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Found 590 Skills
Twitter/X account analytics, viral patterns, VIP follower discovery, tweet drafting. Use when analyzing a creator's reach, finding hidden-gem followers, or drafting tweets in someone's style (e.g. score @vitalik, draft tweet on AI).
End-to-end data engineering and analytics application using Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization
Use the viral.app API from an agent with a local CLI for account analytics, tracked videos/accounts, projects, creator hub, and live data operations.
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
End-to-end ETL pipeline for Harvard Art Museums API with SQL analytics and Streamlit visualization
Build end-to-end ETL pipelines with Harvard Art Museums API, SQL analytics, and Streamlit visualization
Build and maintain an executable context layer for data and analytics agents using ktx's semantic layer, wiki knowledge, and MCP integration
Analytics engineering for reliable metrics and BI readiness. Build transformation layers, dimensional models, semantic metrics, data quality tests, and documentation. Use when you need dbt or SQL transformation strategy, metrics definition, or analytics data modeling.
Expert knowledge for Formedible - A React form library built on TanStack Form with 22+ field types, multi-page forms, analytics, and type-safe validation
Implement PostHog analytics, feature flags, and session replay for Next.js apps. Use this skill for event tracking, user identification, A/B testing, experiments, and session recording setup. Also handles analytics reporting (funnel analysis, retention, SEO) with Google Search Console integration.
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.
Product analytics expert using PostHog MCP. Triggers on requests to understand user behavior, surface insights, create dashboards, analyze funnels, track metrics, set up experiments, or answer questions about product performance. Use when working with PostHog data, discussing analytics strategy, investigating user journeys, retention, conversion, feature adoption, or when asked to help understand what's happening in the product.