star-rnaseq-aligner
STAR alignment skill for splice-aware RNA-seq read mapping with comprehensive QC metrics
Profile →npx skills add a5c-ai/babysitter --skill star-rnaseq-aligner --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# STAR RNA-seq Aligner Skill ## Purpose Provide STAR alignment for splice-aware RNA-seq read mapping with comprehensive QC metrics. ## Capabilities - Splice junction detection - Two-pass alignment mode - Chimeric read detection (fusions) - Gene quantification (--quantMode) - Custom genome index generation - Output in multiple formats ## Usage Guidelines - Generate genome indices with annotation - Use two-pass mode for novel junction discovery - Enable chimeric read detection for fusion analysis - Generate quantification in addition to alignments - Optimize parameters for read length - Document STAR version and parameters ## Dependencies - STAR - HISAT2 - kallisto ## Process Integration - RNA-seq Differential Expression Analysis (rnaseq-differential-expression) - Single-Cell RNA-seq Analysis (scrnaseq-analysis) - Spatial Transcriptomics Analysis (spatial-transcriptomics)
- Purpose
- Capabilities
- Usage Guidelines
- Dependencies
- Process Integration
What does the star-rnaseq-aligner skill do?
STAR alignment skill for splice-aware RNA-seq read mapping with comprehensive QC metrics
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill star-rnaseq-aligner --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.