Agent skill

bio-long-read-sequencing-clair3-variants

Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-long-read-sequencing-clair3-variants --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 7 KB
Bundled scripts: yes
Path: skills/bio-long-read-sequencing-clair3-variants/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: DeepVariant 1.6+, Entrez Direct 21.0+, bcftools 1.19+, minimap2 2.26+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Clair3 Variant Calling **"Call variants from my long-read data"** → Use deep learning to identify germline SNPs and small indels from ONT or PacBio aligned reads with high accuracy. - CLI: `run_clair3.sh --bam_fn=sample.bam --ref_fn=ref.fa --platform=ont` ## Basic Usage ```bash # ONT variant calling run_clair3.sh \ --bam_fn=sample.bam \ --ref_fn=reference.fasta \ --threads=32 \ --platform=ont \ --model_path=${CONDA_PREFIX}/bin/models/ont \ --output=clair3_output # PacBio HiFi variant calling run_clair3.sh \ --bam_fn=sample.bam \ --ref_fn=reference.fasta \ --threads=32 \ --platform=hifi \ --model_path=${CONDA_PREFIX}/bin/models/hifi \ --output=clair3_output # Output: clair3_outpu

What's inside
Steps it walks through
  1. Version Compatibility
  2. Basic Usage
  3. Platform-Specific Models
  4. Key Parameters
  5. Region-Specific Calling
  6. gVCF Output
  7. Phased Variant Calling
  8. Quality Filtering
  9. Python Wrapper
  10. Comparison with Other Callers
  11. Troubleshooting
  12. Docker Usage
  13. Related Skills
Ships with 2 files
  • examples/clair3_workflow.sh
  • usage-guide.md
Commands it runs
ONT variant calling
run_clair3.sh \
PacBio HiFi variant calling
List available models
ls ${CONDA_PREFIX}/bin/models/
Specify exact model
Call variants in specific regions
Call on non-human genomes (all contigs)
Generate gVCF for joint calling
Joint genotyping multiple samples
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the bio-long-read-sequencing-clair3-variants skill do?

Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-long-read-sequencing-clair3-variants --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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.

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