Skill UI

Collects comprehensive diagnostic evidence for support tickets related to AssemblyAI. This utility automates the gathering of runtime environment details, SDK versions, API connectivity checks, and specific transcript statuses, greatly accelerating troubleshooting and issue resolution for speech-to-text applications.

A comprehensive diagnostic utility designed to collect all necessary evidence when troubleshooting Attio API connectivity issues or preparing a support ticket. It checks API authentication, object schema definitions, rate limit status, and general endpoint health using shell scripting and cURL.

A comprehensive utility designed to collect all necessary diagnostic evidence for troubleshooting BambooHR API issues or preparing detailed support tickets. It captures runtime environment information (Node.js, Python, OS), performs connectivity tests against core endpoints (e.g., employees, time off), and gathers redacted API response details, making it essential for efficient bug reporting.

This guide provides advanced, deep debugging techniques for complex Figma REST API issues that resist standard error handling. It covers full network tracing (cURL), validating unexpected response shapes (TypeScript), measuring rate limits, and safely chunking large files. Use this when standard troubleshooting fails, or detailed evidence is required for support escalation.

This utility collects comprehensive diagnostic evidence for troubleshooting Flexport API integration issues. It automatically captures API connectivity tests, authentication status, recent error logs, and platform status, packaging them into a secure, redacted bundle ready for support tickets. Ideal for developers encountering persistent API failures.

A utility designed to collect comprehensive diagnostic evidence for Framer development issues. It automatically gathers runtime environment details, installed package versions, API connectivity status, and necessary configuration files. Use this when preparing detailed support tickets or troubleshooting persistent bugs related to Framer plugins or server-side APIs.

This bundle collects comprehensive diagnostic evidence for troubleshooting Grammarly API integration issues. It captures API connectivity status, text check responses, authentication details, and rate limit headers. Use this when encountering persistent errors, preparing detailed support tickets, or performing automated health checks on Grammarly services.

This bundle collects comprehensive diagnostic evidence for Hootsuite operations. It gathers API connectivity status, social profile health, scheduled post status, and OAuth token validity into a single, portable archive. Use it when troubleshooting persistent issues like failed scheduling, authentication errors, or disconnected social accounts.

This structured skill guides users through root-cause-first debugging when an implementer is blocked, verification fails, or repeated attempts at fixing do not converge. It mandates a systematic approach combining local evidence inspection, runtime analysis, and external documentation research to accurately classify the failure's true source (e.g., missing dependency, spec conflict). It prevents speculative patching and generates a clear, actionable fix plan.

This comprehensive debug bundle collects all necessary diagnostic evidence from Lucidchart's API integrations. It systematically checks document access, data linking integrity, shape rendering, and export status by capturing API responses, page metadata, and rate limit information. It is essential for support engineers diagnosing complex integration failures without needing direct account access.

This comprehensive bundle collects all necessary diagnostic artifacts for troubleshooting Palantir Foundry issues. It gathers SDK versions, environment variables (with secrets automatically redacted), API connectivity status, and recent application error logs. This structured output is essential for creating detailed, actionable support tickets when reporting persistent platform malfunctions.

This internal agent skill defines a systematic, multi-step procedure for scanning GitHub repositories to gather comprehensive evidence supporting or explaining bug clusters. The process covers searching open issues, inspecting recent commits, analyzing code paths, and checking deployment history. It assigns evidence confidence tiers (Exact, Strong, Moderate, Weak) to provide a deep, actionable understanding of potential software defects.