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Found 2,795 Skills
Perl testing patterns using Test2::V0, Test::More, prove runner, mocking, coverage with Devel::Cover, and TDD methodology.
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
Pipeline status check for GTM projects. Use when returning to a project mid-implementation, when unsure what step comes next, or to get a quick overview of what has been completed. Reads all GTM output files and shows which skills have run, current implementation coverage, and the recommended next step. No API calls, instant. Trigger on - "what step am I on", "gtm status", "where did I leave off", "what's been done", "check progress", "pipeline status".
Fixes broken line wrapping in Claude Code exported conversation files (.txt), reconstructing tables, paragraphs, paths, and tool calls that were hard-wrapped at fixed column widths. Includes an automated validation suite (generic, file-agnostic checks). Triggers when the user has a Claude Code export file with broken formatting, mentions "fix export", "fix conversation", "exported conversation", "make export readable", references a file matching YYYY-MM-DD-HHMMSS-*.txt, or has a .txt file with broken tables, split paths, or mangled tool output from Claude Code.
RED-GREEN-REFACTOR cycle with strict phase gates. Write failing test first, implement minimum code to pass, then refactor while keeping tests green. Use when implementing new features, fixing bugs with test-first approach, improving test coverage, or when user mentions TDD. Use for "TDD", "test first", "red green refactor", "write tests", or "implement with tests". Do NOT use for debugging existing failures (use systematic-debugging) or for refactoring without new tests (use systematic-refactoring).
Implement Syncfusion React Scheduler component for calendar, event scheduling, and appointment management. Use this when building scheduling systems, calendar applications, booking systems, or time management interfaces. Covers all scheduler views (Day, Week, Month, Timeline, Agenda, Year), data binding, resource scheduling, recurring events, CRUD operations, drag-and-drop scheduling, customization, accessibility, and advanced features.
Create and manage WPF TabControl (TabControlExt) components for organizing content into tabs. Use this skill when users need to implement tabbed interfaces, manage tab selection, configure close buttons, handle tab interactions (clicking, keyboard navigation), customize tab appearance (orientation, placement), or bind tab data. Covers tab creation via XAML/C#, tab item management, event handling, context menus, drag-and-drop reordering, and theming.
Guide users through defining their pricing strategy for an AI product or SaaS. Covers billing model selection (usage-based, subscription, hybrid), subscription tier pricing, credit/overage costs, real-time vs invoice billing trade-offs, existing PSP integration, custom currency vs fiat, and pricing dimensions. Ends with a personalised pricing strategy summary, MRR projection, visual output (HTML or PDF), and tool recommendations. Use when a user wants to define their pricing, figure out how to charge for their AI product, decide between billing models, understand the real-time vs invoice billing trade-off, or evaluate what tools to use for monetisation.
When the user wants to implement cross-docking operations, optimize transshipment, or reduce warehouse storage. Also use when the user mentions "crossdock," "transshipment," "flow-through distribution," "dock-to-dock," "consolidation center," or "break-bulk operations." For general warehouse design, see warehouse-design. For dock scheduling, see dock-door-assignment.
Go testing patterns for Gentleman.Dots, including Bubbletea TUI testing. Trigger: When writing Go tests, using teatest, or adding test coverage.
FLOW framework integration — evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "seo flow", "evidence-led SEO", "find leverage optimize win", or wants stage-specific SEO prompts.
Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.