Agent skills for Zed

Bootstrap a reproducible LLM Observability experiment through the Python ddtrace SDK or the Node dd-trace SDK.

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, advanced prompt engineering, LLM costs, quality evals and…

Pydantic-AI agents, RAG, embeddings for Pulse Radar knowledge extraction.

LLM streaming response patterns. Use when implementing real-time token streaming, Server-Sent Events for AI responses, or streaming with…

Get reliable JSON, enums, and typed objects from LLMs using response_format, tool_use, and schema-constrained decoding across OpenAI…

Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests…

Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content.

Extracts procedural content from project docs into slash commands.

Reviews skills (D1-D11 + M1-M6 criteria) or .claude/commands for quality.

Bootstraps projects to production-ready structure.

Upgrades npm/yarn/pnpm dependencies with breaking change handling

Coordinates project structure migration to Clean Architecture

Scaffolds new React projects or restructures monoliths to component-based architecture.

Generates .NET Clean Architecture backend structure from entity definitions.

Migrates mock data from Drizzle ORM schemas to C# MockData classes

Universal platform artifact cleaner: Replit, StackBlitz, CodeSandbox, Glitch

Sets up Docker, CI/CD, and environment configuration with auto-detection.

Generates Docker and docker-compose configuration for multi-container development

Configures environment variables and secrets protection.

Configures Husky, lint-staged, commitlint, and Python pre-commit hooks.

Sets up logging, error handling, CORS, health checks, and API docs.