Parabricks by NVIDIA-BioNeMo — AI Skill for Any LLM

Overview

Publisher	NVIDIA-BioNeMo
Repository	`bionemo-agent-toolkit`
Skill name	`parabricks`
Stars	495
Forks	77
Bundled files	41
Links	Skill source Repository

Markdown instructions

A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

Works with any LLM

AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

41 bundled files

Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

Open source

Published by NVIDIA-BioNeMo on GitHub. Read the source before you install it.

Installation

Install the Parabricks AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/library-skills/parabricks
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/library-skills/parabricks

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit.git /tmp/bionemo-agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/bionemo-agent-toolkit/library-skills/parabricks .claude/skills/parabricks

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parabricks in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Parabricks on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Parabricks is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed

The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

Switch models mid-chat

Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Parabricks

Purpose

Use this skill to discover the right NVIDIA Parabricks pbrun command, assess runtime readiness, and generate version-aware command guidance for individual tools.

Do not use this skill for whole-workflow inspection, acceleration planning, or wiring optional GPU branches. For pipeline-level work, use genomics-workflow-acceleration.

When to Use This Skill

Which pbrun tool fits the user's data and goal
GPU, driver, Docker, container, storage, or installation readiness
Command shape, flags, and validation for a specific Parabricks tool
Reference/index compatibility and CPU-to-Parabricks option translation
Troubleshooting a single Parabricks command or tool family
Planning-only or "do not run" questions about a named pbrun tool (for example reference/index compatibility, output naming, or flag validity). Use this skill's references to answer; do not skip it because nothing will be executed.

Prerequisites

Command guidance: No Parabricks/NGC credential or local GPU/container runtime is required to read references or generate commands.
Readiness helper: Requires Bash and GNU coreutils timeout (or gtimeout) on the host being inspected. Missing runtime components are reported as readiness gaps; without a timeout utility it exits before running probes.
STAR translation helper: Requires Python 3.7 or newer, using only the standard library. It does not require a GPU or a Parabricks installation.
Execution: The documented Docker commands require a supported Linux host, an NVIDIA GPU with sufficient memory, a compatible NVIDIA driver, Docker, NVIDIA Container Toolkit, and access to the selected Parabricks image. Pulling the image may require an NGC API key for authentication to nvcr.io. See runtime-environment.md for version-specific requirements and checks.

Tool scope: read this skill's references and user-supplied manifests; write only requested command artifacts. Use the bundled helpers for translation or an authorized host inspection. Documentation lookups use docs.nvidia.com; image access uses the selected registry only for authorized container execution. These instructions do not grant tool permissions; the host's permission policy still applies to shell, file, network and optional documentation MCP access.

Inputs

Use the user's explicit arguments and supplied manifests as the source of truth; use prior context only where it does not conflict. Read a named manifest before translating it. For command guidance, identify the assay, input paths, reference/index provenance, outputs and target release. Ask only for missing facts that change the answer; label unresolved template values as placeholders. Required inputs are the selected tool's read/reference and output arguments. Optional inputs include intervals, resource bundles, logs and tuning parameters when supported by that tool; do not invent them to fill a template. Runtime-only questions do not need sample metadata. Supplied deployment paths are not permission to probe local data or hardware.

If the user is unsure which tool applies, scan the Tool Reference Index below, then load the matching references/pbrun-<tool>.md file.

Limitations

This skill routes and guides Parabricks commands. It does not install Parabricks, infer missing sample metadata, guarantee output parity, provide clinical interpretation, or promise exact runtime without benchmark data.

Instructions

Identify the requested release. The option references cover v4.7.1; examples from another release are labeled. A software version and image tag can differ: NVIDIA's 4.7.1 image is tagged 4.7.1-1. Preserve the user's verified image selection; use command-conventions.md for the shared launch and validation contract.
Classify the request:
- Runtime → runtime-environment.md
- Tool discovery → Tool Reference Index (below)
- Specific command → matching references/pbrun-<tool>.md. Read it before any web search or MCP lookup; use those only to verify version-sensitive details the reference does not settle.
Combine the tool-specific mapping with shared-options.md rows whose Tools column names the selected command. For STAR argument arrays or multi-lane batches, use the local translation helper; it preserves tokens and reports unmapped semantics without executing a workload.
Collect missing biological and filesystem context before generating commands.
Return the requested command or artifact with unresolved assumptions. For planning-only work, check command text against the references and supplied facts. Filesystem, container and output checks apply only to an authorized execution. Do not turn command preparation into a readiness inspection.

Available Scripts

Run these from the skill directory containing this file; paths are relative to that directory. See the helper prerequisites above.

