Agent skill

Ligandmpnn

by adaptyvbio in adaptyvbio/protein-design-skills

Ligand-aware protein sequence design using LigandMPNN. An agent skill from adaptyvbio/protein-design-skills.

Install Ligandmpnn

skills CLI

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

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

GitHub CLI

$ gh skill install adaptyvbio/protein-design-skills ligandmpnn --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/ligandmpnn .claude/skills/ligandmpnn && 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 "ligandmpnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ligandmpnn into .claude/skills/ligandmpnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ligandmpnn", 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/adaptyvbio/protein-design-skills/tree/main/skills/ligandmpnn

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 adaptyvbio/protein-design-skills --skill ligandmpnn -a codex

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

GitHub CLI

$ gh skill install adaptyvbio/protein-design-skills ligandmpnn --agent codex

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

Manual copy

$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ligandmpnn .agents/skills/ligandmpnn && 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 "ligandmpnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ligandmpnn into .agents/skills/ligandmpnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ligandmpnn", 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 adaptyvbio/protein-design-skills --skill ligandmpnn -a cursor

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

GitHub CLI

$ gh skill install adaptyvbio/protein-design-skills ligandmpnn --agent cursor

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

Manual copy

$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ligandmpnn .cursor/skills/ligandmpnn && 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 "ligandmpnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ligandmpnn into .cursor/skills/ligandmpnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ligandmpnn", 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/adaptyvbio/protein-design-skills.git --path skills/ligandmpnn

--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 adaptyvbio/protein-design-skills --skill ligandmpnn -a gemini-cli

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

GitHub CLI

$ gh skill install adaptyvbio/protein-design-skills ligandmpnn --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/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ligandmpnn .gemini/skills/ligandmpnn && 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 "ligandmpnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ligandmpnn into .gemini/skills/ligandmpnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ligandmpnn", 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 adaptyvbio/protein-design-skills ligandmpnn

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 adaptyvbio/protein-design-skills --skill ligandmpnn -a github-copilot

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

Manual copy

$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ligandmpnn .github/skills/ligandmpnn && 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 "ligandmpnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ligandmpnn into .github/skills/ligandmpnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ligandmpnn", 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 adaptyvbio/protein-design-skills --skill ligandmpnn -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 adaptyvbio/protein-design-skills ligandmpnn --agent opencode

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

Manual copy

$ git clone --depth 1 https://github.com/adaptyvbio/protein-design-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ligandmpnn .opencode/skills/ligandmpnn && 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 "ligandmpnn" agent skill from https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ligandmpnn into .opencode/skills/ligandmpnn/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ligandmpnn", 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: ligandmpnn
GitHub stars: 164
Used in: 3 other repos
Token cost: ~1.2k tokens
SKILL.md length: 252 words
Files: 1
Skills in repo: 24
Repo updated: Jun 11, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Ligand-aware protein sequence design using LigandMPNN. An agent skill from adaptyvbio/protein-design-skills.

Designing sequences around small molecules
SKILL.md covers Prerequisites, How to run, Key parameters (LigandMPNN… and Ligand Specification, plus 6 more sections
Calls python, modal and git; reaches github.com
Enzyme active site design

What it does

Ligandmpnn is an agent skill from adaptyvbio/protein-design-skills. Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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

Designing sequences around small molecules
Enzyme active site design
Ligand binding pocket optimization
Metal coordination site design

Example prompts

“/ligandmpnn”

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:
- python
- modal
- git
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

Ligandmpnn loads about 1.2k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 252 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~91
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k

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 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). 252 words, ~1,185 tokens.

Download SKILL.mdSave it as .claude/skills/ligandmpnn/SKILL.md (or your agent's skills folder).

name: ligandmpnn
description: Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.
license: MIT
category: design-tools
tags: sequence-design, inverse-folding, ligand-aware
biomodals_script: modal_ligandmpnn.py

LigandMPNN Ligand-Aware Design

Prerequisites

Requirement	Minimum	Recommended
Python	3.8+	3.10
CUDA	11.0+	11.7+
GPU VRAM	8GB	16GB (T4)
RAM	8GB	16GB

How to run

First time? See Getting started to set up Modal and biomodals.

bash

cd biomodals
# modal_ligandmpnn.py takes --input-pdb; LigandMPNN run.py args go in --params-str
modal run modal_ligandmpnn.py \
  --input-pdb protein_ligand.pdb \
  --params-str "--model_type ligand_mpnn --number_of_batches 16 --temperature 0.1"

GPU: A10G default | Timeout: 900s default

Option 2: Local installation

bash

git clone https://github.com/dauparas/LigandMPNN.git
cd LigandMPNN

python run.py \
  --model_type ligand_mpnn \
  --pdb_path protein_ligand.pdb \
  --out_folder output/ \
  --number_of_batches 16 \
  --temperature 0.1

Key parameters (LigandMPNN run.py)

Parameter	Default	Description
`--pdb_path`	required	PDB with ligand
`--model_type`	`protein_mpnn`	`ligand_mpnn`, `soluble_mpnn`, etc.
`--temperature`	0.1	Sampling temperature
`--number_of_batches`	1	Batches (sequences = batch_size x batches)
`--batch_size`	1	Sequences per batch
`--ligand_mpnn_use_side_chain_context`	0	Use ligand side-chain context

Ligand Specification

In PDB File

Ligand must be present as HETATM records:

ATOM    ...protein atoms...
HETATM  1  C1  LIG A 999      x.xxx  y.yyy  z.zzz  1.00  0.00           C

Supported Ligand Types

Small molecules (HETATM)
Metals (Zn, Fe, Mg, Ca, etc.)
Cofactors (NAD, FAD, ATP)
DNA/RNA

