Explore our collection of Agent Skills to enhance your AI workflow.
Generates testable, evidence-based scientific hypotheses and experimental designs from observations or literature.
Accesses the world's largest somatic mutation database for cancer research and precision oncology data retrieval.
Manipulates, analyzes, and visualizes phylogenetic and hierarchical trees for biological research and genomic data.
Accesses and analyzes comprehensive FDA regulatory data for drugs, medical devices, and food safety through the openFDA API.
Controls laboratory automation equipment through a unified Python interface for liquid handling, plate reading, and protocol simulation.
Performs advanced biological data analysis including sequence manipulation, phylogenetic tree construction, and microbial diversity metrics.
Accesses and queries the ClinicalTrials.gov API v2 to retrieve detailed medical study data, recruitment status, and eligibility criteria for clinical research.
Accesses the ZINC database of 230M+ purchasable compounds for drug discovery, virtual screening, and molecular analog searching.
Searches and retrieves life sciences preprints from the bioRxiv server using keywords, authors, date ranges, and categories.
Accesses global statistical data from the Data Commons knowledge graph to analyze demographics, economics, health, and environmental trends.
Queries and retrieves genomic data from NCBI Gene databases using E-utilities and the modern Datasets API.
Searches archived Claude Code conversations using semantic and text matching to retrieve past decisions, patterns, and facts.
Performs comprehensive hypothesis testing, regression analysis, and Bayesian statistics with automated assumption checking and APA-style reporting.
Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using Python's statsmodels library.
Dispatches multiple concurrent Claude agents to investigate and fix independent system failures or test errors simultaneously.
Facilitates the retrieval and analysis of over 200 million AI-predicted protein structures from the AlphaFold DB for biological research and drug discovery.
Randomly selects fair and unbiased winners from spreadsheets, lists, and Google Sheets for giveaways and contests.
Facilitates direct access to PubMed literature and the NCBI E-utilities API for advanced biomedical research and data extraction.
Integrates state-of-the-art machine learning models for NLP, computer vision, and audio tasks using the Hugging Face ecosystem.
Queries and interprets NCBI ClinVar data to evaluate human genetic variants and their clinical significance.
Provides a systematic framework for evaluating the methodology, statistics, and integrity of scientific manuscripts and grant proposals.
Executes complex implementation plans in controlled batches with built-in review checkpoints to ensure accuracy and alignment.
Performs advanced server-side AI code reviews with integrated web UI reporting via MCP.
Drafts, structures, and refines professional scientific manuscripts following IMRAD standards and journal-specific reporting guidelines.
Automates complex web interactions, authentication, and data extraction using natural language commands.
Automates an iterative implement-review-fix development cycle to ensure high-quality code through multi-level AI analysis.
Configures workspace environments to automatically prioritize established scientific research patterns, database access protocols, and package usage guidelines.
Queries the Open Targets Platform to identify therapeutic drug targets, evaluate disease associations, and analyze clinical trial data.
Streamlines the finalization of development tasks by verifying tests and providing structured options for merging, pushing, or cleaning up Git branches.
Orchestrates parallel code reviews across three randomly selected reasoning models to provide diverse, unbiased architectural perspectives.
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