Skill UI

This guide covers essential security best practices for utilizing Together AI's OpenAI-compatible API for inference, fine-tuning, and model deployment. It provides code examples and architectural advice on secure API key management, robust webhook signature verification, input validation using schemas (like Zod), and implementing data redaction pipelines to protect sensitive information in logs and datasets. Essential for building secure AI applications.

This guide provides fundamental security and technical guidance for integrating with Veeva Vault. It covers REST API authentication (OAuth 2.0, session-based), executing VQL queries, and utilizing the VAPIL Java SDK. It is an essential resource for developers working with Veeva Vault for clinical operations, document management, and CRM systems.

This utility collects comprehensive diagnostic evidence necessary for troubleshooting Webflow integrations and API usage. It automatically gathers crucial data points into a single, support-ready diagnostic bundle. Key metrics include the Webflow SDK version, API token validation status, rate limit headers, site connectivity reports, CMS health checks, and recent error logs, significantly streamlining the technical support process.

Implement a comprehensive observability stack for Webflow Data API v2 integrations. This skill provides structured code using Prometheus metrics, OpenTelemetry distributed tracing, and structured logging (pino) to monitor API call frequency, latency, error rates, and rate limit usage. Ideal for developers building robust, production-grade Webflow-connected SaaS applications that require deep performance insights and reliable alerting.

This guide details comprehensive security best practices for Webflow Data API v2. It covers crucial topics such as selecting the correct token type, implementing least privilege access scopes, secure secret rotation, and verifying webhook signatures. Use this knowledge when building or auditing Webflow integrations to ensure robust data security and system reliability.

The official SDK for integrating Webiny's Headless CMS and File Manager into external applications (e.g., Next.js, Vue, Node.js). It provides a robust TypeScript interface for reading, writing, updating, and managing content entries. Features include listing, filtering, sorting, handling content revisions (read vs. preview mode), and ensures type safety using generics, making complex data operations reliable and scalable.

This comprehensive guide provides developers with best practices and detailed instructions for integrating the Wispr Flow voice-to-text API. It covers critical topics such as managing rate limits, choosing between WebSocket streaming and REST endpoints, handling common error codes (e.g., 401 Unauthorized), and optimizing context awareness, particularly for programming code and developer jargon. Essential for building reliable, low-latency voice-powered applications.

This pattern provides comprehensive guidance for integrating the Wispr Flow voice-to-text API. It covers best practices for both low-latency WebSocket streaming and simpler REST API usage. Key topics include authentication, recommended audio formats, robust error handling, and context awareness (e.g., understanding code or CLI commands), making it ideal for building advanced voice-powered applications.

This guide provides essential security and technical guidance for integrating Wispr Flow's voice-to-text API. It covers best practices for authentication (API keys, access tokens), utilizing streaming methods like WebSockets, handling common API errors, and optimizing context awareness for high-accuracy transcription in various applications.

This guide provides comprehensive security and integration details for the Workhuman Social Recognition and rewards API. Learn how to implement OAuth 2.0, manage peer-to-peer nominations, and sync data bidirectionally with major HRIS platforms like Workday and SAP SuccessFactors. Use cases include building custom recognition workflows and integrating with popular tools like Microsoft Teams and Slack.

This powerful compiler ingests heterogeneous research inputs, including PDF papers, code repositories, experiment logs, and raw notes. It transforms this diverse data into a comprehensive, structured Agent-Native Research Artifact (ARA). The system extracts core knowledge atoms, builds cognitive maps (claims, concepts, heuristics), and generates a verifiable, executable knowledge package for advanced AI consumption and research iteration.

This tool functions as a post-task epilogue, designed to audit and record the entire research journey. It scans the conversation history at the end of a coding or research session to extract critical research artifacts, including decisions, experiments, dead ends, claims, heuristics, and major pivots. It systematically writes this provenance data into the structured `ara/` directory, ensuring a faithful and auditable trace of how the project truly evolved.