionemo-agent-toolkit at commit 4f1b6a4, republished under its Apache-2.0 licence (© NVIDIA-BioNeMo). 606 words, ~1,435 tokens.

Download SKILL.mdSave it as .claude/skills/protein-binder-design/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.

name: protein-binder-design
description: Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills. Use for binder design, minibinder design, de novo binders, RFdiffusion + ProteinMPNN + Boltz2/OpenFold3 pipelines, epitope/hotspot-targeted design, in-silico binder validation, and ranking designs by interface confidence.
allowed-tools: Bash, Read, Write, AskUserQuestion
compatibility: numpy>=1.24; requests>=2.28
license: Apache-2.0

Protein Binder Design (workflow)

Run a de novo binder design campaign by composing atomic NIM skills. This skill owns orchestration, handoff contracts, filtering, validation, and the run manifest. It does NOT duplicate per-NIM API details — defer those to each atomic skill's SKILL.md.

Composed skills

Step	Skill	Owns
Backbones	`rfdiffusion-nim`	binder backbone PDBs (contigs + hotspots)
Sequences	`proteinmpnn-nim`	sequences for each backbone
Co-fold / score	`boltz2-nim` or `openfold3-nim`	binder–target complex + confidence / ipTM
MSA (optional)	`msa-search-nim`	target A3M for higher-quality folding

The atomic NIM skills are recommended companions (one per NIM, from the BioNeMo NIM skill set). They are not required: references/pipeline.md carries the concrete request shape for every NIM call, so an agent with NIM access can follow this skill standalone. For endpoints/auth see Configuration below.

Pipeline

Target prep — get the target PDB + epitope; map epitope/hotspot author residue numbers to RFdiffusion hotspot_res strings; optionally build a target MSA with msa-search-nim.
Backbones (rfdiffusion-nim) — binder contig + hotspot_res; N backbones.
Sequences (proteinmpnn-nim) — k sequences per backbone; drop the native/WT row from mfasta.
Co-fold + score (boltz2-nim / openfold3-nim) — co-fold binder+target; collect interface confidence (ipTM) and binder pLDDT.
Self-consistency — CA-RMSD between the RFdiffusion backbone and the predicted binder (scripts/metrics.py).
Filter + rank — apply thresholds; rank survivors; write manifest + CSV.

Full handoff contracts, branching, and the cost funnel: references/pipeline.md.

Handoff contracts (the fragile glue)

RFdiffusion output_pdb → ProteinMPNN input_pdb (inline PDB text).
ProteinMPNN mfasta → Boltz2 binder polymer sequence (exclude the native/WT row; pair scores only with designed rows).
Epitope author residue numbers → 1-based sequence indices: remap with scripts/pdb_utils.py:remap_to_seq_index. RFdiffusion hotspot_res uses chain+author strings like "A50"; Boltz2 pocket/contacts use 1-based indices.
Boltz2 complex .cif → binder chain → self-consistency RMSD vs the backbone.

Run manifest (reproducibility backbone)

Every campaign writes manifest.json (+ candidates.csv) under a run dir via scripts/manifest.py. It records lineage, params, scores, artifacts, filter status, and controls — enabling ranking, resumability, validation, and the final report. Schema and usage: references/manifest.md.

Filters (defaults)

ipTM ≥ 0.8, binder pLDDT ≥ 80, self-consistency RMSD ≤ 2.0 Å.
Override per campaign and record overrides in the manifest filters.

Validation

Always run controls and report a success rate, not just top scores. Negative controls via scripts/controls.py (scrambled sequences); positive controls = published binders re-scored through the same pipeline. Benchmark targets live in assets/targets.json (scripts/registry.py). Methodology and metric definitions: references/validation.md.

Show full SKILL.md (235 more words)Show less

Human-in-the-loop + cost

Confirm target, epitope/hotspots, binder length range, and hosted-vs-local with the user before generating backbones (AskUserQuestion).
Co-folding is the expensive stage: co-fold a capped shortlist, review, then expand. State hosted vs local once and reuse it across all NIM calls.

Responsible use

De novo binder design is dual-use. Decline requests aimed at enhancing pathogen fitness, toxin potency, or bioweapon function; keep designs to legitimate research and therapeutic intent.

Configuration (NIM access)

Each composed NIM is reached over HTTP; choose hosted or local once and reuse it for every call:

Hosted (managed): base URL https://health.api.nvidia.com/v1/... per NIM at build.nvidia.com; set NVIDIA_API_KEY (sent as Authorization: Bearer). Read keys from the env — never hardcode them.
Local (self-hosted NGC containers): point each NIM at its local URL (e.g. http://localhost:8000/...); local NIMs need no auth header. To launch the NIMs yourself (docker run per NIM, persistent caches, health checks, and the GPU profile‑selection gotcha — some NIMs (e.g. Boltz2) need NIM_MODEL_PROFILE pinned on GPUs that have no bundled profile, while others (RFdiffusion/ProteinMPNN) auto‑select by compute capability): see references/local-nim-setup.md.

