Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fmschulz/omics-skills --skill bio-prefect-dask-nextflowgit clone --depth 1 https://github.com/fmschulz/omics-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fmschulz/omics-skills/bio-prefect-dask-nextflow)<a href="https://agentmods.dev/skills/fmschulz/omics-skills/bio-prefect-dask-nextflow"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-prefect-dask-nextflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fmschulz/omics-skills/bio-prefect-dask-nextflow"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/bio-prefect-dask-nextflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.00550 |
| Opus 5 | $0.00018 | $0.00275 |
| Sonnet 5 | $0.00007 | $0.00110 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
bio-prefect-dask-nextflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bio Prefect + Dask + Nextflow
Choose and scaffold the right workflow engine for local, distributed, or HPC bioinformatics pipelines.
Supplementary docs last verified: 2026-05-30. Current source checks cover Prefect 3.7.2, Dask/distributed 2026.3.0, prefect-dask v0.2.6 (archived repository; install through prefect[dask]), and Nextflow v26.04.3.
Instructions
- Collect requirements (scheduler, container policy, data location, scale).
- Choose engine: Prefect+Dask, Nextflow, or Hybrid.
- Generate a runnable scaffold with clear data layout and resources.
- Validate with a small test and resume/retry checks.
Quick Reference
| Task | Action |
|---|---|
| Engine choice | See decision-matrix.md |
| Prefect+Dask scaffold | See prefect-dask.md |
| Prefect on Slurm | See prefect-hpc-slurm.md |
| Nextflow on HPC | See nextflow-hpc.md |
| Submit Nextflow through Slurm | SLURM_ACCOUNT=... scripts/submit_nextflow.sh main.nf 'data/*.fastq.gz' results |
| Examples | See examples.md |
Input Requirements
- Workflow requirements and steps
- Target environment (local, cluster, cloud)
- Scheduler and container constraints
- Data locations and expected volumes
Output
- Engine recommendation with rationale
- Runnable scaffold (files + commands)
- Resource plan per step
- Validation plan and checkpoints
Quality Gates
- Tiny test run completes end-to-end
- Resume/retry behavior verified
- Resource plan matches cluster limits
- Temporary Dask clusters are created by the task runner at flow runtime and closed with the flow
- Compound FASTQ suffixes do not leak into sample output names
- Nextflow launch runs through
sbatchand verifies trace 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 due to environment mismatch Solution: Pin container/conda versions and validate with a minimal test dataset.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 66 lines · 37 tokens per session scan A 9692b1a7168f
bio-prefect-dask-nextflow is a skill published in the GitHub repository fmschulz/omics-skills (7 stars, last pushed 4d ago), licensed MIT. It adds 37 tokens to every session and 550 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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