Official agent skill

Proteinmpnn Nim

by NVIDIA in NVIDIA/skills

Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone.

OfficialApache-2.0Auto-check: notesResearch & Science

Install Proteinmpnn Nim

skills CLI
$ npx skills add NVIDIA/skills --skill proteinmpnn-nim -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/skills proteinmpnn-nim --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/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bionemo-proteinmpnn-nim .claude/skills/proteinmpnn-nim && 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
proteinmpnn-nim
GitHub stars
3.5k
Used in
1 other repo
Token cost
~2k tokens
SKILL.md length
929 words
Files
25 (incl. scripts, references)
Skills in repo
386
Repo updated
First seen
Licence
Apache-2.0

At a glance

Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone.

  • Works in 4 steps: Use the user's PDB path and requested… → Select --mode hosted or --mode local and… → Choose a new --output-dir for each… → …
  • Sequence design
  • SKILL.md covers Choose Mode, Data Transfer and Authorization, Local Docker and Instructions, plus 3 more sections
  • Runs Python scripts from its folder; calls docker and python; reaches health.api.nvidia.com; needs NGC_API_KEY and NVIDIA_API_KEY

What it does

Proteinmpnn Nim is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Sends user-provided PDB files and design parameters to NVIDIA's hosted API, authenticated with an environment API key, or to a user-selected local NIM. Use for sequence design, backbone redesign, fixed chains and residues, omitAAs, sampling temperature, soluble model, local Docker, and multi-FASTA output.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 32 other files, including scripts and reference files (for example `BENCHMARK.md`, `config/skillspector-baseline.yml` and `evals/README.md`). Compatibility notes: Python =3.10; requests=2.28

It sits in Research & Science, covering Protein structure and design. It works with NVIDIA AI Platform and Docker. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

  • Sequence design
  • Backbone redesign
  • Fixed chains and residues
  • Sampling temperature

Example prompts

  • “/proteinmpnn-nim”

Requirements

  • Python 3
  • Docker
  • A credential in NGC_API_KEY
  • A credential in NVIDIA_API_KEY
  • Compatibility (from SKILL.md): Python >=3.10; requests>=2.28
  • Pre-approved tools (allowed-tools): Bash, Read, Write, AskUserQuestion

Workflow steps

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

  1. Use the user's PDB path and requested sequence count. The client reads the
  2. Select --mode hosted or --mode local and follow **Data Transfer and
  3. Choose a new --output-dir for each request. The client reserves it before
  4. Read summary.json and report the actual results described below. If the

What it can do on your machine

Read from SKILL.md and the folder at commit dfdd080. 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 1 file in scripts/ (Python, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • docker
    • python

    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

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • NGC_API_KEY
    • NVIDIA_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Python >=3.10; requests>=2.28

    From compatibility in the SKILL.md frontmatter.

Context cost

Proteinmpnn Nim loads about 2k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 105 tokens; SKILL.md has 929 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~105
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.3k

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.

  • NoteMentions a .env fileSKILL.md:69
    ython request. For the exact preflight (`.env` sourcing,
  • 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/skills at commit dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 929 words, ~1,998 tokens.

Download SKILL.mdSave it as .claude/skills/proteinmpnn-nim/SKILL.md (or your agent's skills folder). This skill also uses 24 other files; get the full folder from GitHub.
name
proteinmpnn-nim
description
Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Sends user-provided PDB files and design parameters to NVIDIA's hosted API, authenticated with an environment API key, or to a user-selected local NIM. Use for sequence design, backbone redesign, fixed chains and residues, omit_AAs, sampling temperature, soluble model, local Docker, and multi-FASTA output.
allowed-tools
Bash, Read, Write, AskUserQuestion
compatibility
Python >=3.10; requests>=2.28
license
Apache-2.0 AND CC-BY-4.0
permissions
network, env

ProteinMPNN NIM

<!-- nv-carps: dummy edit to trigger NIM skill validation. -->

Design protein sequences for a supplied backbone PDB. Use this guide for first-pass hosted/local usage; load supplemental files only when needed:

  • references/api.md: exact endpoints, schemas, Docker flags, response fields.
  • references/science.md: inverse-folding uses, limits, and validation.
  • references/parameters.md: design controls, fixed positions, sampling.
  • references/validation.md: FASTA, score, and structure checks.
  • references/examples.md: compact hosted/local request patterns.

