Juan Lamadrid
Community@juanlamadrid20 · New York, NY
Solutions Architect @ Databricks
Agent Skills by Juan Lamadrid
Showing 49 vetted skills indexed across 2 GitHub repositories.
using-git-worktrees
Create isolated Git worktrees for parallel development on separate branches.
databricks-spark-structured-streaming
Build Spark Structured Streaming pipelines on Databricks with Kafka and Delta Lake.
test-driven-development
Guide the Red-Green-Refactor cycle for Test-Driven Development.
refresh-databricks-skills
Clone an upstream repository and copy new Databricks skill directories.
systematic-debugging
Guide a four-phase process to identify root causes before implementing fixes.
spark-python-data-source
Develop custom Python-based Apache Spark data source connectors for batch and streaming workloads.
databricks-model-serving
Deploy MLflow models and AI agents to Databricks Model Serving endpoints.
databricks-dbsql
Guide Databricks SQL scripting, stored procedures, and advanced query features.
using-superpowers
Enforce mandatory skill invocation before AI agents respond to requests.
dispatching-parallel-agents
Dispatch independent tasks to separate agents for concurrent execution.
databricks-vector-search
Create and query Databricks Vector Search indexes for RAG applications.
databricks-genie
Create and query Databricks Genie Spaces using natural language.
databricks-synthetic-data-generation
Generate realistic synthetic data for Databricks using Python, Faker, and Spark.
executing-plans
Execute implementation plans in batched steps with review checkpoints.
finishing-a-development-branch
Verify tests and present structured options for merging or creating a pull request.
databricks-app-python
Develop Python-based Databricks apps with Dash, Streamlit, Gradio, Flask, FastAPI, or Reflex.
databricks-app-apx
Create full-stack Databricks applications with FastAPI and React.
databricks-zerobus-ingest
Ingest data into Databricks Delta tables via the Zerobus gRPC API.
brainstorming
Guide initial concepts into detailed project designs through structured dialogue.
databricks-mlflow-evaluation
Run MLflow GenAI evaluations with custom scorers and analyze traces.
writing-plans
Generate step-by-step implementation plans with file paths, code snippets, testing commands, and commit messages.
requesting-code-review
Dispatch a code-reviewer subagent to analyze Git commit changes against requirements.
databricks-lakebase-autoscale
Manage Databricks Lakebase Autoscaling projects, branches, compute, and credentials.
receiving-code-review
Manages code review feedback with technical verification and reasoned pushback.