Skill UI

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

A comprehensive guide to setting up a minimal working example on Databricks. This skill covers creating a development cluster, uploading and executing notebooks (Python SDK), running tasks as jobs, and setting up a Serverless SQL Warehouse. It is ideal for new users or initializing a basic data engineering project.

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.

A comprehensive, AI-assisted methodology for diagnosing complex software errors in production environments. This skill guides users through the entire debugging lifecycle, from initial triage and gathering observability data (Sentry, DataDog, Jaeger) to generating hypotheses, selecting appropriate debugging strategies (Time-Travel, Chaos Engineering), and performing AI-powered root cause analysis. It culminates in proposing validated fixes and prevention measures.

Provides comprehensive guidance on building robust evaluation frameworks for complex agent systems. It addresses the inherent challenges of non-determinism and multiple valid paths, enabling systematic performance testing, validating context engineering choices, and measuring continuous improvements across multiple quality dimensions (e.g., factual accuracy, tool efficiency).

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

A comprehensive toolkit for monitoring the health and performance of Fireflies.ai integration. It tracks crucial metrics such as API connectivity, webhook delivery status, transcript processing latency, and seat utilization. Built using Prometheus metrics and suitable for Grafana dashboards, ensuring reliable data pipeline monitoring for engineering teams.

A comprehensive guide detailing the entire workflow for analyzing embedded firmware. It covers hardware-level extraction methods (UART, JTAG, SPI dump), filesystem analysis (SquashFS, JFFS2), binary reverse engineering using tools like Ghidra, and identifying common vulnerabilities (Buffer Overflow, Command Injection). Ideal for security researchers and embedded systems engineers.

Guides teams through engineering-as-marketing plans for free tools that generate leads, uplift SEO, and boost brand awareness, covering assessment, ideation, MVP scope, lead capture, SEO, build-vs-buy, and evaluation.

This guide details the comprehensive setup of Granola for large-scale, multi-departmental enterprise environments. It covers creating isolated workspaces (e.g., Engineering, HR, Sales), integrating Identity Providers using SSO/SCIM, and implementing granular compliance controls (data retention, external sharing, MFA) to ensure data security and departmental autonomy.

Provides expert guidance for GRPO training with TRL, covering dataset prep, reward engineering, structured outputs, and multi-objective alignment when preference pairs are scarce.