productivity▌

Static type checking for React code across DOM and native platforms. \n \n Four renderer options cover standard DOM, browser-specific code, React Native, and Fabric environments \n Run yarn flow with optional renderer argument; use yarn flow-ci for comprehensive but slower checks \n Reports type errors with file locations to help identify and fix issues quickly \n Watch for common pitfalls: missing renderer specification, unexamined $FlowFixMe suppressions, and incorrect type imports \n

LaminDB is an open-source data framework for biology designed to make data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). It provides a unified platform that combines lakehouse architecture, lineage tracking, feature stores, biological ontologies, LIMS (Laboratory Information Management System), and ELN (Electronic Lab Notebook) capabilities through a single Python API.

PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute complete workflows from data loading through result interpretation, including single-factor and multi-factor designs, Wald tests with multiple testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData.

Transforms surface-level reading into deep learning through systematic analysis using 10+ proven thinking frameworks. Guides users from understanding to application through structured workflows.

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Welcome! This skill helps new users discover the most relevant Axiom skills for their situation.

Project-specific context for conversations through curated documentation, code, and team instructions. \n \n List and load Copilot Spaces using MCP read-only tools; create, update, and delete spaces via GitHub REST API with gh api \n Spaces contain attached repositories, files, documentation, and custom instructions that ground Copilot responses in actual project knowledge \n Use spaces to answer architecture and standards questions, generate code following team conventions, or execute multi-ste

sentry-workflow

Comprehensive AI-powered research with multi-source synthesis and citations. \n \n Produces structured reports grounded in web sources, taking 30-120 seconds depending on model selection (mini for targeted queries, pro for complex comparisons) \n Supports multiple output formats: markdown reports, JSON with custom schemas, and configurable citation styles (numbered, MLA, APA, Chicago) \n Includes async workflow for long-running research via --no-wait , status , and poll commands, plus real-time

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