How do I install the Bio Nextflow Manager skill?

Run npx skillmds@latest add frumu-ai/bio-nextflow-manager 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 Bio Nextflow Manager safe to use?

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Which AI agents work with Bio Nextflow Manager?

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Is Bio Nextflow Manager free to use?

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

Who published Bio Nextflow Manager?

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

Bio Nextflow Manager by frumu-ai — Agent Skill

Name: Bio Nextflow Manager
Rating: 4.2 (5 reviews)
Author: frumu-ai

Bio Nextflow Manager

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

frumu-ai Updated Sep 10, 2026

File contents

nf-core Pipeline Deployment

Run nf-core bioinformatics pipelines on local or public sequencing data.

Note: This skill utilizes the Bio-Informatics Pack. Scripts and references are located in: src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/

Target users: Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses.

Workflow Checklist

- [ ] Step 0: Acquire data (if from GEO/SRA)
- [ ] Step 1: Environment check (MUST pass)
- [ ] Step 2: Select pipeline (confirm with user)
- [ ] Step 3: Run test profile (MUST pass)
- [ ] Step 4: Create samplesheet
- [ ] Step 5: Configure & run (confirm genome with user)
- [ ] Step 6: Verify outputs

Step 0: Acquire Data (GEO/SRA Only)

Skip this step if user has local FASTQ files.

For public datasets, fetch from GEO/SRA first. See pack's references/geo-sra-acquisition.md.

Quick start:

# Set path to pack scripts
$PACK_SCRIPTS = "src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts"

# 1. Get study info
python $PACK_SCRIPTS/sra_geo_fetch.py info GSE110004

# 2. Download (interactive mode)
python $PACK_SCRIPTS/sra_geo_fetch.py download GSE110004 -o ./fastq -i

# 3. Generate samplesheet
python $PACK_SCRIPTS/sra_geo_fetch.py samplesheet GSE110004 --fastq-dir ./fastq -o samplesheet.csv

DECISION POINT: After fetching study info, confirm with user:

Which sample subset to download (if multiple data types)
Suggested genome and pipeline

Then continue to Step 1.

Step 1: Environment Check

Run first. Pipeline will fail without passing environment.

python src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts/check_environment.py

All critical checks must pass. If any fail, provide fix instructions (Docker, Nextflow, Java).

Step 2: Select Pipeline

DECISION POINT: Confirm with user before proceeding.

Data Type	Pipeline	Goal
RNA-seq	`rnaseq`	Gene expression
WGS/WES	`sarek`	Variant calling
ATAC-seq	`atacseq`	Chromatin accessibility

Auto-detect from data:

python src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts/detect_data_type.py /path/to/data

---name: bio-nextflow-managerdescription: Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.---# nf-core Pipeline DeploymentRun nf-core bioinformatics pipelines on local or public sequencing data.> **Note:** This skill utilizes the **Bio-Informatics Pack**.> Scripts and references are located in: `src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/`**Target users:** Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses.## Workflow Checklist```- [ ] Step 0: Acquire data (if from GEO/SRA)- [ ] Step 1: Environment check (MUST pass)- [ ] Step 2: Select pipeline (confirm with user)- [ ] Step 3: Run test profile (MUST pass)- [ ] Step 4: Create samplesheet- [ ] Step 5: Configure & run (confirm genome with user)- [ ] Step 6: Verify outputs```---## Step 0: Acquire Data (GEO/SRA Only)**Skip this step if user has local FASTQ files.**For public datasets, fetch from GEO/SRA first. See pack's `references/geo-sra-acquisition.md`.**Quick start:**```bash# Set path to pack scripts$PACK_SCRIPTS = "src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts"# 1. Get study infopython $PACK_SCRIPTS/sra_geo_fetch.py info GSE110004# 2. Download (interactive mode)python $PACK_SCRIPTS/sra_geo_fetch.py download GSE110004 -o ./fastq -i# 3. Generate samplesheetpython $PACK_SCRIPTS/sra_geo_fetch.py samplesheet GSE110004 --fastq-dir ./fastq -o samplesheet.csv```**DECISION POINT:** After fetching study info, confirm with user:- Which sample subset to download (if multiple data types)- Suggested genome and pipelineThen continue to Step 1.---## Step 1: Environment Check**Run first. Pipeline will fail without passing environment.**```bashpython src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts/check_environment.py```All critical checks must pass. If any fail, provide fix instructions (Docker, Nextflow, Java).---## Step 2: Select Pipeline**DECISION POINT: Confirm with user before proceeding.**| Data Type | Pipeline | Goal || --------- | --------- | ----------------------- || RNA-seq | `rnaseq` | Gene expression || WGS/WES | `sarek` | Variant calling || ATAC-seq | `atacseq` | Chromatin accessibility |Auto-detect from data:```bashpython src-tauri/resources/packs/bio-informatics-pack/nextflow-pipelines/scripts/detect_data_type.py /path/to/data```

Frequently asked questions