Explore our collection of Agent Skills to enhance your AI workflow.
Orchestrates parallel scientist agents to perform deep, multi-stage research and synthesis within codebases and documentation.
Terminates active Oh My Claude (OMC) execution modes while managing state cleanup and progress preservation.
Optimizes token usage and operational costs by routing tasks to the most efficient AI models and parallelizing agent execution.
Conducts comprehensive security audits of codebases to identify OWASP vulnerabilities, hardcoded secrets, and unsafe implementation patterns.
Enforces a strict Red-Green-Refactor workflow by requiring failing tests before any production code implementation.
Facilitates collaborative research ideation and hypothesis generation for scientists and academic researchers.
Generates visually engaging, research-backed slide decks and presentations for academic conferences, seminars, and thesis defenses.
Conducts systematic, high-rigor peer reviews of scientific manuscripts and grant proposals across all major research disciplines.
Evaluates research papers and scholarly work using the ScholarEval framework to provide structured quality assessments and actionable feedback.
Implements professional machine learning workflows in Python including classification, regression, clustering, and data preprocessing.
Automates electronic lab notebook management through the LabArchives REST API for research documentation, data backups, and tool integration.
Reads, manipulates, and writes genomic datasets including BAM, VCF, and FASTA files using a Pythonic interface to htslib.
Evaluates the rigor of scientific research by analyzing methodology, statistical validity, and potential biases using standardized frameworks like GRADE and Cochrane.
Performs rigorous statistical modeling, econometric analysis, and time series forecasting using the Statsmodels library.
Accesses the comprehensive BRENDA enzyme database to retrieve kinetic parameters, biochemical reactions, and metabolic pathway data.
Accesses the European Nucleotide Archive (ENA) to retrieve DNA/RNA sequences, raw reads, and genome assemblies for bioinformatics pipelines.
Generates publication-quality statistical graphics and complex data visualizations using a high-level, dataset-oriented Python interface.
Accesses and analyzes over 200 million AI-predicted protein structures for bioinformatics and structural biology research.
Facilitates advanced molecular analysis and cheminformatics workflows including property calculation, substructure searching, and 3D coordinate generation.
Accesses and queries the Catalogue of Somatic Mutations in Cancer (COSMIC) to retrieve high-quality genomic data for precision oncology and cancer research.
Queries the ChEMBL database to retrieve bioactive molecule data, drug targets, and bioactivity measurements for medicinal chemistry.
Accesses the NIH Metabolomics Workbench API to retrieve metabolite structures, study metadata, and standardized chemical nomenclature for biomarker discovery.
Facilitates drug discovery and therapeutic machine learning with curated datasets, ADMET benchmarks, and molecular optimization oracles.
Automates R&D data management by integrating Claude with the Benchling platform for biological entity tracking, inventory control, and lab notebook documentation.
Queries the PubChem database to retrieve chemical properties, perform structure searches, and access bioactivity data for over 110 million compounds.
Streamlines genomics pipeline development and data management on the DNAnexus cloud platform using the dxpy Python SDK.
Automates laboratory liquid handling workflows by writing Python-based Protocol API v2 scripts for Opentrons Flex and OT-2 robots.
Manipulates, creates, and analyzes Microsoft Word documents with professional-grade tracked changes and scientific schematic integration.
Accesses USPTO APIs to perform comprehensive patent and trademark searches, analyze prosecution history, and track intellectual property assignments.
Generates publication-quality scientific figures and multi-panel layouts using Python libraries while adhering to journal-specific standards.
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