Bio Nextflow Manager logo

Bio Nextflow Manager

Organization
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.

Overview

Publisherfrumu-ai
Repositorytandem
Skill namebio-nextflow-manager
Stars
121
Forks
13
Bundled files
Instructions only
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by frumu-ai on GitHub. Read the source before you install it.

Installation

Install the Bio Nextflow Manager AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/frumu-ai/tandem.git /tmp/tandem
mkdir -p .claude/skills
cp -r /tmp/tandem/apps/tandem-desktop/src-tauri/resources/skill-templates/bio-nextflow-manager .claude/skills/bio-nextflow-manager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bio Nextflow Manager in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bio Nextflow Manager on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bio Nextflow Manager is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
# 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.

bash
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 TypePipelineGoal
RNA-seqrnaseqGene expression
WGS/WESsarekVariant calling
ATAC-seqatacseqChromatin accessibility

Auto-detect from data:

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

Frequently asked questions

What does the Bio Nextflow Manager AI skill do?

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.

Why use Bio Nextflow Manager on TypingMind?

Because you install it once and use it with any model. Bio Nextflow Manager is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bio Nextflow Manager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/frumu-ai/tandem/tree/main/apps/tandem-desktop/src-tauri/resources/skill-templates/bio-nextflow-manager. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bio Nextflow Manager?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bio Nextflow Manager?

As many as you like. As long as a model supports skills, you can use Bio Nextflow Manager with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bio Nextflow Manager AI skill free?

It is published on GitHub by frumu-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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