sequential-think
Multi-step reasoning engine for complex analysis and systematic problem solving. Use when: (1) Complex debugging scenarios with multiple layers, (2) Architectural analysis and system design, (3) Problems requiring hypothesis testing and validation, (4) Multi-component failure investigation, (5) Performance bottleneck identification. Triggers: "--think", "--think-hard", "--ultrathink", "analyze step by step", "break down this problem", "systematic analysis". IMPORTANT: Do NOT use for simple single-step tasks.
geoparquet
Convert spatial data (GeoJSON, Shapefile, etc.) to optimized GeoParquet using the gpio CLI. Analyzes files, recommends settings, and publishes to cloud storage.
using-plan-and-execute
Use when starting any conversation - establishes mandatory workflows for finding and using skills, including using Read tool before announcing usage, following brainstorming before coding, and creating TodoWrite todos for checklists
creating-an-agent
Use when creating specialized subagents for Claude Code plugins or the Task tool - covers description writing for auto-delegation, tool selection, prompt structure, and testing agents
writing-claude-directives
Use when writing instructions that guide Claude behavior - skills, CLAUDE.md files, agent prompts, system prompts. Covers token efficiency, compliance techniques, and discovery optimization.
research-ideation
Generate structured research questions, testable hypotheses, and empirical strategies from a topic or dataset
diagram-reports-generator
This skill generates comprehensive diagram and MicroSim reports for the geometry course by analyzing chapter markdown files and creating table and detail reports. Use this skill when working with an intelligent textbook (specifically geometry-course) that needs updated visualization of all diagrams and MicroSims across chapters, including their status, difficulty, Bloom's Taxonomy levels, and UI complexity.
book-metrics-generator
This skill generates comprehensive metrics reports for intelligent textbooks built with MkDocs Material, analyzing chapters, concepts, glossary terms, FAQs, quiz questions, diagrams, equations, MicroSims, word counts, and links. Use this skill when working with an intelligent textbook project that needs quantitative analysis of its content, typically after significant content development or for project status reporting. The skill creates two markdown files - book-metrics.md with overall statistics and chapter-metrics.md with per-chapter breakdowns - in the docs/learning-graph/ directory.
reporting-dashboards
This skill should be used when the user asks to "create report", "dashboard", "chart", "visualization", "analytics", "scheduled report", "export data", or any ServiceNow reporting and dashboard development.
infographic-syntax-creator
Generate AntV Infographic syntax outputs. Use when asked to turn user content into the Infographic DSL (template selection, data structuring, theme), or to output `infographic <template>` plain syntax.
create-correlation-context
Generates Correlation ID propagation components for PHP 8.4. Creates PSR-15 middleware, Monolog processor, message bus header propagation, and CorrelationContext value object. Includes unit tests.
multi-chart-draw
支持多种图表类型的绘制工具，包括思维导图、流程图、数据可视化图表、数学函数图等；可根据用户需求生成 Mermaid、ECharts、Mindmap、DrawIO、GeoGebra 等格式的图表，并导出为 PNG、SVG、HTML 等格式
create-iterator
Generates Iterator pattern for PHP 8.4. Creates sequential access to aggregate elements without exposing underlying representation, with iterator interface and iterable collections. Includes unit tests.
aws-sdk-java-v2-dynamodb
Amazon DynamoDB patterns using AWS SDK for Java 2.x. Use when creating, querying, scanning, or performing CRUD operations on DynamoDB tables, working with indexes, batch operations, transactions, or integrating with Spring Boot applications.
langchain4j-testing-strategies
Testing strategies for LangChain4j-powered applications. Mock LLM responses, test retrieval chains, and validate AI workflows. Use when testing AI-powered features reliably.
langchain4j-ai-services-patterns
Build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered features with minimal boilerplate code in Java applications.