Skill UI

This skill provides comprehensive guidelines for monitoring Granola usage, tracking team meeting patterns, and building detailed analytics dashboards. It guides users in defining critical metrics (e.g., adoption rate, efficiency score) and establishing robust data pipelines. Users can stream meeting metadata from Granola to data warehouses like BigQuery, enabling deep, customizable reporting and automated weekly insights via services like Zapier and Slack.

This skill guides users on automating fundraising workflow analysis using local data methods. Since Finta lacks a public API, it teaches how to export pipeline data (CSV) and use Python (Pandas) scripts to calculate key funnel metrics, conversion rates, and generate detailed weekly reports for investor tracking and reporting.

This skill processes raw weekly CRM export CSV files into a clean, structured regional sales summary. It automatically deduplicates records, calculates total revenue per specified region, and outputs the results as a structured JSON object, making it perfect for downstream automation steps, reporting, or dashboard population.

Generates weekly telemetry reports and customer adoption reports for Azure Backup MCP tools. Runs KQL queries to classify errors into Customer/Azure Service/MCP Tool Bug, identifies customers via cross-cluster joins, compares week-over-week metrics, correlates with merged PRs, and produces an Outlook-compatible HTML report.

Transform raw structured data (CSV, Excel, JSON) into professional, long-form, interactive visualization reports. This tool generates comprehensive business review pages featuring KPI dashboards, multiple dynamic charts (using Chart.js/ECharts), detailed data tables, and narrative business insights, making it ideal for weekly or monthly performance reporting.

This skill analyzes local Q&A session JSONL files to produce a complete, auditable weekly report in Markdown format. It supports generating routine weekly summaries, comparing performance across consecutive time windows, and identifying key trends and risks. The output is a single, self-contained Markdown file designed to provide sufficient aggregates and structure for downstream documentation rendering (e.g., Feishu or whiteboards), without needing external data sources or APIs.