Choose Mode

Honor the user's explicit mode; otherwise use the configured runtime. NIM_API_MODE=local selects the local service at PROTEINMPNN_NIM_URL; the URL defaults to http://localhost:8000 for a NIM running in the same host or container. Ask only when neither the environment nor the user's request makes the mode clear:

Hosted NVIDIA API or local Docker NIM?

  • Hosted: https://health.api.nvidia.com/v1/biology/ipd/proteinmpnn/predict
  • Local: append /biology/ipd/proteinmpnn/predict to PROTEINMPNN_NIM_URL (default base URL: http://localhost:8000).

Local inference paths do not include /v1/. Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for registry login, entitlement checks, and first-run model downloads; pass it into the container with -e NGC_API_KEY. Local inference requests use no auth header after readiness. Warm-cache key-free startup varies by image/version and should not be assumed.

Data Transfer and Authorization

Before a hosted request, tell the user that the entire PDB file and design parameters will be uploaded to NVIDIA's hosted API at the endpoint above. Proceed if the user has explicitly requested hosted processing of that PDB or already approved the transfer; otherwise ask for confirmation before submitting. For confidential structures, recommend a local NIM in the user's approved environment. A configured local URL may point to another machine; use only the configured or user-selected destination. Do not switch from local to hosted processing without the user's authorization.

The client reads NIM_API_MODE, PROTEINMPNN_NIM_URL, and, for hosted mode, NGC_API_KEY from the environment. It sends the key only in the HTTPS Authorization header to the hosted endpoint; local inference sends no key. Keep credentials out of logs and saved artifacts. The output directory contains the full input PDB in request.json and the returned sequences and scores, so use a location appropriate for the input's sensitivity. See references/api.md for endpoint and data-handling details.

Local Docker

For local setup, run the full sequence — env preflight, docker login, docker run, readiness loop, then the no-auth localhost request; do not answer with only a localhost Python request. For the exact preflight (.env sourcing, NGC_API_KEY/NVIDIA_API_KEY handling, and the docker run for nvcr.io/nim/ipd/proteinmpnn:latest), copy the command block in references/api.md under Docker Reference verbatim. This NIM's cache mount is /home/nvs/.cache/nim, not /opt/nim/.cache. When PROTEINMPNN_NIM_URL is supplied, the service is already managed elsewhere; use that URL and do not start another Docker container.

Readiness:

bash
proteinmpnn_nim_url="${PROTEINMPNN_NIM_URL:-http://localhost:8000}"
until curl -sf "${proteinmpnn_nim_url%/}/v1/health/ready"; do sleep 5; done

Instructions

For a request to execute a design, run scripts/design.py and inspect its results. Writing a request script alone does not complete an execution request. If the user asks only for code or setup instructions, provide those without submitting an inference request.

  1. Use the user's PDB path and requested sequence count. The client reads the entire PDB into input_pdb; do not replace or truncate the supplied backbone.
  2. Select --mode hosted or --mode local and follow Data Transfer and Authorization above before submitting. Hosted mode uploads the PDB to the documented NVIDIA endpoint and requires NGC_API_KEY in the environment. Check only whether the key is set; do not print it, dump the environment, or save authentication headers. Local inference sends no authorization header.
  3. Choose a new --output-dir for each request. The client reserves it before submitting, preserves the raw response for diagnostics, and validates the designed sequence count and score alignment before reporting completion.
  4. Read summary.json and report the actual results described below. If the request or validation fails, report the failure and diagnostic path; do not substitute example sequences or repeatedly resubmit the same request.
Show full SKILL.md (310 more words)Show less

Examples

Run from this skill's directory, or use an absolute path to scripts/design.py. Substitute the user's input path and a new output directory:

bash
python scripts/design.py --mode hosted \
  --pdb /path/to/backbone.pdb --num-sequences 10 \
  --temperature 0.1 --output-dir /path/to/new-design-run

For a running local NIM, use --mode local; the client honors PROTEINMPNN_NIM_URL. To design only chain A, exclude cysteine, or request the soluble model, add --chains A, --omit-aas C, or --soluble respectively. --seed sets random_seed; --ca-only selects the CA-only model. The helper uses one temperature per request; run separate output directories for a temperature sweep. For advanced JSONL controls or a custom batch request, use references/api.md and the post-response example in references/examples.md.

