Skill UI

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 tool collects a comprehensive diagnostic archive for the Hex platform. It gathers crucial evidence, including API connectivity status, project inventory, recent run histories, data connection health, and current rate limit information. Use this bundle when encountering persistent bugs, preparing detailed support tickets, or troubleshooting complex data flow issues.

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 skill restructures AI-generated content to maximize cognitive accessibility, specifically optimized for users with ADHD or neurodivergence. It uses evidence-based techniques such as chunking, visual hierarchy, front-loading key points, and progressive disclosure. It offers three distinct modes (clean, flow, zen) to tailor the output structure, ensuring that complex information is highly digestible and easy to follow.

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 bundle collects comprehensive diagnostic evidence from the MindTickle sales enablement API. It gathers data on API token validation, course catalog status, user enrollment, assessment progress, and CRM sync state. It allows users to diagnose complex issues like content gaps, scoring discrepancies, and SSO failures without requiring admin access, providing a complete diagnostic tarball for support teams.

This tool collects comprehensive diagnostic evidence for troubleshooting Miro REST API v2 integration problems. It gathers environment details, performs connectivity tests (DNS, HTTPS), checks rate limits, and aggregates recent application logs into a single, structured bundle. Use this when preparing a detailed support ticket or diagnosing persistent API issues.

A comprehensive guide for rapid incident response and troubleshooting of Miro API v2 integration failures. It covers defining severity levels, quick triage steps, detailed error handling for 4xx/5xx status codes (e.g., token expiration, rate limiting), mitigation strategies, and postmortem evidence collection.

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.

Performs rigorous, zero-context verification of all quantitative claims, comparisons, and scope statements reported in a research paper against the raw evidence files. It is designed to detect confirmation bias, such as rounding errors, data cherry-picking, or misreported deltas, ensuring strict fidelity between published claims and underlying raw results. Ideal for pre-submission quality control.

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.