How do I install the Dorado Bench V2 skill?

Run npx skillmds@latest add majiayu000/dorado-bench-v2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Dorado Bench V2 safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Dorado Bench V2?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Dorado Bench V2 free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Dorado Bench V2?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Dorado Bench V2 by majiayu000 — Agent Skill

Name: Dorado Bench V2
Rating: 4.2 (5 reviews)
Author: majiayu000

Dorado Bench V2

Oxford Nanopore basecalling with Dorado on University of Michigan HPC clusters (ARMIS2 and Great Lakes). Use when running dorado basecalling, generating SLURM jobs for basecalling, benchmarking models, optimizing GPU resources, or processing POD5 data. Captures model paths, GPU allocations, and job metadata. Integrates with ont-experiments for provenance tracking. Supports fast/hac/sup models, methylation calling, and automatic resource calculation.

majiayu000 Updated Sep 9, 2026 567 repo stars

File contents

Dorado-Bench v2 - ONT Basecalling

Basecalling toolkit for UM HPC clusters with provenance tracking.

Integration

Run through ont-experiments for provenance tracking:

ont_experiments.py run basecalling exp-abc123 --model sup --output calls.bam --json stats.json

Or standalone:

python3 dorado_basecall.py /path/to/pod5 --model sup --cluster armis2 --output calls.bam

Cluster Configurations

ARMIS2 (sigbio-a40)

partition: sigbio-a40
account: bleu1
gres: gpu:a40:1
dorado: /nfs/turbo/umms-bleu-secure/programs/dorado-1.1.1-linux-x64/bin/dorado
models: /nfs/turbo/umms-bleu-secure/programs/dorado_models

Great Lakes (gpu_mig40)

partition: gpu_mig40
account: bleu99
gres: gpu:nvidia_a100_80gb_pcie_3g.40gb:1

Model Tiers

Tier	Accuracy	ARMIS2 Resources
fast	~95%	batch=4096, mem=50G, 24h
hac	~98%	batch=2048, mem=75G, 72h
sup	~99%	batch=1024, mem=100G, 144h

Options

Option	Description
`--model TIER`	fast, hac, sup (default: hac)
`--version VER`	Model version (default: v5.0.0)
`--cluster`	armis2 or greatlakes
`--output FILE`	Output BAM file
`--json FILE`	Output JSON statistics
`--slurm FILE`	Generate SLURM script
`--emit-moves`	Include move table
`--modifications MOD`	Enable 5mCG_5hmCG methylation

SLURM Generation

python3 dorado_basecall.py /path/to/pod5 \
  --model sup@v5.0.0 \
  --cluster armis2 \
  --slurm job.sbatch

sbatch job.sbatch

Event Tracking

When run through ont-experiments, captures:

Model name and full path
Model tier/version/chemistry
Batch size and device
BAM statistics (reads, qscore, N50)
SLURM job ID, nodes, GPUs

Methylation Calling

ont_experiments.py run basecalling exp-abc123 \
  --model sup \
  --modifications 5mCG_5hmCG \
  --output calls_5mc.bam

Resources adjusted: memory +50%, batch size -30%

---name: dorado-bench-v2description: Oxford Nanopore basecalling with Dorado on University of Michigan HPC clusters (ARMIS2 and Great Lakes). Use when running dorado basecalling, generating SLURM jobs for basecalling, benchmarking models, optimizing GPU resources, or processing POD5 data. Captures model paths, GPU allocations, and job metadata. Integrates with ont-experiments for provenance tracking. Supports fast/hac/sup models, methylation calling, and automatic resource calculation.--- # Dorado-Bench v2 - ONT Basecalling Basecalling toolkit for UM HPC clusters with provenance tracking. ## Integration Run through ont-experiments for provenance tracking: ```bash ont_experiments.py run basecalling exp-abc123 --model sup --output calls.bam --json stats.json ``` Or standalone: ```bash python3 dorado_basecall.py /path/to/pod5 --model sup --cluster armis2 --output calls.bam ``` ## Cluster Configurations ### ARMIS2 (sigbio-a40) ```yaml partition: sigbio-a40 account: bleu1 gres: gpu:a40:1 dorado: /nfs/turbo/umms-bleu-secure/programs/dorado-1.1.1-linux-x64/bin/dorado models: /nfs/turbo/umms-bleu-secure/programs/dorado_models ``` ### Great Lakes (gpu_mig40) ```yaml partition: gpu_mig40 account: bleu99 gres: gpu:nvidia_a100_80gb_pcie_3g.40gb:1 ``` ## Model Tiers | Tier | Accuracy | ARMIS2 Resources | |------|----------|------------------| | fast | ~95% | batch=4096, mem=50G, 24h | | hac | ~98% | batch=2048, mem=75G, 72h | | sup | ~99% | batch=1024, mem=100G, 144h | ## Options | Option | Description | |--------|-------------| | `--model TIER` | fast, hac, sup (default: hac) | | `--version VER` | Model version (default: v5.0.0) | | `--cluster` | armis2 or greatlakes | | `--output FILE` | Output BAM file | | `--json FILE` | Output JSON statistics | | `--slurm FILE` | Generate SLURM script | | `--emit-moves` | Include move table | | `--modifications MOD` | Enable 5mCG_5hmCG methylation | ## SLURM Generation ```bash python3 dorado_basecall.py /path/to/pod5 \ --model sup@v5.0.0 \ --cluster armis2 \ --slurm job.sbatch sbatch job.sbatch ``` ## Event Tracking When run through ont-experiments, captures: - Model name and full path - Model tier/version/chemistry - Batch size and device - BAM statistics (reads, qscore, N50) - SLURM job ID, nodes, GPUs ## Methylation Calling ```bash ont_experiments.py run basecalling exp-abc123 \ --model sup \ --modifications 5mCG_5hmCG \ --output calls_5mc.bam ``` Resources adjusted: memory +50%, batch size -30%

Frequently asked questions