Results for “tidb”

Manage biological datasets and models with LaminDB, an open-source lineage-native lakehouse. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation, collections, branches, storage, and workflow integrations.

Manages a local GitHub knowledge base and searches GitHub via the gh CLI, covering repos, issues, and pull requests.

Intercepts responses to let users choose the depth and token budget before answering, with heuristic token estimation and preset depth levels.

Queries Belgian railway (NMBS/SNCB) schedules via the iRail CLI, including departures, connections, train compositions, and service disruptions.

Esta habilidade deve ser usada ao trabalhar com LaminDB, um framework de dados de código aberto para biologia que torna dados consultáveis, rastreáveis, reproduzíveis e FAIR. Use ao gerenciar datasets biológicos (scRNA-seq, espacial, citometria de fluxo, etc.), rastrear workflows computacionais, curar e validar dados com ontologias biológicas, construir data lakehouses, ou garantir linhagem de dados e reprodutibilidade em pesquisa biológica. Aborda gerenciamento de dados, anotação, ontologias (genes, tipos de célula, doenças, tecidos), validação de esquema, integrações com orquestradores de workflow (Nextflow, Snakemake) e plataformas MLOps (W&B, MLflow), e estratégias de deployment.

High-performance graph analysis for beads issue tracker using 9 metrics (PageRank, Betweenness, HITS, Critical Path, etc). Provides AI-driven task prioritization, dependency analysis, and architectural health monitoring via robot protocol.

Use when the user explicitly enters TURBO, mode TURBO, enable TURBO, or TURBO for this task, and Codex should optimize for maximum quality without normal token-cost restraint.

Enforces a disciplined Red-Green-Refactor (TDD) workflow in TypeScript/Node.js. Use this whenever creating new features, fixing bugs, or migrating logic to ensure high-quality, verifiable implementations.

Refines an existing skill in place with minimal diffs, using evidence from usage and session mining to improve triggers, workflow, metadata, and resources.