deepvariant-caller
DeepVariant deep learning variant calling skill for high-accuracy SNV and indel detection
npx skills add a5c-ai/babysitter --skill deepvariant-caller --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.
# DeepVariant Caller Skill ## Purpose Enable DeepVariant deep learning variant calling for high-accuracy SNV and indel detection. ## Capabilities - GPU-accelerated variant calling - WGS/WES/PacBio mode selection - Model customization and retraining - Confidence calibration - Multi-sample variant calling - Docker/Singularity deployment ## Usage Guidelines - Select appropriate model for sequencing type - Use GPU acceleration when available - Validate accuracy against benchmark datasets - Consider container deployment for reproducibility - Document model version and parameters - Compare with traditional callers for validation ## Dependencies - DeepVariant - Parabricks ## Process Integration - Whole Genome Sequencing Pipeline (wgs-analysis-pipeline) - Long-Read Sequencing Analysis (long-read-analysis) - Analysis Pipeline Validation (pipeline-validation)
- Purpose
- Capabilities
- Usage Guidelines
- Dependencies
- Process Integration
What does the deepvariant-caller skill do?
DeepVariant deep learning variant calling skill for high-accuracy SNV and indel detection
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill deepvariant-caller --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.