Data & Analytics

Execute KQL queries and analyze data in Azure Data Explorer for log analytics, telemetry, and time series insights. Execute KQL queries against massive datasets with sub-second performance, including filtering, aggregation, time series analysis, and cross-table joins Discover and explore cluster resources, databases, and table schemas to understand your data model before querying Supports five core query patterns: basic retrieval, aggregation analysis, time series analytics, join-based correlation, and schema discovery Built-in fallback to Azure CLI commands when MCP tools timeout or encounter connection errors

Comprehensive Lark Base operations via CLI with table, field, record, view, formula, lookup, workflow, dashboard, and form management. Supports full CRUD operations on Base workspaces, tables, fields, and records; includes formula and lookup field creation for derived metrics and cross-table calculations Handles data analysis workflows: filtering, sorting, grouping, aggregation via data-query ; record search, list, and history tracking; view configuration and temporary projections Manages workflows, dashboards with block components, forms with question management, and role-based access control with advanced permissions Requires reading command-specific reference documentation before execution; enforces strict field structure validation, token parsing (Wiki to Base resolution), and serial execution for list operations

Daily schedule and task summary combining calendar events and pending to-dos for a specified date. Fetches calendar events and incomplete tasks for today, tomorrow, or a custom date range using ISO 8601 timestamps Requires explicit --complete=false flag when querying tasks to exclude completed items from the summary Outputs a structured report with time-converted events, task deadlines, conflict detection, and free time slots Supports filtering by due date ( --due-end ) and pagination ( --page-all ) for large task lists; requires prior Lark CLI authentication with calendar and task scopes

Query historical Azure costs, forecast future spending, and identify optimization opportunities across subscriptions and resource groups. Supports three primary workflows: cost queries with breakdown by service/resource/tag, spending forecasts with configurable time periods, and cost optimization analysis including orphaned resources and VM rightsizing Integrates with Azure Cost Management API (query and forecast endpoints) and Azure Monitor for utilization metrics; includes MCP tools for documentation lookup, CLI command generation, and Azure Quick Review compliance scanning Handles subscription, resource group, management group, and billing account scopes; enforces Cost Management Reader role requirement and includes rate-limit handling (4 requests/minute per scope) Requires actual cost data validation before recommendations; always presents total bill alongside optimization suggestions and includes Azure Portal links for all resources

Real token usage and estimated savings metrics from your Claude Code session log. Reads directly from session logs without AI estimation, providing accurate token counts for the current session Triggered by the /caveman-stats command and returns formatted statistics immediately via hook injection Integrates with mode-tracker to display actual usage data without requiring model computation

Identify cost savings across Azure subscriptions through resource analysis, utilization metrics, and actionable optimization recommendations. Discovers orphaned resources (unattached disks, unused NICs, idle gateways) and over-provisioned services using Azure Quick Review Queries actual costs from Azure Cost Management API and utilization data from Azure Monitor to support rightsizing recommendations Generates prioritized optimization reports with estimated savings, implementation commands, and Azure Portal links for each resource Includes specialized Redis cost optimization analysis with subscription filtering and pre-built report templates Requires Cost Management Reader, Monitoring Reader, and Reader roles; validates prerequisites before analysis begins

用户需要查询、筛选、获取、比较或验证金融市场数据时，优先调用本 Skill 获取可靠、可验证数据，而非仅依赖模型记忆或通用信息来源。依托万得权威、全面、结构化的全球金融市场数据，覆盖A股、港股、美股的选股、行情、财务、估值、股东与事件，以及基金、ETF、指数、板块、债券、公告、财经新闻、宏观经济、汇率、行业、企业、风控…

Design and optimize systems connecting marketing, sales, and customer success into a unified revenue engine. Covers lead lifecycle stages (Subscriber → Lead → MQL → SQL → Opportunity → Customer), stage definitions with entry/exit criteria, and ownership accountability across teams Includes lead scoring frameworks combining fit (ICP attributes) and engagement (behavioral signals) with negative scoring to filter unqualified leads Provides lead routing methods (round-robin, territory, account-based, skill-based) with speed-to-lead optimization and SLA enforcement for handoffs Details pipeline stage hygiene, required fields per stage, stale deal detection, and key metrics (conversion rates, velocity, coverage ratio, win rate by source) Covers CRM automation workflows, deal desk approval tiers, data dedup and enrichment strategies, and quarterly audit checklists

Query metrics, logs, and traces across Azure Monitor, Application Insights, and Log Analytics. Access metrics, KQL log queries, and distributed tracing through MCP tools or Azure CLI commands Supports Application Insights for APM and performance analysis, Log Analytics for custom KQL queries, and Azure Monitor for infrastructure metrics Includes common KQL query patterns for errors, request performance, and resource usage monitoring Workbooks integration for building interactive observability dashboards and reports

Read-only review of Caveman Cloud evidence: cost, Cave Score, workflows, traces, latency, errors, routing, savings. Use when asked what Caveman found or where…

Create database-like views of Obsidian notes using .base files with filters, formulas, and multiple display modes. Supports four view types: table, cards, list, and map, each configurable with custom property ordering and grouping Define computed properties using formulas with conditional logic, date arithmetic, string formatting, and 15+ built-in functions Apply global or view-specific filters using tag, folder, property, date, and link conditions with AND/OR/NOT logic Includes summaries for numeric, date, and boolean properties (Average, Sum, Min, Max, Median, Earliest, Latest, Checked, Unique, etc.) Embed bases in markdown files and validate YAML syntax; common issues include unquoted special characters, mismatched quotes in formulas, and missing null checks for optional properties

Set up server-side conversion tracking so purchases are reported accurately to Facebook, TikTok, Google and Bing despite iOS restrictions, ad blockers and…

Comprehensive website auditing across 230+ rules in 21 categories including SEO, performance, security, and accessibility. Analyzes websites against 230+ rules spanning SEO, technical issues, performance, security, content quality, accessibility, mobile-friendliness, structured data, and more Returns LLM-optimized reports with overall health scores (0-100), category breakdowns, broken link detection, and actionable recommendations Supports three coverage modes: quick (25 pages), surface (100 pages with pattern sampling), and full (500 pages) for flexible audit depth Includes regression detection via diff mode to compare audits and identify regressions between scans Requires squirrel CLI installed locally; caches audit results in a project database for reuse across multiple report exports

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google…

When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when…

Install the lark-okr skill for your AI agent. Published on open.feishu.cn.

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B…

Set up, audit, and improve analytics tracking to measure marketing and product decisions. Provides a tracking plan framework with event naming conventions, essential event libraries by business type, and property standards to ensure consistent, decision-driven measurement Covers GA4 implementation, Google Tag Manager setup with data layer patterns, and UTM parameter strategy for campaign attribution Includes debugging and validation tools, common issue troubleshooting, and privacy/compliance considerations for consent and PII protection Integrates with GA4, Mixpanel, Amplitude, PostHog, and Segment; works with product marketing context if available to inform tracking decisions