Gtars
Skill AktivHigh-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
To enable efficient and robust analysis of genomic interval data for researchers and ML practitioners in computational genomics.
Funktionen
- High-performance genomic interval analysis in Rust
- Python bindings for programmatic use
- CLI for rapid analysis and scripting
- Overlap detection and IGD indexing
- Coverage track generation (WIG, BigWig)
- Genomic tokenization for ML
- Reference sequence management and digest computation
Anwendungsfälle
- Analyzing BED files and genomic regions
- Detecting overlaps between genomic features
- Generating coverage tracks from sequencing data
- Preprocessing genomic data for ML models
- Validating reference genome integrity
Nicht-Ziele
- Performing wet-lab experimental design
- Direct integration with cloud sequencing platforms
- Replacing comprehensive bioinformatics pipelines for complex multi-omics integration
Workflow
- Load genomic data (BED, FASTA)
- Perform analysis (overlap, coverage, tokenization, sequence retrieval)
- Export results or use in downstream ML/analysis pipelines
Praktiken
- Genomic data analysis
- ML preprocessing
- Bioinformatics workflows
Voraussetzungen
- Python 3.11+ with uv
- Rust/Cargo for CLI installation
Documentation
- info:Configuration & parameter referenceWhile parameters are demonstrated in examples and code snippets, explicit documentation of all options, defaults, and precedence order is not detailed.
Version
- warning:Release ManagementWhile Cargo install uses features, the SKILL.md frontmatter lists license as 'Unknown' and there is no explicit semver version declared for the skill itself, making version pinning difficult for users.
Execution
- info:ValidationInput file paths and coordinates are expected to be valid based on common usage, but explicit schema validation using libraries like Zod or pydantic is not detailed in the documentation.
Errors
- info:Actionable error messagesError handling is mentioned, with examples for file not found and invalid formats, but specific remediation steps or doc links are not always provided for every error path.
Practical Utility
- info:Edge casesWhile common error handling is mentioned, detailed documentation of specific edge cases (e.g., malformed input, rate limits for hypothetical external interactions) and their recovery steps is limited.
Installation
npx skills add K-Dense-AI/claude-scientific-skillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
Vertrauenssignale
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