Skill UI

A powerful cognitive tool that moves beyond surface-level brainstorming. It forces users to systematically deconstruct complex problems down to their 'irreducible truths.' The skill identifies 8-12 hidden assumptions (Surface, Middle, Deep), classifies them (Fact, Convention, Belief, Interest-Driven), and ranks them by Fragility × Impact. This process allows users to stress-test their premises and rebuild conclusions based only on verified realities, making it invaluable for major life decisions or business hypothesis testing.

This skill provides a comprehensive guide to implementing and managing BambooHR webhooks. It covers both global and permissioned webhook types, detailing the process from API setup to robust payload handling. Key security aspects include HMAC-SHA256 signature validation for ensuring data integrity and authenticity. Ideal for developers needing to build real-time employee synchronization triggers, process employee creation, updates, or deletions, and integrate BambooHR data into other enterprise systems.

This comprehensive guide details the end-to-end process for integrating Bright Data into development projects. It covers gathering zone credentials, setting up environment variables, downloading necessary SSL certificates, and configuring both Node.js and Python environments for robust and reliable web scraping proxy authentication.

This comprehensive checklist guides the secure and systematic deployment of web scraping integrations using Bright Data. It covers critical stages including production zone verification, code quality checks (e.g., rate limiting, error logging), infrastructure setup, robust monitoring configuration, and safe gradual rollout procedures, ensuring reliable, mission-critical data collection processes into production.

This comprehensive guide details how to implement robust webhook endpoints to receive, validate, and process data deliveries from Bright Data's Web Scraper and Datasets APIs. Learn best practices for handling large JSON/NDJSON payloads, verifying authorization headers, processing status notifications, and ensuring data deduplication in automated scraping pipelines.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.

This guide details how to set up robust CI/CD pipelines for Canva Connect API integrations using GitHub Actions. It covers unit testing with MSW mocks and full integration testing, including secure secret management and automatic token refreshing workflows. Use this when automating Canva API tests in your build process to ensure reliable deployments.

This comprehensive workflow manages the entire Canva creative pipeline. Users can upload, read, and modify design assets (images, files) via API. Key features include autofilling brand templates using dynamic data (text, images, charts) and programmatically organizing generated designs into specific folders, significantly streamlining the content creation process.

This advanced guide covers core Anthropic workflow patterns, specifically focusing on tool use (function calling) and building complex, agentic loops. It provides detailed instructions on defining tools using JSON Schema, executing tool calls, and managing the iterative process required for building sophisticated, AI-powered agents using the Anthropic API.

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.