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Found 875 Skills
Profile datasets to understand schema, quality, and characteristics. Use when analyzing data files (CSV, JSON, Parquet), discovering dataset properties, assessing data quality, or when user mentions data profiling, schema detection, data analysis, or quality metrics. Provides basic and intermediate profiling including distributions, uniqueness, and pattern detection.
Retrieve and display GitHub Copilot usage metrics for organizations and enterprises using the GitHub CLI and REST API.
Diagnose why a listing is losing the Amazon Buy Box (Featured Offer) and build a plan to win it back. Covers seller-health signals, pricing relative to the competing offer, fulfillment method, stock, and account metrics. Use when a user asks why they lost the Buy Box, how to win the Featured Offer, why a reseller is beating them, or why their own listing shows another seller's offer. Trigger phrases: "buy box", "featured offer", "lost the buy box", "win the buy box", "buy box percentage", "another seller has my listing". Works with zero tools. the user describes the offer and account state.
Triage an Amazon Account Health Rating (AHR) screenshot or text dump. Identifies which violations and metrics are pulling the rating down, prioritizes by point weight, and outputs a remediation order. Use when a user asks about Account Health, AHR, account health rating dropping, violations to address, or stabilizing a yellow or red account. Trigger phrases: "account health", "AHR", "violations", "policy strikes", "account at risk". Works with zero tools. the user pastes the AHR snapshot.
Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".
Query Apple Health SQLite database for vitals, activity, sleep, and workouts. Supports Markdown, JSON, and FHIR R4 output formats. This skill should be used when analyzing health metrics, generating health reports, answering questions about fitness or sleep patterns, or exporting health data in standard formats.
Application monitoring and observability setup for Python/React projects. Use when configuring logging, metrics collection, health checks, alerting rules, or dashboard creation. Covers structured logging with structlog, Prometheus metrics for FastAPI, health check endpoints, alert threshold design, Grafana dashboard patterns, error tracking with Sentry, and uptime monitoring. Does NOT cover incident response procedures (use incident-response) or deployment (use deployment-pipeline).
Search GitHub for real-world code examples and implementation patterns. Use when user wants to find code examples on GitHub, search GitHub repositories, discover how others implement features, learn library usage patterns, or research architectural approaches. Fetches top results with smart ranking (stars, recency, language), extracts factual data (imports, syntax patterns, metrics), and returns clean markdown for analysis and pattern identification.
Token-Oriented Object Notation (TOON) format expert for 30-60% token savings on structured data. Auto-applies to arrays with 5+ items, tables, logs, API responses, database results. Supports tabular, inline, and expanded formats with comma/tab/pipe delimiters. Triggers on large JSON, data optimization, token reduction, structured data, arrays, tables, logs, metrics, TOON.
Design and execute customer onboarding that drives activation and retention. Use when building onboarding flows for new users, reducing churn in the first 30 days, improving time-to-value, or creating onboarding sequences (email, in-app, or manual). Covers activation metrics, onboarding step design, friction reduction, and measuring onboarding success. Trigger on "customer onboarding", "onboarding flow", "user onboarding", "reduce early churn", "improve activation", "onboarding sequence", "time to value".
Executive-grade data analysis with pandas/polars and McKinsey-quality visualizations. Use when analyzing data, building dashboards, creating investor presentations, or calculating SaaS metrics.
Senior SaaS CFO / Financial Analyst (15+ years) specialized in financial modeling, projections, and exit strategy for bootstrapped and VC-backed SaaS companies. Activate when user needs: (1) Revenue projections (1-5 years), (2) Exit valuation and multiples, (3) Unit economics analysis (CAC, LTV, payback), (4) Scenario modeling (conservative/base/optimistic), (5) Fundraising narratives with financial backing, (6) M&A due diligence financials, (7) SaaS metrics benchmarking, (8) Cohort analysis and churn modeling. Triggers: "proyecciones", "projections", "exit", "valuation", "ARR", "MRR", "multiples", "revenue forecast", "financial model", "exit strategy", "CAC", "LTV", "unit economics", "churn", "fundraising", "M&A", "acquisition", "5 year plan".