Skill UI

Implements automated policy enforcement and guardrails for Canva Connect API integrations. This system enforces best practices such as preventing hardcoded credentials, ensuring proper rate limit handling (429 responses), and validating robust OAuth configurations. It is essential for maintaining high code quality and security in CI/CD pipelines and pre-commit hooks.

A comprehensive guide to production-ready patterns for integrating with Anthropic's Claude API using both TypeScript and Python SDKs. Covers robust client setup (retries, timeouts), advanced error handling, streaming implementations, prompt caching, and batch processing to ensure high performance and cost efficiency in real-world AI applications.

Utilize Anthropic Message Batches API for high-volume, asynchronous bulk processing of AI tasks. This method allows processing up to 10,000 requests per batch, ideal for summarizing large document sets or running repetitive tasks where immediate, synchronous results are not required. The process involves creating the batch, polling for status completion (within 24 hours SLA), and then retrieving detailed results, including success and error statuses for each item.

A comprehensive guide to designing high-performance schemas in ClickHouse. Learn how to select the optimal MergeTree engine (e.g., ReplacingMergeTree, AggregatingMergeTree), structure the ORDER BY key for maximum query efficiency, and implement effective partitioning strategies for large-scale analytical workloads and ETL processes.

This comprehensive guide provides advanced techniques to diagnose and significantly boost ClickHouse query performance. It covers analyzing slow queries using system tables, optimizing ORDER BY keys, implementing data skipping indexes (Bloom, Set, MinMax), utilizing projections for pre-aggregation, and fine-tuning server settings and materialized views. Essential for high-volume OLAP environments.

Provides comprehensive strategies for handling external API rate limits (HTTP 429 errors). This skill implements advanced patterns including exponential backoff with jitter, request queuing, and header monitoring. It is crucial for building reliable, high-throughput integrations with rate-limited services like ClickUp.

This skill provides a comprehensive framework for conducting high-quality code reviews. It guides developers through checking code across four critical dimensions: Correctness (functional accuracy and edge cases), Maintainability (readability and documentation), Performance (efficiency and resource usage), and Security (input validation and vulnerability management). It also emphasizes providing actionable, constructive, and empathetic feedback.

This guide details advanced patterns for managing API rate limits, particularly for external AI services like Cohere. It covers implementing exponential backoff with jitter for handling transient 429 errors, utilizing concurrency-limited request queues, and building proactive rate limit trackers. These techniques ensure highly reliable and optimized API throughput in production systems.

A sophisticated tool for comparing two versions of a legal contract side-by-side. It detects all added, removed, and modified clauses, classifying changes by type and severity. Crucially, it performs favorability analysis, determining which party (Party A or Party B) each change strategically favors, and alerts the user to dangerous patterns like IP scope creep or indemnification drift, making it essential for high-stakes contract negotiations.

This guide details how to set up and execute large-scale, distributed GPU training jobs on the CoreWeave platform. It covers both single-node multi-GPU setups and multi-node training utilizing PyTorch DDP and Kubernetes resources (A100/H100). This workflow is ideal for fine-tuning large language models (LLMs) or running complex deep learning models that require high-performance, shared-storage compute clusters.

This skill provides comprehensive observability for CoreWeave's GPU-intensive workloads running on Kubernetes. It monitors critical operational metrics, including GPU utilization, memory usage, inference latency (p99), and job completion rates. Use it for setting up DCGM metrics dashboards, configuring proactive alerts for underutilization or Out-Of-Memory (OOM) conditions, and ensuring high reliability for AI/ML deployments.

This skill provides methods for managing GPU quota limits and rate restrictions for CoreWeave cloud services. It includes using `kubectl` to check resource quotas and demonstrates implementing robust request queuing mechanisms using Python's `asyncio` library, ensuring stable and efficient resource allocation for high-concurrency inference endpoints.