bio-prefect-dask-nextflow
Skill9 starsChanged 4 months agoMITView on GitHub
Choose and scaffold Prefect+Dask or Nextflow pipelines. Use when designing a local, distributed, or Slurm-backed bioinformatics workflow.
In this repository35 other files it gives its agents.
- AGENTS.mdAGENTS.md
- docs/agents.mdAGENTS.md
- ai-scientist-evaluatorSkill
- arxiv-searchSkill
- beautiful-data-vizSkill
- bio-annotationSkill
- bio-assembly-qcSkill
- bio-binning-qcSkill
- bio-fasta-database-curatorSkill
- bio-foundation-housekeepingSkill
- bio-gene-callingSkill
- bioinformatics-projectSkill
- bio-interdomain-hgtSkill
- bio-logicSkill
- bio-phylogenomicsSkill
- bio-protein-clustering-pangenomeSkill
- bio-reads-qc-mappingSkill
- biorxiv-searchSkill
- bio-stats-ml-reportingSkill
- bio-structure-annotationSkill
- bio-viromicsSkill
- bio-workflow-methods-docwriterSkill
- crossref-lookupSkill
- csag-extractionSkill
- exploratory-data-analysisSkill
- manuscript-review-councilSkill
- notebooksSkill
- pdf-to-mdSkill
- plotly-dashboard-skillSkill
- polars-dovmedSkill
- proposal-reviewSkill
- public-db-lookupSkill
- scientific-impact-assessmentSkill
- scientific-writingSkill
- tracking-taxonomy-updatesSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 18
Run heavy work through the scheduler, never on a login node.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Bio Prefect + Dask + Nextflow
Choose a workflow engine for a local, distributed, or HPC bioinformatics pipeline and scaffold it.
Supplementary docs last verified: 2026-10-01 against Prefect 3.8.7, prefect-dask 0.3.7 (maintained in the Prefect repository; install with prefect[dask]), Dask/distributed 2026.8.0, and Nextflow v26.04.6.
Instructions
- Collect requirements: scheduler, container policy, data location, scale, and whether steps are CLI tools or Python functions.
- Choose the engine with decision-matrix.md: Nextflow for file-based CLI tools on a scheduler, Prefect+Dask for Python-heavy or dynamic work, or a hybrid with Prefect as the control plane and Nextflow as the compute plane.
- Generate a runnable scaffold with a clear data layout, per-step resources, and explicit thread counts (
task.cpusin Nextflow,threads_per_workerin Dask). - Pin the software: pixi for tool stacks or Apptainer images on HPC, uv for Python (
uv add "prefect[dask]"). Nextflow v26.04+ parses scripts and configs with the strict syntax by default; write code that passesnextflow lint. - Run heavy work through the scheduler, never on a login node. Submit the Nextflow launcher with
scripts/submit_nextflow.sh; it runs from the current directory, so call it from the project root. - Validate with a small test dataset, then check resume (
-resume, or Prefect retries plus idempotent outputs) before scaling.
Quick Reference
| Task | Action |
| Engine choice | decision-matrix.md |
| Prefect+Dask scaffold | prefect-dask.md |
| Prefect on Slurm | prefect-hpc-slurm.md |
| Nextflow on HPC | nextflow-hpc.md |
| Submit Nextflow through Slurm | SLURM_ACCOUNT=... scripts/submit_nextflow.sh main.nf 'data/*.fastq.gz' results |
| Multi-cluster sites | Export SBATCH_CLUSTERS, SBATCH_PARTITION, and SBATCH_QOS before the wrapper; --parsable then prints jobid;cluster, so strip the suffix and pass the cluster to squeue -M and sacct -M |
| Examples | examples.md |
Input Requirements
- Workflow requirements and steps
- Target environment (local, cluster, cloud)
- Scheduler, account, partition, and container constraints
- Data locations and expected volumes
Output
- Engine recommendation with rationale
- Runnable scaffold (files and commands)
- Resource plan per step (CPUs, memory, walltime)
- Validation plan and checkpoints
Quality Gates
- A tiny test run completes end-to-end through the same launcher used for production.
- Resume and retry behavior is verified; Prefect and Nextflow retries are not stacked without a stated policy.
- The resource plan matches the cluster's limits, and every tool receives an explicit thread count.
- Temporary Dask clusters are created by the task runner at flow runtime and closed with the flow.
- Compound FASTQ suffixes (
.fastq.gz) do not leak into sample output names. - The Nextflow launch runs through
sbatchand verifies the trace file and non-empty result artifacts.
Examples
Example 1: Engine recommendation
Choice: Nextflow
Why: CLI-heavy pipeline, HPC scheduler required, reproducible cache/resume needed.Troubleshooting
Issue: Workflow fails on HPC because of an environment mismatch Solution: Pin the tool stack (pixi lockfile or Apptainer image) and validate with a minimal test dataset on a compute node.
Issue: Nextflow v26.04+ rejects a script or config that ran on an older version
Solution: The strict parser is now the default. Fix the reported pattern (see the Nextflow strict-syntax migration guide), or set NXF_SYNTAX_PARSER=v1 as a temporary workaround and record it.
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