Per-NIM paths, request/response schemas, and worked curl/Python examples live in references/pipeline.md.

Scripts

scripts/manifest.py — campaign manifest (create / load / score / filter / rank / CSV).
scripts/pdb_utils.py — PDB parse, chain extract, sequence, residue remap, CA coords.
scripts/metrics.py — Kabsch CA-RMSD for self-consistency.
scripts/controls.py — scrambled negative controls.
scripts/registry.py + assets/targets.json — example benchmark target registry (illustrative epitopes — verify against the cited structure before a real campaign). Replace with your own targets.

Files

SKILL.md and 14 other files (scripts, references, assets) in skills/bionemo-agent-toolkit/skills/protein-binder-design of NVIDIA-BioNeMo/bionemo-agent-toolkit.

SKILL.md
LICENSE
README.md
assets/targets.json
evals/evals.json
evals/trigger_evals.json
references/local-nim-setup.md
references/manifest.md
references/pipeline.md
references/validation.md
scripts/controls.py
scripts/manifest.py
scripts/metrics.py
scripts/pdb_utils.py
scripts/registry.py

Open the folder on GitHubat commit 4f1b6a4

Compare with similar skills

Protein 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.

Protein Binder Design compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Protein Binder Design this skillNVIDIA-BioNeMo/bionemo-agent-toolkit	479	—	~1.4k	Automated safety check: Notes	Apache-2.0	yesterday
Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills	3.2k	2 repos	~1.2k	Automated safety check: Pass	Apache-2.0	today
Alphafoldadaptyvbio/protein-design-skills	164	3 repos	~1.2k	Automated safety check: Pass	MIT	4 mo ago
Pymol VisualizationChatMol/ChatMol	373	—	~1.2k	Automated safety check: Pass	MIT	6 mo ago
Bindcraftadaptyvbio/protein-design-skills	164	3 repos	~1.3k	Automated safety check: Pass	MIT	4 mo ago
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills	48k	1 repos	~3k	Automated safety check: Notes	MIT	5 days ago

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Questions about Protein Binder Design

What does Protein Binder Design do?

Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills. Protein Binder Design is an agent skill from 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.

When should I use Protein Binder Design?

Protein Binder Design fits situations like: minibinder design; de novo binders; RFdiffusion + ProteinMPNN + Boltz2/OpenFold3 pipelines; epitope/hotspot-targeted design.

How do I install Protein Binder Design in Claude Code?

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

How do I install Protein Binder Design in Codex?

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

Can I use Protein 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 NVIDIA-BioNeMo/bionemo-agent-toolkit --skill protein-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/protein-binder-design, .gemini/skills/protein-binder-design, .github/skills/protein-binder-design and .opencode/skills/protein-binder-design in your project.

What does Protein Binder Design need to run?

Going by SKILL.md and its folder, Protein Binder Design needs Python for the scripts in its folder and credentials named NVIDIA_API_KEY. Our summary lists: Python 3; Docker; A credential in NVIDIA_API_KEY. Its frontmatter pre-approves these tools: Bash, Read, Write, AskUserQuestion. Compatibility (from SKILL.md): numpy>=1.24; requests>=2.28.

Does Protein Binder Design access the network?

SKILL.md names 2 domains. In commands or code: health.api.nvidia.com; the agent is likely to contact it when it follows the instructions. As links in the text: build.nvidia.com. This is read from the text; nothing was executed.

Is Protein Binder Design safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Protein Binder Design use?

Protein Binder Design is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Protein Binder Design use?

About 1.4k tokens (SKILL.md is roughly 5.7k 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 3.7k tokens, read only when the agent opens those files.

What are the alternatives to Protein Binder Design?

Skills that share tags, products or a category with Protein Binder Design: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Alphafold (adaptyvbio/protein-design-skills, 164 stars), Pymol Visualization (ChatMol/ChatMol, 373 stars) and Bindcraft (adaptyvbio/protein-design-skills, 164 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Protein Binder Design?

NVIDIA-BioNeMo (a GitHub organization) maintains it in NVIDIA-BioNeMo/bionemo-agent-toolkit, which has 479 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA-BioNeMo/bionemo-agent-toolkit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Protein Binder Design

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Compatibility

Context cost

Safety

SKILL.md

Protein Binder Design (workflow)

Composed skills

Pipeline

Handoff contracts (the fragile glue)

Run manifest (reproducibility backbone)

Filters (defaults)

Validation

Human-in-the-loop + cost

Responsible use

Configuration (NIM access)

Scripts

Files

Compare with similar skills

Similar skills

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