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Found 860 Skills
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".
Test-Driven Development workflow with session integration. Use when implementing features/bugfixes to enforce RED-GREEN-REFACTOR discipline. Integrates with session-management for enhanced TDD session tracking, checkpoints, and metrics.
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.
Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan.
Use ONLY when creating NEW registrable components in ML projects that require Factory/Registry patterns. ✅ USE when: - Creating a new Dataset class (needs @register_dataset) - Creating a new Model class (needs @register_model) - Creating a new module directory with __init__.py factory - Initializing a new ML project structure from scratch - Adding new component types (Augmentation, CollateFunction, Metrics) ❌ DO NOT USE when: - Modifying existing functions or methods - Fixing bugs in existing code - Adding helper functions or utilities - Refactoring without adding new registrable components - Simple code changes to a single file - Modifying configuration files - Reading or understanding existing code Key indicator: Does the task require @register_* decorator or Factory pattern? If no, skip this skill.
Collect and analyze on-device performance metrics and crash diagnostics using MetricKit. Use when setting up MXMetricManager, handling MXMetricPayload or MXDiagnosticPayload, processing crash/hang/disk-write diagnostics via MXCallStackTree, adding custom signpost metrics, or uploading telemetry to an analytics backend.