frontend-development
Modern frontend development with Next.js 14, React 18, TailwindCSS, and TanStack Query. Use when building UI components, implementing pages, optimizing performance, or following Next.js App Router patterns.
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Modern frontend development with Next.js 14, React 18, TailwindCSS, and TanStack Query. Use when building UI components, implementing pages, optimizing performance, or following Next.js App Router patterns.
Create ASCII wireframes, user flow diagrams, and cross-cutting specifications for UI/UX visualization. Use PROACTIVELY when user mentions wireframe, UI design, UX flow, screen layout, screen design, user flow,...
Parallel team composition analysis using G-1 PERSONNEL. Deploy 6 probes for comprehensive roster assessment before complex task assembly.
Spawn PAI agents via MCP factory tool. Loads identity, injects RAG context, validates spawn chain, and executes via Task(). The bridge between MCP tools and Claude Code's agent spawning.
Analyze code structure, patterns, and complexity without making modifications. Use when reviewing code, planning refactoring, or conducting read-only code analysis.
Validate documentation requirements before creating a PR. Checks for scratchpad entries, CHANGELOG updates, tests, and plan files based on change type. Use before creating pull requests to ensure all documentat...
Reference for parallel agent deployment protocols (search-party, qa-party, plan-party)
Human verification checklist for generated schedules. Use when reviewing Block 10 or any generated schedule to ensure it makes operational sense. Covers FMIT, call, Night Float, clinic days, and absence handlin...
Invoke ARCHITECT Deputy for systems architecture, design decisions, and technical infrastructure work.
Rapid cross-domain assessment using 18-series SOF Operators. Deploy 7 probes for immediate situational awareness before task force assembly.
Solver kill-switch and progress monitoring for schedule generation. Use when aborting runaway solvers, monitoring long-running schedule generation, or integrating abort checks into solver loops.
Parallel 120-probe reconnaissance using G-2 RECON agents. Deploy 12 G-2s commanding 10 D&D-inspired probes each. Use for comprehensive codebase exploration.
Parallel metrics collection using G-6 SIGNAL. Deploy 6 probes to gather metrics from all sources for data-driven decisions.
CP-SAT constraint model for TAMC Family Medicine scheduling optimization. Use when building or debugging the OR-Tools solver for faculty/resident scheduling. Documents hard constraints, soft constraints, decisi...
Guide for creating effective Claude Code skills with proper YAML frontmatter, directory structure, templates, scripts, and best practices. Use PROACTIVELY when creating new skills, updating existing skills, lea...
Delta Live Tables (DLT) pipeline patterns and examples for building declarative, self-healing data pipelines with automatic quality enforcement and lineage tracking.
Modern deployment with Databricks Asset Bundles (DAB), supporting multi-environment configurations and CI/CD integration.
Bronze/Silver/Gold layer design patterns and templates for building scalable data lakehouse architectures. Includes incremental processing, data quality checks, and optimization strategies.
Unity Catalog governance patterns, permissions models, security best practices, and policy enforcement for enterprise data governance.
Data classification strategies, tagging frameworks, sensitivity levels, and automated classification patterns for Unity Catalog.
RBAC/ABAC implementation patterns, least privilege access, row-level security, column masking, and access review workflows.
Data product design patterns with contracts, SLAs, and governance for building self-serve data platforms using Data Mesh principles.
Delta Sharing configuration, monitoring, and recipient management for secure cross-organization data sharing.
Comprehensive data quality patterns using Great Expectations, DLT expectations, and custom validators for ensuring data reliability and trust.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。