Skill UI

This guide provides production-ready patterns for integrating the Speak language learning API. It covers managing conversation sessions, performing pronunciation assessment, preprocessing audio files using ffmpeg, implementing robust retry mechanisms (with backoff), and tracking user learning progress. Use this when developing a full-featured, high-reliability language learning application or troubleshooting complex API integrations.

A comprehensive set of specialized tools for deep interaction with source codebases. This context-mode is mandatory for advanced tasks such as running code snippets, analyzing files, batch processing repository commands, indexing knowledge, searching content, and fetching web data. It ensures that interactions are context-aware and highly efficient, moving beyond basic file listing or general chat prompts.

Generate structured specification files optimized for Generative AI consumption. Define requirements, constraints, and interfaces clearly using standardized Markdown templates. Ensure machine-readability and self-containment for effective AI processing and implementation.

Learn how to implement and handle Figma Webhooks V2 for real-time integrations. This guide covers setting up the webhook endpoint, processing various event types (like file updates, comments, and library publishes), verifying payloads using passcodes, and implementing best practices such as idempotency for robust, event-driven automation workflows.

This skill provides advanced techniques to optimize interactions with the Hootsuite API. It addresses common performance bottlenecks by implementing efficient LRU caching for social profiles, optimizing message scheduling through concurrent batch processing, and ensuring robust connection reuse. Use this when experiencing slow API responses or needing to process high volumes of automation tasks efficiently.

This reference architecture provides a production-ready, layered structure for integrating external applications with Klaviyo. It outlines best practices for building robust marketing automation systems, including profile synchronization (bidirectional sync), event-driven webhook handlers, background job processing, and implementing middleware like rate limiters. Use this when designing new data pipelines, reviewing project structure, or establishing standardized data synchronization standards for email/SMS campaigns.

A comprehensive guide to setting up and processing Klaviyo webhooks. This skill covers creating webhook subscriptions via the API, implementing crucial HMAC-SHA256 signature verification for security, and building a robust event handler. Learn how to manage different topic types (e.g., profile updates, list changes) and ensure idempotency, making your application reliable for event-driven marketing integrations.

This guide provides a comprehensive workflow for building robust ETL data pipelines within Palantir Foundry. It teaches how to utilize Python transforms, including the @transform_df decorator, for dataset-to-dataset processing. Key topics covered include multi-input joins, implementing incremental updates for efficiency, low-level file I/O, and configuring Spark resources for heavy computations. Essential for data engineers working with complex data modeling in Foundry.

This router entry point is designed for comprehensive testing of file access, download endpoints, and file upload workflows. Use this guide when validating file paths, detecting Local File Inclusion (LFI), or analyzing vulnerabilities in preview, storage, or extraction processes. It helps categorize whether the security issue relates to path traversal or insecure data processing chains.

A comprehensive, expert playbook for challenging insecure file upload mechanisms. It provides advanced attack vectors covering validation bypass (magic bytes, extension), storage path abuse (traversal, overwrite), and exploitation of post-upload processing chains (XXE, RCE, CMDi). Essential for deep security auditing of file-handling workflows.

This skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).

Implements a robust caching strategy by using the SHA-256 hash of file content as the primary key, instead of the file path. This pattern is essential for high-cost file processing pipelines (like OCR, PDF parsing) because it ensures cache hits survive file renaming or moving, while still automatically invalidating entries when the content changes. It adheres to the Single Responsibility Principle (SRP) by isolating caching logic into a service layer wrapper.