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Found 501 Skills
Implement Syncfusion Angular Dropdown Tree component for hierarchical data selection with single or multiple values. Use this when selecting items from tree-structured hierarchies, enabling checkboxes for multi-selection, binding hierarchical data, customizing tree items with templates, or implementing parent-child dependent selection. Works with self-referential structures and remote OData/REST endpoints for category selectors and organizational interfaces.
How to access SuprSend documentation and get support. Includes docs site, LLM-friendly doc endpoints, in-app chat, AI copilot, Slack community, and email support.
Selects a base model and fine-tuning technique (SFT, DPO, or RLVR) for the user's use case by querying SageMaker Hub. Use when the user asks which model or technique to use, wants to start fine-tuning, or mentions a model name or family (e.g., "Llama", "Mistral") — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, validates technique compatibility, and confirms selections.
Validates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
Generates a Jupyter notebook that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
Generates a Jupyter notebook that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, and RLVR trainers, including RLVR Lambda reward function creation.
Generates a Jupyter notebook that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.
When the user wants to create, optimize, or audit the API introduction/overview page. Also use when the user mentions "API page," "API landing page," "/api page," "API overview," "developer landing," "API marketing," or "API for developers." Note: API documentation (endpoint reference) lives in docs; use docs-page-generator.
LeadMagic platform help — Email Finder (97% accuracy), Email Validation (catch-all detection), Mobile Finder, Profile Search, Personal Email Finder, Company Search (firmographics), Technographics, Company Funding, Employee Finder, Role Finder, Job Change Detector, Jobs Finder, Google/Meta/B2B Ads Search, REST API (19 endpoints), MCP Server (Claude/Cursor/Windsurf), CLI. Use when asking 'how do I use LeadMagic', 'LeadMagic API', 'LeadMagic email finder', 'LeadMagic mobile finder', 'LeadMagic company search', 'LeadMagic ads intelligence', 'LeadMagic MCP', 'LeadMagic vs Apollo', 'LeadMagic vs Clay'. Do NOT use for enrichment strategy across tools (use /sales-enrich), prospect list strategy across tools (use /sales-prospect-list), intent signal strategy across tools (use /sales-intent), or competitive intelligence strategy across tools (use /sales-compete).
한강홍수통제소 기반 현재 수위/유량을 관측소명 또는 관측소코드로 조회한다. 기본 경로는 k-skill-proxy의 han-river water-level endpoint다.
Create MCP servers using the C# SDK and .NET project templates. Covers scaffolding, tool/prompt/resource implementation, and transport configuration for stdio and HTTP. USE FOR: creating new MCP server projects, scaffolding with dotnet new mcpserver, adding MCP tools/prompts/resources, choosing stdio vs HTTP transport, configuring MCP hosting in Program.cs, setting up ASP.NET Core MCP endpoints with MapMcp. DO NOT USE FOR: debugging or running existing servers (use mcp-csharp-debug), writing tests (use mcp-csharp-test), publishing or deploying (use mcp-csharp-publish), building MCP clients, non-.NET MCP servers.
BFL FLUX API integration guide covering endpoints, async polling patterns, rate limiting, error handling, webhooks, and regional endpoints with Python and TypeScript code examples.