Unified access to Azure AI services: Search, Speech, OpenAI, and Document Intelligence. AI Search supports full-text, vector, hybrid, and semantic search with AI enrichment capabilities like entity extraction and OCR Speech service enables speech-to-text transcription (real-time and batch), text-to-speech with neural voices, speaker diarization, and custom models MCP server integration provides direct tool access via azure__search and azure__speech commands; falls back to CLI and SDK when MCP is unavailable Includes OpenAI model access, DALL-E image generation, embeddings, and Document Intelligence for form extraction and OCR
Quick reference for ADK Python patterns: agents, tools, callbacks, and state management. Covers agent creation with model and instruction configuration, basic tool definition via FunctionTool, and callback patterns for state initialization and lifecycle hooks Includes built-in tool imports, agent orchestration (SequentialAgent, ParallelAgent, LoopAgent), and state management through CallbackContext ADK 2.0 Workflow API available as opt-in experimental feature for graph-based pipelines with conditional routing and parallel processing; requires Python 3.11+ and explicit user consent before use Requires an existing scaffolded project; use agents-cli scaffold create or scaffold enhance before writing agent code
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini Enterprise", "publish to Gemini…
End-to-end AI Runway setup on AKS from bare cluster to running model deployment. Walks through six sequential steps: cluster verification, controller installation, GPU assessment, inference provider setup, first model deployment, and summary Includes cost awareness warnings for GPU node pools and error handling for common deployment failures Supports resuming from any step via skip-to-step N argument if setup is partially complete Uses only kubectl and make CLI tools; no MCP tools required
Coordinate supervised Orca workers: threaded messages, blocking ask/reply, task dispatch, worker_done/escalation waits, task DAGs, decision gates, coordinator…
Claude API integration for building LLM-powered applications across Python, TypeScript, Java, Go, Ruby, C#, and PHP. Defaults to Claude Opus 4.6 with adaptive thinking and streaming; supports tool use, structured outputs, batches, and file uploads through a single /v1/messages endpoint Language detection automatically routes you to the correct SDK documentation; includes decision trees for choosing between single API calls, workflows with tool use, and agentic loops Tool runner (beta in most languages) handles automatic loop execution; manual loops available for fine-grained control over approval gates, logging, and conditional execution Agent SDK (Python and TypeScript only) provides built-in file, web, and terminal tools with permissions, MCP support, and safety guardrails; Claude API is the right choice for custom agent tools
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
Generate comprehensive Product Requirements Documents that translate business vision into technical specifications. Follows a strict three-phase workflow: discovery interview to fill knowledge gaps, analysis and scoping to identify dependencies, and technical drafting using a standardized PRD schema Requires concrete, measurable success criteria and acceptance criteria; explicitly avoids vague language like "fast" or "intuitive" in favor of quantifiable benchmarks Covers executive summary, user personas and stories, technical architecture, AI system requirements with evaluation strategies, and phased rollout planning with risk analysis Includes discovery questions on core problems, success metrics, and constraints before generating any document; presents drafts for iterative feedback on specific sections
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent…
One API and one credential for frontier and open-source LLMs, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, add…
Add Clerk authentication to Expo and React Native apps using @clerk/expo. Use for Expo setup, prebuilt native components (AuthView, UserButton), custom…
Core package for defining schemas, catalogs, and AI prompt generation for json-render. Use when working with @json-render/core, defining schemas, creating…
Comprehensive safety analysis and improvement framework for AI prompts with detailed assessment methodologies. Evaluates prompts across eight dimensions: safety, bias detection, security, effectiveness, best practices compliance, pattern analysis, technical robustness, and performance optimization Provides structured analysis reports with risk scoring, critical issue identification, and strength assessment across all evaluation criteria Delivers improved prompt versions with specific enhancements, safety measures, bias mitigation strategies, and security hardening recommendations Includes comprehensive testing frameworks covering standard test cases, edge cases, safety testing, and bias validation with expected outcomes Offers educational insights explaining prompt engineering principles applied, common pitfalls avoided, and responsible AI best practices from industry leaders
Iterative evaluation and refinement patterns for improving AI agent outputs through self-critique loops. Provides three core patterns: basic reflection (self-critique loops), evaluator-optimizer (separated generation and evaluation), and code-specific test-driven refinement Supports multiple evaluation strategies including outcome-based assessment, LLM-as-judge comparison, and rubric-based scoring with weighted dimensions Includes practical Python implementations with structured JSON output parsing, iteration limits, and convergence detection to prevent infinite loops Best suited for quality-critical tasks like code generation, reports, and analysis where clear evaluation criteria and success metrics exist
Structured PRD generation prompt for breaking down Epics into detailed feature specifications. Generates complete Product Requirements Documents in markdown format with standardized sections covering goals, user personas, stories, functional and non-functional requirements, and acceptance criteria Prompts for clarifying questions when insufficient information is provided, ensuring comprehensive feature definition before engineering handoff Organizes output into a consistent file structure ( /docs/ways-of-work/plan/{epic-name}/{feature-name}/prd.md ) for centralized documentation management Includes explicit out-of-scope definition to prevent scope creep and establish clear feature boundaries