Skill UI

This skill is designed to retrieve and display detailed usage metrics for GitHub Copilot within organizations and enterprises. Users can analyze adoption rates, total suggestions, acceptance rates, and chat interactions, broken down either by the entire organization/enterprise or on a per-user basis. It supports querying data for specific historical dates using GitHub CLI and REST API.

A comprehensive guide for interpreting, diagnosing, and fixing validation errors within n8n workflows. It covers error severity levels (Errors, Warnings, Suggestions), details common error types (e.g., missing_required, type_mismatch), and explains the iterative validation process. Learn how to use different validation profiles (minimal, runtime, strict) to ensure robust and functional automated workflows.

Napkin gives Copilot CLI users a browser whiteboard to draw, sketch, and add sticky notes; Copilot reads snapshots and JSON data, then replies with analysis, suggestions, and next steps for business-oriented thinkers.

Use this skill to easily submit feedback, report bugs, or provide suggestions for the Nexu platform. Simply use `/feedback <message>` in the chat. The system automatically gathers the conversation history and any attached images, ensuring comprehensive and convenient reporting without manual copying or pasting.

This guide details the operational usage limits for Finta's core features, such as investor pipeline records, deal room creation, and AI suggestions. Since Finta lacks a traditional REST API, limits are defined by plan tiers. It provides reference tables and TypeScript examples for implementing usage tracking, robust webhooks retry strategies, and batch processing to ensure smooth scaling and prevent mid-raise disruptions during integrations.

This workflow demonstrates the core capabilities of the Glean Client API, allowing users to build comprehensive knowledge assistants. It covers advanced full-text search with filters and facets, implements AI-powered chat for contextual Q&A, and provides autocomplete suggestions. Ideal for integrating deep search and conversational AI into enterprise applications.

This comprehensive runbook provides structured procedures for diagnosing and responding to critical failures when integrating with the Grammarly API. It covers various incident types, including full API outages, OAuth token expiration, rate limit storms, and suggestion quality degradation. The guide includes severity classification, diagnostic `curl` commands, detailed playbooks, and post-incident documentation steps to ensure service resilience and minimal downtime.

This skill implements comprehensive observability (metrics, logging, and alerting) for Grammarly API integrations. It monitors critical performance indicators such as text check latency (p95), API error rates, suggestion acceptance rates, and token consumption. Use this setup when establishing robust monitoring systems, tracking real-time API performance degradation, or implementing proactive alerting to ensure consistent, high-quality user experiences in writing and editing flows.

This skill provides comprehensive analysis and improvement suggestions for plugin prompts, skill definitions, and command instructions. It evaluates content across five dimensions—clarity, safety, effectiveness, completeness, and conciseness—providing a detailed score card and concrete, actionable rewrites to ensure compliance and optimal AI performance.

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.

This skill provides comprehensive code reviews, helping users assess code quality, identify various potential issues (syntax, logic, security, performance), and receive actionable suggestions for improvement. It generates a detailed, structured report covering a summary, found issues, and quality metrics.

This skill is designed to assist developers in debugging complex code issues. It analyzes detailed error messages, stack traces, and common error types (like TypeError or ReferenceError) to accurately pinpoint the root cause of bugs. It provides a structured, step-by-step report including specific fix suggestions and proactive prevention strategies.