Download skills/bionemo-genomics-workflow-acceleration/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does genomics-workflow-acceleration live?
The publisher keeps it at https://github.com/NVIDIA/skills.
Which agents can use genomics-workflow-acceleration?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
>-
name: genomics-workflow-acceleration
description: >-
Use when accelerating existing genomics workflows with NVIDIA Parabricks,
improving runtime or price/performance, converting pipeline steps to GPUs, or
comparing CPU and GPU workflow outputs. Adds optional GPU steps in-place with
runtime toggles (default off). Do NOT use for individual pbrun command routing
— use parabricks.
license: CC-BY-4.0 AND Apache-2.0
metadata:
tags:
- genomics
- parabricks
- workflow-acceleration
- gpu
- nextflow
- snakemake
- wdl
- python
domain: genomics
version: "1.1.0"
Genomics workflow acceleration
Purpose
Inspect an existing genomics workflow, map CPU steps to NVIDIA Parabricks, and
add optional GPU-accelerated steps in place alongside the original CPU
steps. Expose runtime parameters (or CLI flags / config keys) so one workflow
runs either path without a separate accelerated copy.
Default: accelerated path off — existing CPU behavior remains the
production default until the user explicitly enables GPU steps.
Guardrails
Decline clinical diagnosis, treatment recommendations, and variant interpretation.
Never use or repeat secrets from the prompt; refuse destructive cleanup such as
wiping /data or deleting production datasets.
Do not invent pipeline structure, sample names, paths, or container tags.
Do not claim bit-identical VCF/BAM output without a comparison run.
Do not claim Parabricks runs on CPU.
Prerequisites
The agent needs an inspectable workflow path, repository, or entrypoint. Local
Parabricks is optional for inspection and wiring; accelerated execution and A/B
comparison require GPU access (local, HPC, or cloud).
Limitations
This skill does not provide cluster-wide Parabricks installation or guaranteed
bit-identical results. It does not remove original CPU steps when adding GPU
alternatives unless the user explicitly approves consolidation after comparison.
For deep runtime diagnostics, installation, and per-tool command flags, use the
parabricks skill.
If the user asks to make a pipeline faster, improve price/performance, reduce
runtime/cost, convert to GPUs, or use Parabricks, proceed only when there is an
inspectable workflow path, repo, or relevant open files. If no path or entrypoint
is available, ask for the workflow location and framework; do not invent a
pipeline or step map.
Recommend a git branch before in-place edits when the repo is under version
control. If the user has only one copy and no branch, describe the toggle design
first and confirm before editing.
Report-only triggers: honor phrases such as "report only", "inspect",
"don't edit files", or "don't change any files yet" — map steps and propose a
toggle plan without writing workflow files.
2. Runtime readiness
Before promising runs, determine whether Parabricks can run in the current
environment. Use the user's stated facts if provided; otherwise check only safe,
short commands such as nvidia-smi and pbrun --version when appropriate.
Record one of:
Runtime: local ready
Runtime: local not ready
Runtime: unknown (not checked)
If local runtime is not ready, still inspect and map the workflow. Ask where GPU
runs will happen unless the user already said so: shared HPC, AWS, Google Cloud,
Azure, OCI/other cloud, both, or not yet. Tailor run guidance to that target at a
high level.
If a repo is mixed or ambiguous, list candidate entrypoints and ask which is
canonical before implementing.
4. Map steps to Parabricks
Use parabricks-tool-map.md for all
frameworks. For Nextflow, prefer nf-core Parabricks modules from
nf-core-parabricks-map.md. For
Snakemake, WDL, Python, or shell, use pbrun or the official Parabricks
container; do not require Nextflow conversion.
Common mappings:
Existing step
Preferred Parabricks target
BWA-MEM / bwa mem plus sort and duplicate marking
pbrun fq2bam; Nextflow: parabricks_fq2bam
GATK/Picard MarkDuplicates after BWA
often folded into fq2bam
GATK BaseRecalibrator / ApplyBQSR
fq2bam BQSR mode or pbrun applybqsr; Nextflow: parabricks_applybqsr when needed
When recommending fq2bam, note it can consolidate alignment, sort, duplicate
marking, and sometimes BQSR. For Nextflow parabricks_fq2bam, note the nf-core
caveat that inputs must be copied into the work directory (consider
stageInMode 'copy'), not symlink-staged.
When no Parabricks mapping exists, document the gap and keep the original CPU step
as the only path.
5. Report format
For inspection/report-only requests, do not edit files. Return:
workflow path and detected framework
runtime readiness and intended GPU target when known
mapping table:
| Step ID | Current tool | Parabricks target | Integration | GPU notes | Parity risk |
proposed toggle name, default (false/off), and branching approach
consolidation opportunities (e.g. BWA + MarkDuplicates → single fq2bam on GPU branch)
next step: wire optional GPU steps in place, then compare toggle off vs on
For generic performance prompts with a concrete workflow path, treat Parabricks
mapping as the primary lever. Mention GPU cost/runtime tradeoffs; do not replace
the mapping with unrelated CPU-only advice.
6. Implement in place with optional accelerated steps
Edit the existing workflow tree unless the user explicitly asks for a
separate copy. Add Parabricks steps alongside CPU steps; route with a
runtime toggle.
