Skill UI

This tool integrates advanced AI capabilities into the entire Git lifecycle within the IDE. It assists developers by automatically generating standardized commit messages from staged diffs, providing deep code review using the @Git context (checking for vulnerabilities, missing handling), and offering systematic guidance for resolving complex merge conflicts. It streamlines version control and elevates code quality.

A comprehensive guide for diagnosing and fixing common integration errors with Customer.io. It covers critical topics such as API status codes (400, 401, 429), authentication failures, incorrect timestamp formatting, and best practices for event tracking and transactional message delivery. Use this resource when troubleshooting campaign failures or API calls failing.

This skill implements core Customer.io platform features, enabling sophisticated customer communication workflows. Key functionalities include sending transactional messages (e.g., receipts, password resets) and push notifications, executing API-triggered broadcasts to defined segments, and managing user attributes to dynamically drive segment membership. Ideal for building advanced, data-driven marketing automation and customer lifecycle management systems.

This comprehensive guide identifies and explains the most common anti-patterns and mistakes when integrating with Customer.io. It covers crucial development topics, including proper API key usage, Unix timestamp formatting, ensuring correct event tracking order (Identify before Track), and optimizing API calls for performance. Use this as a definitive checklist for code review and developer onboarding.

This skill provides advanced engineering patterns to dramatically improve the performance and reliability of Customer.io API integrations. It implements critical optimizations such as HTTP connection pooling, LRU deduplication caching, parallel batch processing, and fire-and-forget async tracking. Use this when integrating Customer.io into high-throughput, mission-critical systems to reduce latency from seconds to milliseconds.

Guide for automating Customjs tasks through Composio’s toolkit using the Rube MCP interface, covering connection checks, tool discovery, execution calls, and schema compliance while emphasizing repeated tool searches.

This comprehensive diagnostic bundle collects critical evidence from the Databricks environment, including environment setup, cluster state, job run history, and Delta table diagnostics. It generates a redacted, safe tar.gz file specifically designed for submitting detailed support tickets and efficiently troubleshooting complex data pipeline issues.

A comprehensive runbook designed for on-call engineers to manage Databricks incidents, outages, and major job failures. It provides structured procedures for immediate triage, decision tree guidance (covering cluster failures, code errors, and data quality issues), and steps for evidence collection and postmortem documentation, ensuring rapid and systematic incident resolution.

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 guide and implementation pattern for managing rate limits, throttling, and ensuring idempotency when interacting with the Databricks API. This skill demonstrates techniques like exponential backoff with jitter, token-bucket rate limiting, and using idempotency tokens to make bulk and concurrent API calls reliable and efficient, minimizing HTTP 429 errors.

This skill provides comprehensive methods to optimize Deepgram API usage and manage costs. It implements budget-aware transcription pipelines that preemptively check projected spending against set limits, utilizes audio preprocessing techniques (like silence removal via ffmpeg) to minimize billable minutes, and includes API calls to monitor current usage and spending against defined budgets. Ideal for cost-sensitive, production-grade deployments.

This guide implements Deepgram's callback and webhook mechanism for handling asynchronous audio transcription. It provides a complete workflow, including submitting audio for background processing, setting up a secure webhook endpoint (with HMAC signature verification), and managing job status using Redis. Essential for integrating robust, delayed voice processing results into a production backend system.