How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
bulk-rnaseq
bulk-rnaseq is an open-source data skill for Claude Code and compatible agents, published by K-Dense-AI. Its author describes it as: “End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then…”. The project has 34k stars on GitHub and is available under the MIT license. Add it to your setup with `git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/bulk-rnaseq`.
What bulk-rnaseq does
This skill orchestrates a complete, **defensible** bulk RNA-seq differential-expression study, from raw sequencing reads to enriched pathways and figures. It is a router, not a reimplementation: most stages already have dedicated skills in this repo, and this skill connects them in the right order, fills the one real gap (raw reads → a gene-level counts matrix), and enforces the design and QC decisions that determine whether the final result is trustworthy.
Installation
Add bulk-rnaseq to your agent with:
git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/bulk-rnaseqAlways review a skill's source before installing it. This command comes from the skill's public repository; the linked repo is the source of truth for exact setup steps.
What's inside
The SKILL.md for bulk-rnaseq is organised into these sections:
- Overview
- When to Use This Skill
- The Pipeline at a Glance
- Two Upstream Paths — Pick One
- Setup
- Quick Start
- Path A — nf-core/rnaseq (recommended)
- Path B — standalone STAR/Salmon (abbreviated)
- Counts → DE → enrichment (both paths)
- Stage-by-Stage Workflow
- The counts → DE bridge (the key glue)
- Common Pitfalls
When to use it
Reach for bulk-rnaseq when you want data help from your agent without writing the same instructions every session. Load the skill and the agent picks it up automatically for relevant tasks.
Strengths
- Clear MIT license — safe to read and adapt
- Ships in K-Dense-AI/scientific-agent-skills, an established project with 33,821 GitHub stars
- Actively maintained (recent commits)
Topics
Frequently asked questions
- What does bulk-rnaseq do?
- End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.
- How do I install bulk-rnaseq?
- Run git clone https://github.com/K-Dense-AI/scientific-agent-skills ~/.claude/skills/bulk-rnaseq in your agent, then reload your skills. Review the source at https://github.com/K-Dense-AI/scientific-agent-skills before installing.
- Is bulk-rnaseq free to use?
- Yes. bulk-rnaseq is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Where does bulk-rnaseq come from?
- bulk-rnaseq ships inside K-Dense-AI/scientific-agent-skills, a repository that contains 41 catalogued skills in total. The repository's 33,821 GitHub stars apply to that whole collection, not to this skill on its own.
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