Claude Skills Guide

Computational biology and bioinformatics workflows involve intensive data processing, statistical analysis, and visualization tasks that consume significant researcher time. Claude Code skills provide specialized automation for sequence analysis, genome annotation, protein structure prediction, and reproducible research pipelines. These skills work alongside your existing toolchain—Biopython, Bowtie, BLAST, R/Bioconductor—to accelerate common operations without replacing your core analysis tools. For a broader look at how Claude skills support data-heavy workflows, the Claude skills for data science and Jupyter guide covers interactive analysis environments.

Claude skills are Markdown files stored in ~/.claude/skills/ and invoked with /skill-name inside a Claude Code session. They provide standing instructions for specialized tasks without requiring Python imports or package installations.

Essential Skills for Bioinformatics Workflows

xlsx Skill for Experimental Data Management

Bioinformatics projects generate substantial tabular data from experiments, sequencing runs, and analysis results. The xlsx skill handles spreadsheet operations that would otherwise require manual intervention.

/xlsx
Load gene_expression_counts.xlsx, normalize using TPM method, identify differentially expressed genes with p-value < 0.05, and export results to formatted report

This skill processes expression matrices, applies standard normalization techniques, and generates publication-ready output. The xlsx skill preserves formulas and formatting, making it useful for maintaining experimental logs alongside computational results.

pdf Skill for Literature Review Automation

Staying current with bioinformatics literature requires processing numerous publications. The pdf skill extracts text, tables, and figures from research papers, enabling rapid literature review and knowledge synthesis.

/pdf
Extract all tables from multiple_sequence_alignment_methods.pdf, convert to CSV format, and identify common alignment algorithms referenced across papers

This approach accelerates systematic reviews and helps identify trends in methodology without manual data entry.

tdd Skill for Reproducible Analysis Pipelines

Test-driven development principles apply directly to bioinformatics pipeline construction. The tdd skill helps you build reliable, tested workflows that produce consistent results across datasets.

/tdd
Create a Python module for parsing FASTQ files, include tests for quality score extraction, read pairing validation, and adapter sequence detection

This skill generates test scaffolding, validates pipeline components, and ensures your analysis code handles edge cases—critical when processing sensitive biological data where reproducibility matters.

Specialized Workflows for Biological Data

Sequence Analysis and Manipulation

Bioinformatics workflows frequently require sequence manipulation: filtering reads, translating DNA to protein, identifying motifs, and preparing data for downstream analysis. Claude skills provide conversational interfaces to these operations without requiring custom script creation for each task.

/xlsx
Parse alignment_summary.tsv, filter reads with mapping quality < 30, calculate coverage statistics per chromosome, and generate BED file for visualization

The skill translates your intent into executable code, handles file I/O, and produces outputs in standard formats compatible with genome browsers and other tools.

Phylogenetic Analysis Pipelines

Building phylogenetic trees from sequence data involves multiple steps: alignment, model selection, tree construction, and visualization. Claude skills coordinate these steps into cohesive workflows.

/tdd
Build a pipeline that takes multiple FASTA sequences, performs Clustal alignment, selects best substitution model using BIC criterion, constructs maximum likelihood tree, and exports annotated Newick file

This skill generates modular, testable code that you can integrate into larger analysis frameworks.

NGS Data Processing

Next-generation sequencing data requires quality control, trimming, alignment, and variant calling—repetitive tasks ideal for automation. Claude skills provide templates and execution guidance for standard pipelines.

/pdf
Extract sample metadata from experiment_manifest.pdf, validate sample names match FASTQ filenames, generate sample sheet for BCLconvert, and create configuration for variant calling pipeline

This approach reduces configuration errors and ensures consistent processing across samples.

Integration with Existing Bioinformatics Tools

Claude skills work alongside established bioinformatics tools rather than replacing them. The key integration points include:

Biopython Operations: Skills generate Biopython code for sequence parsing, alignment, and database queries. Your existing BioPython scripts remain usable while Claude assists with new functionality.

Command-Line Tool Automation: Tools like Bowtie2, BWA, GATK, and samtools benefit from workflow orchestration. Skills generate shell commands, manage parallelization, and handle output parsing.

Statistical Analysis in R: The tdd skill creates R functions with testthat unit tests, ensuring statistical computations remain correct across code modifications.

/tdd
Create R functions for DESeq2 differential expression analysis including normalizations, model fitting, and results extraction with comprehensive test cases using testthat

Knowledge Management with supermemory Skill

Bioinformatics projects accumulate substantial institutional knowledge: protocol notes, analysis decisions, troubleshooting logs, and results interpretations. The supermemory skill maintains persistent context across sessions, making this knowledge accessible conversationally.

/supermemory
Search previous conversations for RNA-seq alignment parameters used on project-alpha, extract the specific BWA mem command and flags, and explain why certain settings were chosen

This skill preserves institutional knowledge that would otherwise be lost between projects or team member transitions.

Documentation and Reporting

Research reproducibility requires comprehensive documentation. Claude skills generate analysis reports, update laboratory notebooks, and create publication-ready figures.

/pdf
Parse variant_calling_results.vcf, generate summary statistics including transition/transversion ratio, SNP density per chromosome, and create visualization plots for inclusion in Methods section

This automation ensures documentation keeps pace with analysis without becoming a separate burden.

Practical Implementation

To integrate Claude skills into your bioinformatics workflow:

  1. Identify repetitive tasks: data formatting, quality control checks, report generation
  2. Install relevant skills: xlsx for spreadsheet operations, pdf for document processing, tdd for pipeline development
  3. Define standard operating procedures as skill instructions
  4. Test skills with small datasets before production use
  5. Build skill sequences for complete pipeline automation

Skills function as collaborative assistants rather than autonomous agents—they respond to your direction while handling implementation details. This approach maintains researcher control while accelerating mechanical tasks.

The bioinformatics community benefits from reproducible, well-documented analysis. Claude skills support these goals by automating documentation generation, test creation, and pipeline construction. Your domain expertise remains essential; skills amplify your productivity without substituting for scientific judgment.

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