Forrest Murray
Community@FMurray · Thurmont, MD
Explorer of full-stack things -- LLM Agents, Data Intensive Applications, Hardware Hacking. Aspiring to Software Craftsmanship.
Agent Skills by Forrest Murray
Showing 26 vetted skills indexed across 1 GitHub repositories.
FMurraydatabricks-docs
Fetches the Databricks llms.txt documentation index to look up APIs and concepts.
FMurrayanalyzing-mlflow-trace
Analyzes a single MLflow trace to debug errors and root-cause agent behavior.
FMurrayanalyzing-mlflow-session
Analyzes multi-turn MLflow chat sessions by reconstructing traces and assessments.
FMurraydatabricks-config
Configure Databricks profiles and authenticate for Databricks Connect, CLI, and SDK.
FMurraymodel-serving
Deploy and query MLflow models and GenAI agents on Databricks Model Serving endpoints.
FMurraysynthetic-data-generation
Generate realistic synthetic datasets with Faker and Spark and save them to Databricks Unity Catalog Volumes.
FMurrayspark-declarative-pipelines
Create and configure Databricks Lakeflow Spark Declarative Pipelines with SQL or Python.
FMurraydatabricks-python-sdk
Provides reference guidance and code examples for the Databricks Python SDK, Connect, CLI, and REST API.
FMurraysearching-mlflow-docs
Retrieves MLflow documentation pages and code examples from the official docs site.
FMurrayunstructured-pdf-generation
Generates synthetic PDF documents with RAG evaluation JSON files and uploads them to Unity Catalog Volumes.
FMurraydatabricks-genie
Create and query Databricks Genie Spaces for natural language SQL exploration.
FMurraymlflow-onboarding
Guides MLflow onboarding by detecting GenAI or traditional ML use cases and integrating tracing or tracking.
FMurraydatabricks-app-python
Build Python-based Databricks applications using Dash, Streamlit, or Flask frameworks.
FMurrayquerying-mlflow-metrics
Fetches aggregated trace metrics from MLflow tracking servers.
FMurrayagent-bricks
Create and manage Databricks Knowledge Assistants, Genie Spaces, and Multi-Agent Supervisors.
FMurraydatabricks-app-apx
Build full-stack Databricks applications using the APX framework with FastAPI and React.
FMurraydatabricks-jobs
Create, schedule, and monitor Databricks Jobs using Python SDK, CLI, or Asset Bundles.
FMurrayinstrumenting-with-mlflow-tracing
Instruments Python and TypeScript applications with MLflow Tracing for LLM observability.
FMurrayretrieving-mlflow-traces
Retrieves and filters MLflow traces by ID, status, session, tags, and execution time via CLI or Python API.
FMurrayasset-bundles
Create and configure Databricks Asset Bundles for multi-environment deployments.
FMurrayaibi-dashboards
Create Databricks AI/BI dashboards with validated SQL queries and structured JSON deployment.
FMurraylakebase-provisioned
Provision and connect to Databricks Lakebase managed PostgreSQL instances for OLTP workloads.
FMurrayagent-evaluation
Evaluates LLM agent output quality using MLflow datasets, scorers, and tracing.
FMurraydatabricks-unity-catalog
Query Unity Catalog system tables and manage volume file operations on Databricks.
Frequently Asked Questions About Forrest Murray
FAQPage SchemaWhat tasks can I accomplish with Forrest Murray's Databricks skills?▼
You can configure Databricks authentication, create and run jobs, build Asset Bundles for multi-environment deployments, provision Lakebase PostgreSQL for OLTP, query Unity Catalog system tables, generate synthetic data with Faker and Spark, and create AI/BI dashboards with validated SQL.
Who are these MLflow and Databricks skills designed for?▼
Data engineers, ML engineers, and GenAI developers working on the Databricks platform. They suit practitioners building pipelines, instrumenting LangChain/LangGraph/OpenAI agents with tracing, evaluating agent quality, or deploying conversational apps via Agent Bricks and Genie Spaces.
How do I analyze and debug MLflow traces with these skills?▼
Provide a trace ID to analyze a single trace, or a session ID for multi-turn chat debugging. You can retrieve traces filtered by status, tags, or latency, query aggregated metrics like token usage and costs, then tag traces or log feedback assessments after review.
What are the prerequisites for using these Databricks skills?▼
You need a Databricks workspace with configured profile authentication via Databricks Connect, CLI, or SDK. MLflow skills require a tracking server and logged traces; pipeline skills require serverless compute access; Genie and Agent Bricks require appropriate workspace entitlements.
Can I build full-stack applications on Databricks with these skills?▼
Yes. The databricks-app-python skill covers Dash, Streamlit, and Flask apps, while databricks-app-apx covers the APX framework combining FastAPI backends with React frontends, both deployable as Databricks applications.