bjornmelin skills

Expert guidance for Vercel's Streamdown library - a streaming-optimized react-markdown replacement for AI applications. Use when: (1) Rendering AI-generated markdown from useChat/streamText, (2) Building chat UIs with streaming responses, (3) Migrating from react-markdown to streaming-friendly rendering, (4) Configuring code blocks (Shiki), math (KaTeX), diagrams (Mermaid), (5) Handling incomplete markdown during AI streaming (remend preprocessor), (6) Customizing markdown styling with Tailwind/CSS variables, (7) Securing AI output with rehype-harden (link/image protocols). Triggers: Streamdown, streaming markdown, AI chat markdown, react-markdown replacement, AI Elements Response, incomplete markdown, remend, Shiki themes, Mermaid diagrams, KaTeX math, rehype-harden, isAnimating, markdown streaming.

Expert guidance for auditing, refactoring, and designing machine learning Jupyter notebooks with production-quality patterns. Use when: (1) Analyzing notebook structure and identifying anti-patterns, (2) Detecting data leakage and reproducibility issues, (3) Refactoring messy notebooks into modular pipelines, (4) Generating templates for ML workflows (EDA, classification, experiments), (5) Adding reproducibility instrumentation (seeding, logging, env capture), (6) Converting notebooks to Python scripts, (7) Generating experiment summary reports. Triggers on: ML notebook, Jupyter audit, notebook refactor, data leakage, experiment template, ipynb best practices, notebook to script, reproducibility.

Expert guidance for building AI agents with ToolLoopAgent (AI SDK v6+). Use when creating agents, configuring stopWhen/prepareStep, callOptionsSchema/prepareCall, dynamic tool selection, tool loops, or agent workflows (sequential, routing, evaluator-optimizer, orchestrator-worker). Triggers: ToolLoopAgent, agent loop, stopWhen, stepCountIs, prepareStep, callOptionsSchema, prepareCall, hasToolCall, InferAgentUIMessage, agent workflows.

Expert guidance for building chat UIs with AI SDK React hooks. Use when: (1) Building chatbots with useChat hook, (2) Implementing tool UIs with server/client execution, (3) Handling message persistence and stream resumption, (4) Creating generative UI with React components, (5) Integrating with Next.js/Node/Fastify/Nest backends, (6) Using useObject for streaming structured data. Triggers: useChat, useObject, useCompletion, chat UI, chatbot, generative UI, message persistence, @ai-sdk/react, UIMessage, tool parts, streaming UI, toUIMessageStreamResponse.

Architect-level Streamlit development for building, refactoring, debugging, testing, and deploying Streamlit apps (single-page or multipage) with correct rerun/state/caching/fragments, AppTest-based testing, custom components v2, safe theming/CSS, security-by-default, and Playwright MCP end-to-end automation.

Expert guidance for AI SDK Core: text generation, structured data, tool calling (tool/dynamicTool), MCP integration (createMCPClient, Experimental_StdioMCPTransport), embeddings/reranking, provider setup, middleware, telemetry, and error handling. Use when building with generateText/streamText, generateObject/streamObject, tools (needsApproval, strict, inputExamples, activeTools, toolChoice, experimental_context), embeddings (embed/embedMany/rerank), or MCP tools/resources/prompts/elicitation.

Architect-level guidance, workflows, and scripts for building agentic coding systems with OpenAI Codex. Use for Codex SDK (@openai/codex-sdk) threads + streaming JSONL events; Codex CLI automation (codex exec, output schema, JSONL, resume); MCP server usage (codex mcp-server) and dynamic tool integration; multi-agent orchestration with OpenAI Agents SDK (handoffs, gating, tracing); durable state, caching, and memory using SQLite; safe-by-default sandbox/approval patterns and execpolicy rules.

Architect-level development, audit, and migration of multi-agent systems using LangGraph (v1+) and LangChain (v1+). Use when building or refactoring supervisor/subagent architectures, orchestrator-worker workflows, routing/hand-offs, agentic RAG, memory (short + long-term), state + context engineering, guardrails + human-in-the-loop, MCP tool integration, observability (LangSmith/OpenTelemetry), deployment, and performance/cost optimization — or when migrating off deprecated patterns like `langgraph.prebuilt.create_react_agent` and libraries like `langgraph-supervisor(-py)`, LlamaIndex agents, CrewAI, Agno, or OpenAI Agents.

World-class pytest engineer for Python: write/refactor tests, fix flakiness, design fixtures/markers, add coverage, speed up suites (collection/runtime), and optimize CI (GitHub Actions sharding, xdist parallelism, caching). Use when asked about pytest best practices, pytest 9.x features (subtests, strict mode, TOML config), pytest plugins (xdist/cov/asyncio/mock/httpx), or test performance/CI tuning.

Production-ready Supabase integration patterns for Next.js/React/TypeScript applications. Use when working with Supabase for (1) SSR authentication with @supabase/ssr, (2) Database operations and migrations, (3) Row Level Security (RLS) policies, (4) Storage buckets and file uploads, (5) Realtime channels and presence, (6) Edge Functions with Deno, (7) pgvector embeddings and semantic search, (8) Vercel deployment and connection pooling, (9) CLI operations (type generation, migrations). Triggers on Supabase client setup, auth patterns, RLS policies, storage uploads, realtime subscriptions, Edge Functions, vector search, or Vercel+Supabase deployment.

World-class Vitest QA/test engineer for TypeScript + Next.js (local + CI performance focused)

Expert guidance for Zod v4 schema validation in TypeScript. Use when designing schemas, migrating from Zod 3, handling validation errors, generating JSON Schema/OpenAPI, using codecs/transforms, or integrating with React Hook Form, tRPC, Hono, or Next.js. Covers all Zod v4 APIs including top-level string formats, strictObject/looseObject, metadata, registries, branded types, and recursive schemas.

SOTA Docker/Compose architecture, implementation, refactor, and security hardening. Use when working on containerization tasks such as creating or rewriting Dockerfiles, docker-compose files, buildx/bake configs, .dockerignore, and CI pipelines for build/test/scan/publish; auditing existing container setups for security, correctness, size/perf, and best practices (least privilege, non-root, minimal images, pinned base images, BuildKit secrets, healthchecks); debugging Docker build/run issues; or designing dev vs prod compose workflows across services (DB/cache/queues) with correct networking, volumes, secrets, and resource limits.