Script	Purpose	Arguments
`scripts/check_parabricks_runtime.sh`	Inspect the execution host; report gaps without installing anything	`--path DIR` (repeatable), `--format text\|json`, `--timeout SECONDS`; container probes require `--run-container-check` or `--run-parabricks-check` with `--parabricks-version TAG` / `--parabricks-image IMAGE`
`scripts/translate_star_to_parabricks.py`	Convert explicit STAR argument arrays to reviewed command drafts	`--input FILE` or `--input -`; request and output schema

Output Format

For explanations, give the decision, complete command, and remaining checks. For file requests, write the specified path and schema, then read back and validate the artifact against the input manifest. Report all job IDs, omitted options and unresolved output contracts. Do not replace supplied values with illustrative examples. The runtime helper's JSON has schema_version, checks and readiness results; the translator emits status, draft_argv, command, notes, issues and untranslated for each request.

Tool Reference Index

If the command is named, open references/pbrun-<command>.md directly. For tool selection and official manual links, use tool-index.md. Common routes:

Request	Reference
DNA FASTQ alignment / RNA STAR translation	fq2bam / rna_fq2bam
Existing BAM germline calls / tumor-normal calls	haplotypecaller / mutectcaller
Giraffe graph alignment	giraffe
STAR-Fusion migration	starfusion
Recalibration / apply a recalibration report	bqsr / applybqsr
BAM metrics / multiple metric families	bammetrics / collectmultiplemetrics
GVCF indexing / joint genotyping	indexgvcf / genotypegvcf
Panel-of-normals preparation / annotation	prepon / postpon
BWA performance options	performance

Examples

Runtime Readiness

For GPU, driver, Docker, container, storage, or installation questions, read runtime-environment.md and prefer:

bash
bash scripts/check_parabricks_runtime.sh --format json

Add --path <dir> for known input/output/tmp paths. Run container probes only with user consent.

STAR Translation

bash
python3 scripts/translate_star_to_parabricks.py --input requests.json

Prepare the request using the translation schema. Review issues and untranslated before using the draft. Tool references show the pbrun portion; the shared container envelope is documented once in command-conventions.md.

Troubleshooting

Error	Cause	Solution
Multiple plausible tools	Data type or goal underspecified	Ask for assay, inputs, caller preference, desired output; use Tool Reference Index
Exact flag requested	Options are version-sensitive	Check the selected tool reference, NVIDIA docs, or `pbrun <tool> --help`
Runtime question	GPU, Docker, drivers, or storage	Use runtime-environment reference and diagnostic script
Wrong tool family	Assay or input type unclear	Confirm DNA/RNA/methylation/long-read/pangenome before routing
CUDA or memory failure	Runtime not ready or GPU memory constrained	Assess runtime before tuning command flags
Documentation MCP unavailable	Connection, timeout or server error	Use the bundled release reference first; for an unresolved fact, fall back to the official versioned manual and mark any remaining uncertainty

Guardrails

Treat command availability and options as version-sensitive.
Do not infer exact flags from command names alone.
Do not collapse standalone tools and full pipelines when explaining tradeoffs.
Do not substitute DNA fq2bam for RNA, or germline for somatic callers.
Do not invent sample names, read groups, reference builds, known-sites files, model files, graph resources, container tags, or output paths.
Do not install, upgrade, or modify packages. Label setup commands as user-run.
Do not claim CPU execution of Parabricks tools.
Do not claim biological or VCF parity without a comparison run.
Use the optional documentation MCP server only for details the bundled references do not settle. If its connection fails, avoid repeated discovery or lookup loops; use the official manual. Never claim a failed lookup verified an option. Documentation access does not require genomic data or credentials.

Key References

Parabricks documentation MCP server: https://docs.nvidia.com/clara/parabricks/_mcp/server
Parabricks tool index: https://docs.nvidia.com/clara/parabricks/tool-reference
Output accuracy and compatible CPU software versions: https://docs.nvidia.com/clara/parabricks/about-parabricks/software-overview/output-accuracy-and-compatible-cpu-software-versions
Getting started: https://docs.nvidia.com/clara/parabricks/get-started
Overview: https://docs.nvidia.com/clara/parabricks/about-parabricks

Frequently asked questions

What does the Parabricks AI skill do?

Plan NVIDIA Parabricks pbrun commands, translate CPU options and check runtime readiness. Use for individual tools, including planning-only questions.

Why use Parabricks on TypingMind?

Because you install it once and use it with any model. Parabricks is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Parabricks in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/library-skills/parabricks. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Parabricks?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Parabricks?

As many as you like. As long as a model supports skills, you can use Parabricks with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Parabricks AI skill free?

It is published on GitHub by NVIDIA-BioNeMo. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Parabricks

Purpose

When to Use This Skill

Prerequisites

Inputs

Limitations

Instructions

Available Scripts

Output Format

Tool Reference Index

Examples

Runtime Readiness

STAR Translation

Troubleshooting

Guardrails

Key References

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now