Skill UI

This skill provides comprehensive scripts and workflows to automatically identify and remove unused AWS resources across multiple services (EBS, S3, EC2, EIPs). By automating the cleanup of orphaned volumes, old snapshots, and stale data, users can eliminate cloud waste, significantly optimizing their AWS expenditure and ensuring a cost-efficient infrastructure.

A comprehensive guide and set of scripts for optimizing Customer.io spending. It details strategies for auditing profile counts, suppressing/deleting inactive users, deduplicating high-volume events, and implementing event sampling to reduce billing costs and maintain data hygiene.

A comprehensive guide to production-ready patterns for the Databricks Python SDK. This skill covers essential best practices including singleton client initialization, structured error handling with typed exceptions, context-managed cluster lifecycle control (ensuring cleanup), and implementing type-safe job definitions. It is essential for building robust, maintainable, and idiomatic integrations with the Databricks platform.

Comprehensive guide for setting up a complete local development stack for Documenso integrations. It covers project structure, environment variable configuration, running self-hosted services using Docker (PostgreSQL, MailHog), and implementing utility scripts for connection verification and test data cleanup.

This skill helps manage and optimize resource usage and API costs within Evernote integrations. It provides utilities for monitoring monthly upload quotas, compressing images, resizing large files, and performing storage cleanup to prevent exceeding account limits and ensuring efficient data handling.

This skill provides comprehensive methods for managing the entire data lifecycle of LLM observability platforms like Langfuse. Functions include exporting detailed traces and scores, configuring retention policies (e.g., automatic data cleanup), handling GDPR subject requests (access and erasure), and anonymizing Personally Identifiable Information (PII) for secure analytics. Essential for maintaining regulatory compliance in AI applications.

Summarizes React’s official guidance on when to use or avoid useEffect by highlighting derived state handling, expensive calculations, subscriptions, and cleanup strategies for data fetching.

Automates environment prep, service orchestration, and cleanup for executing integration test suites, covering database setup, migrations, and optional coverage reports.

This guide provides comprehensive, actionable methods to audit and significantly reduce Supabase operational costs. It covers advanced database tuning (vacuuming, index cleanup, bloat management), efficient storage lifecycle management, connection pooling strategies, and optimal plan selection. Use this when analyzing high billing reports, identifying resource waste, or implementing robust cost-saving measures in a cloud environment.

A comprehensive guide to managing training data and model artifacts securely on cloud GPU instances (e.g., Vast.ai). It covers best practices for data transfer (SCP, compressed, cloud staging), implementing AES-256 encryption, managing checkpoints to S3, and performing secure cleanup before instance destruction, ensuring compliance and data integrity.

This comprehensive guide outlines security best practices for utilizing Vast.ai's cloud environment. It covers secure API key management, hardening SSH access to GPU instances, implementing proper data cleanup before instance destruction, and adhering to robust network security protocols. Use this when setting up, auditing, or deploying sensitive AI/ML workloads on shared cloud hardware.

A specialized tool for auditing source code (C/C++/Rust) to ensure sensitive data (keys, passwords, PII) is properly zeroized. It analyzes memory handling at the assembly and IR level to detect if compiler optimizations, such as dead-store elimination, have accidentally removed or weakened the secure cleanup process. Essential for maintaining security in critical codebases.