Output format

output/
├── seqs/
│   └── protein.fa          # FASTA sequences
└── protein_pdb/
    └── protein_0001.pdb    # PDBs with designed sequence

Sample output

Successful run

$ python run.py --pdb_path enzyme_substrate.pdb --out_folder output/ --num_seq_per_target 8
Loading LigandMPNN model weights...
Processing enzyme_substrate.pdb
Found ligand: LIG (12 atoms)
Generated 8 sequences in 3.1 seconds

output/seqs/enzyme_substrate.fa:
>enzyme_substrate_0001, score=1.45, global_score=1.38
MKTAYIAKQRQISFVKSHFSRQLE...
>enzyme_substrate_0002, score=1.52, global_score=1.41
MKTAYIAKQRQISFVKSQFSRQLD...

What good output looks like:

Score: 1.0-2.0 (lower = more confident)
Ligand detected and incorporated in context
Active site residues preserved or optimized

Decision tree

Should I use LigandMPNN?
│
├─ What's in your binding site?
│  ├─ Small molecule / ligand → LigandMPNN ✓
│  ├─ Metal ion (Zn, Fe, etc.) → LigandMPNN ✓
│  ├─ Cofactor (NAD, FAD, ATP) → LigandMPNN ✓
│  ├─ DNA/RNA → LigandMPNN ✓
│  └─ Nothing / protein only → Use ProteinMPNN
│
├─ What type of design?
│  ├─ Enzyme active site → LigandMPNN ✓
│  ├─ Metal binding site → LigandMPNN ✓
│  ├─ Protein-protein binder → Use ProteinMPNN
│  └─ De novo scaffold → Use ProteinMPNN
│
└─ Priority?
   ├─ Solubility/expression → Consider SolubleMPNN
   └─ Ligand context accuracy → LigandMPNN ✓

Typical performance

Campaign Size	Time (T4)	Cost (Modal)	Notes
100 backbones × 8 seq	15-20 min	~$2	Standard
500 backbones × 8 seq	1-1.5h	~$8	Large campaign

Throughput: ~50-100 sequences/minute on T4 GPU.

Verify

bash

grep -c "^>" output/seqs/*.fa  # Should match backbone_count × num_seq_per_target

Troubleshooting

Ligand not recognized: Check HETATM format, verify ligand residue name Poor binding residues: Increase sampling around active site Missing contacts: Verify ligand coordinates in PDB

Error interpretation

Error	Cause	Fix
`RuntimeError: CUDA out of memory`	Long protein or large batch	Reduce batch_size
`KeyError: 'LIG'`	Ligand not found in PDB	Check HETATM records
`ValueError: no ligand atoms`	Empty ligand	Verify ligand has atoms in PDB

Next: Structure prediction for validation → protein-qc for filtering.

Files

Just SKILL.md in skills/ligandmpnn of adaptyvbio/protein-design-skills.

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

Ligandmpnn 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.

Ligandmpnn compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Ligandmpnn this skilladaptyvbio/protein-design-skills	164	3 repos	~1.2k	Automated safety check: Pass	MIT	3 mo ago
Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills	3.2k	2 repos	~1.2k	Automated safety check: Pass	Apache-2.0	24 days ago
Pymol VisualizationChatMol/ChatMol	373	—	~1.2k	Automated safety check: Pass	MIT	6 mo ago
Complexa Binder DesignNVIDIA-BioNeMo/bionemo-agent-toolkit	478	—	~3.1k	Automated safety check: Notes	Apache-2.0	today
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills	48k	1 repos	~3k	Automated safety check: Notes	MIT	4 days ago
Biopipelineslocbp-uzh/biopipelines	109	—	~2.4k	Automated safety check: Pass	MIT	9 days ago

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.
373 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 9 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

Questions about Ligandmpnn

What does Ligandmpnn do?

Ligand-aware protein sequence design using LigandMPNN. An agent skill from adaptyvbio/protein-design-skills. Ligandmpnn is an agent skill from adaptyvbio/protein-design-skills. Ligand-aware protein sequence design using LigandMPNN.

When should I use Ligandmpnn?

Ligandmpnn fits situations like: designing sequences around small molecules; enzyme active site design; ligand binding pocket optimization; metal coordination site design.

How do I install Ligandmpnn in Claude Code?

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

How do I install Ligandmpnn in Codex?

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

Can I use Ligandmpnn 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 ligandmpnn -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ligandmpnn, .gemini/skills/ligandmpnn, .github/skills/ligandmpnn and .opencode/skills/ligandmpnn in your project.

What does Ligandmpnn need to run?

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

Does Ligandmpnn access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Ligandmpnn 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 Ligandmpnn use?

Ligandmpnn 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 Ligandmpnn use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Ligandmpnn?

Skills that share tags, products or a category with Ligandmpnn: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Pymol Visualization (ChatMol/ChatMol, 373 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 Ligandmpnn?

adaptyvbio (a GitHub organization) maintains it in adaptyvbio/protein-design-skills, which has 164 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.

Install Ligandmpnn

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

LigandMPNN Ligand-Aware Design

Prerequisites

How to run

Option 1: Modal (recommended)

Option 2: Local installation

Key parameters (LigandMPNN run.py)

Ligand Specification

In PDB File

Supported Ligand Types

Output format

Sample output

Successful run

Decision tree

Typical performance

Verify

Troubleshooting

Error interpretation

Files

Used in 3 other repositories

Compare with similar skills

Similar skills

More from adaptyvbio/protein-design-skills

Related

Questions about Ligandmpnn