Save And Report Output

The client writes request.json, response.raw, response.json, designed_sequences.fa, and summary.json into the requested output directory. The FASTA preserves the complete returned mfasta, including a native/WT entry when present. The summary contains only designed sequences, each paired with its actual score, and records whether scores came from the JSON array or FASTA headers. It is also printed after the artifacts are saved and checked.

In the final response, report:

  • The number of designed sequences, excluding the native/WT reference.
  • Each design's identifier and actual returned score, plus its sequence (for long sequences, give a clearly labelled preview and link to the full FASTA).
  • The saved FASTA and summary paths, and the raw response path for provenance.
  • That these are inverse-folding candidates, with no fold-back validation performed unless it was actually requested and run.

Do not treat a score as proof that a sequence folds or binds. Further validation with Boltz2 or OpenFold3 is an optional next step. For FASTA/score sanity checks, read references/validation.md.

Limits And Troubleshooting

  • Minimum GPU VRAM: about 3 GB.
  • sampling_temp must be a list, even for one value.
  • Empty mfasta: check non-empty input_pdb and num_seq_per_target >= 1.
  • PDB parse errors: use valid PDB ATOM records.
  • Local URL 404 usually means an accidental /v1/ prefix.
  • Cache mount error: use /home/nvs/.cache/nim inside the container.

© NVIDIA, Apache-2.0. 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 24 other files (scripts, references) in skills/bionemo-proteinmpnn-nim of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • config/skillspector-baseline.yml
  • evals/README.md
  • evals/config.yml
  • evals/evals.json
  • evals/files/1R42.pdb
  • evals/harbor/dataset.toml
  • evals/harbor/proteinmpnn-local-design/environment/Dockerfile
  • evals/harbor/proteinmpnn-local-design/environment/input/1R42.pdb
  • evals/harbor/proteinmpnn-local-design/instruction.md
  • evals/harbor/proteinmpnn-local-design/task.toml
  • evals/harbor/proteinmpnn-local-design/tests/grader.py
  • … and 12 more

Open the folder on GitHubat commit dfdd080

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in NVIDIA/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Proteinmpnn Nim 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.

Proteinmpnn Nim compared with similar skills
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DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Deep Researcher DeployNVIDIA-AI-Blueprints/deep-researcher-agent885—~3.5kAutomated safety check: NotesApache-2.0
Proteina ComplexaBioTender-max/awesome-bio-agent-skills199—~1.4kAutomated safety check: NotesMIT
Megatron-LM Container and Dependency SetupNVIDIA/Megatron-LM18k—~2.6kAutomated safety check: PassApache-2.0

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Questions about Proteinmpnn Nim

What does Proteinmpnn Nim do?

Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Proteinmpnn Nim is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone.

When should I use Proteinmpnn Nim?

Proteinmpnn Nim fits situations like: sequence design; backbone redesign; fixed chains and residues; sampling temperature.

How do I install Proteinmpnn Nim in Claude Code?

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

How do I install Proteinmpnn Nim in Codex?

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

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

What does Proteinmpnn Nim need to run?

Going by SKILL.md and its folder, Proteinmpnn Nim needs Python for the scripts in its folder, the command-line tools its instructions call (docker and python) and credentials named NGC_API_KEY and NVIDIA_API_KEY. Our summary lists: Python 3; Docker; A credential in NGC_API_KEY; A credential in NVIDIA_API_KEY. Its frontmatter pre-approves these tools: Bash, Read, Write, AskUserQuestion. Compatibility (from SKILL.md): Python >=3.10; requests>=2.28.

Does Proteinmpnn Nim access the network?

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

Is Proteinmpnn Nim safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file; 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 Proteinmpnn Nim use?

Proteinmpnn Nim 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 Proteinmpnn Nim use?

About 2k tokens (SKILL.md is roughly 8k 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 3.4k tokens, read only when the agent opens those files.

What are the alternatives to Proteinmpnn Nim?

Skills that share tags, products or a category with Proteinmpnn Nim: Complexa Binder Design (NVIDIA-BioNeMo/bionemo-agent-toolkit, 478 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars), Deep Researcher Deploy (NVIDIA-AI-Blueprints/deep-researcher-agent, 885 stars) and Proteina Complexa (BioTender-max/awesome-bio-agent-skills, 199 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Proteinmpnn Nim?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 skills in this directory. The repository was last updated on October 9, 2026.

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