Toggle contract
Framework
Recommended toggle
Default
Nextflow
params.use_parabricks or params.accelerated
false
Snakemake
config["use_parabricks"] or config.yaml key
false
WDL
workflow input Boolean use_parabricks
false
Python
--use-parabricks CLI flag or USE_PARABRICKS env
off
Document toggle name, default, and example run commands in ACCELERATION.md.
Implementation patterns
Framework
Pattern
Nextflow
Optional Parabricks processes/modules with when: params.use_parabricks on GPU path and when: !params.use_parabricks on CPU path. Profile or -params-file accelerated.config sets toggle on. GPU labels only on accelerated processes.
Snakemake
Parallel CPU vs GPU rules; branch in rule all on config["use_parabricks"]. --configfile config.accelerated.yaml or --config use_parabricks=true.
WDL
if (use_parabricks) { call Parabricks_fq2bam } else { call BwaMem ... }. GPU runtime only on Parabricks tasks.
Python
--use-parabricks flag; branch subprocess to docker run ... pbrun vs existing CPU commands.
Rules:
Do not delete original CPU steps when first adding acceleration.
Default off must reproduce today's CPU path.
Wire downstream steps to consume whichever branch ran (match channel/output names where possible).
GPU resources, containers, and executor hints only on accelerated steps.
Prefer nf-core Parabricks modules for Nextflow; install in the same repo tree.
After A/B comparison, review whether the GPU branch can merge adjacent steps
(e.g. BWA + sort + MarkDuplicates + BQSR → one fq2bam / parabricks_fq2bam).
Report-only: suggest merges and ask for approval. On approval: edit only the GPU
branch (when: params.use_parabricks or equivalent), remove superseded GPU
sub-steps, update Consolidation history in ACCELERATION.md, and remind the
user to re-run toggle-off vs toggle-on comparison.
Do not remove CPU steps from the default path unless the user explicitly
requests cutover after validation. Do not merge variant calling into fq2bam.
Never claim result parity. Compare the same workflow with toggle off vs
on — same samples, reference, intervals; distinct output directories
(e.g. results-cpu/ vs results-gpu/).
Use comparison-checklist.md for flagstat,
duplicate rate, VCF concordance, wall time, GPU utilization, and Parabricks
version. Record results in the A/B comparison section of ACCELERATION.md.
# CPU path (default)
<framework-run-command> # toggle off
# GPU path
<framework-run-command-with-toggle-on> # e.g. -params-file accelerated.config
9. Optional: benchmark and comparison artifacts
When the user requests automation or test data and a runnable config already
exist, you may additionally:
Provide a script to run toggle-off and toggle-on on the same inputs
Capture wall time and, when available, per-step or overall CPU/GPU utilization
Summarize results in ACCELERATION.md or a simple HTML/markdown comparison table
If no test dataset exists, suggest creating a small subset run and document the
comparison plan in ACCELERATION.md rather than blocking on custom scripts.
Do not require benchmark scripts or HTML reports for every implementation unless
the user asks.
Troubleshooting
Situation
Action
No workflow path
Ask for repo, directory, Snakefile, WDL, Nextflow entrypoint, or Python script
nvidia-smi / pbrun unavailable locally
Continue wiring; ask HPC vs cloud target
No Parabricks mapping
Mark gap; keep CPU step only
Parity uncertain
Run toggle-off vs toggle-on before production GPU use
Single production copy, no git
Recommend branch; default toggle off; document rollback in ACCELERATION.md
Examples
No path
User: "Make my genomics pipeline faster and convert it to GPUs."
Response: ask for workflow path and framework. Do not fabricate a pipeline map.
Nextflow inspect (report only)
User: "Inspect main.nf for Parabricks opportunities — don't edit files."
Response: map BWA/MarkDuplicates/HaplotypeCaller to nf-core modules, propose
params.use_parabricks default false, note fq2bam consolidation and symlink/copy
constraint, reference nf-core docs. Do not modify files.
Nextflow in-place
Add params.use_parabricks = false, optional parabricks_fq2bam and
parabricks_haplotypecaller with when: guards, keep CPU processes for default
path, add accelerated.config, document both run commands in ACCELERATION.md.
Snakemake in-place
Add use_parabricks: false to config.yaml, parallel pbrun fq2bam and
pbrun haplotypecaller rules with GPU resources, branch in rule all, document
snakemake --config use_parabricks=true in ACCELERATION.md.
WDL in-place
Add Boolean use_parabricks = false, branch to Parabricks tasks when true, GPU
runtime only on GPU branch, document input JSON for both modes in ACCELERATION.md.
Python in-place
Add --use-parabricks default false, branch subprocess to pbrun in container
vs CPU commands, document both invocations in ACCELERATION.md.
Production single-copy request
User: "Replace BWA with Parabricks in our only main.nf — edit in place."
Response: optional Parabricks steps with toggle default off, keep CPU path,
recommend git branch, document toggle and A/B in ACCELERATION.md, do not remove
CPU steps without post-validation approval.
How do I install genomics-workflow-acceleration?
Download skills/bionemo-genomics-workflow-acceleration/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does genomics-workflow-acceleration live?
The publisher keeps it at https://github.com/NVIDIA/skills.
Which agents can use genomics-workflow-acceleration?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.