Data & Analytics

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Analyze Murder Mystery 2 gameplay data, track inventory, and optimize strategy using this Roblox analytics toolkit

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Murder Mystery 2 gameplay analytics, inventory tracking, and strategy optimization toolkit for Roblox

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Analytics and inventory tracking toolkit for Roblox Murder Mystery 2 with strategic gameplay insights

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Analytics and inventory management toolkit for Roblox Murder Mystery 2 gameplay optimization

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Crypto market intelligence via EmblemAI. Trending tokens, on-chain analytics, derivatives data, and smart money tracking from CoinGecko, CoinGlass, Birdeye, and Nansen. Use when the user wants market data, trending tokens, derivatives analytics, or on-chain intelligence.

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Generate publication-quality LaTeX tables from experimental results. Convert JSON/CSV data to booktabs-styled tables with bold best results, multi-row layouts, and proper captions. Use when creating result tables, comparison tables, or ablation tables for papers.

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Build apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.

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Automatically add PostHog analytics instrumentation to code. Triggers when user asks to add tracking, instrument events, add analytics, or implement feature flags in their codebase.

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Murder Mystery 2 inventory tracking, analytics dashboard, and gameplay optimization toolkit for Roblox

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Inside Airbnb data warehouse built with Snowflake and dbt, demonstrating modern analytics engineering patterns with staging, intermediate, and mart layers.

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This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.

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Build ETL pipelines and analytics dashboards for Harvard Art Museums API data with Python, SQL, and Streamlit

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Build end-to-end data engineering pipelines with Harvard Art Museums API, ETL workflows, SQL analytics, and Streamlit visualization.

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SQL and Python-based employee performance analytics with KPI aggregation, departmental insights, and HR dashboard generation

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Build ETL pipelines and analytics dashboards using Harvard Art Museums API with Python, SQL, and Streamlit

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Build AI agents for real-time financial options analysis with LangGraph, ChromaDB RAG, and Polygon.io data

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Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.

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Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

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Analytics dashboard and inventory management toolkit for Roblox Murder Mystery 2 game data tracking and optimization

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Process, clean, and analyze car sales data with statistical modeling and interactive Streamlit dashboards for business insights.

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Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with SQL storage and Streamlit visualization

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Multi-platform hot search crawler and LLM-powered public opinion analysis system with clustering, sentiment analysis, and multi-channel push notifications

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Extract structured company lists from directories with Firecrawl. Use for scraping YC, Crunchbase, Product Hunt, G2, startup directories, category directories, or custom company databases into JSON, CSV, CRM-ready lists, or research tables.

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Pull metrics from analytics dashboards and internal web tools with Firecrawl browser. Use when the user needs dashboard reporting, cross-platform metric summaries, authenticated analytics extraction, date-range reports, or structured metrics from web dashboards.

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Bulk data enrichment. Adds web-sourced fields (CEO names, funding, contact info) to lists of companies, people, or products. Use for enriching CSV files or inline data. Supports multi-turn: pass --previous-interaction-id from a prior research task to carry context forward.

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End-to-end data engineering and analytics application for Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization

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Build ETL pipelines and analytics dashboards using Harvard Art Museums API data with Python, SQL, and Streamlit

om-scogo Bundle

Build ETL pipelines and analytics dashboards for the Harvard Art Museums API using Python, SQL, and Streamlit

om-scogo Bundle

Build ETL pipelines and analytics dashboards for Harvard Art Museums API data using Python, SQL, and Streamlit

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Master data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib

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Build end-to-end ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit

om-scogo Bundle

End-to-end data engineering and analytics application for Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization

om-scogo Bundle

End-to-end data engineering and analytics application using Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization

neekware Bundle

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.

mukul975-2 Bundle

Evaluating and implementing cookie-less tracking alternatives for a post-cookie era. Covers the Privacy Sandbox APIs (Topics, Attribution Reporting, Protected Audiences), server-side analytics, and privacy-preserving measurement techniques.

mukul975-2 Bundle

Conducts Privacy Impact Assessment for large-scale systematic monitoring under GDPR Article 35(3)(c). Covers CCTV and video surveillance, employee monitoring, location tracking, internet monitoring, and behavioural analytics. Applies EDPB WP248rev.01 criteria for systematic monitoring of publicly accessible areas. Keywords: DPIA, large-scale monitoring, CCTV, employee monitoring, systematic monitoring, surveillance, location tracking.