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Found 802 Skills
Measure what matters with proper event tracking, funnels, cohorts, and metrics. Use when setting up analytics, tracking features, or understanding behavior.
Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Includes tech debt analyzer, team scaling calculator, engineering metrics frameworks, technology evaluation tools, and ADR templates. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions CTO, tech debt, technical debt, team scaling, architecture decisions, technology evaluation, engineering metrics, DORA metrics, or technology strategy.
Filter and screen stocks by financial metrics like P/E ratio, market cap, dividend yield, and growth rates. Analyze and compare stocks from CSV data.
Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.
Expert growth product management guidance for SaaS applications. Use when designing growth loops, optimizing activation and onboarding, building retention systems, creating referral mechanics, running growth experiments, defining north star metrics, or implementing PLG strategies. Covers the full growth lifecycle from acquisition to monetization.
Monitor Render services in real-time. Check health, performance metrics, logs, and resource usage. Use when users want to check service status, view metrics, monitor performance, or verify deployments are healthy.
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".
Full Sentry SDK setup for Ruby. Use when asked to add Sentry to Ruby, install sentry-ruby, setup Sentry in Rails/Sinatra/Rack, or configure error monitoring, tracing, logging, metrics, profiling, or crons for Ruby applications. Also handles migration from AppSignal or Honeybadger. Supports Rails, Sinatra, Rack, Sidekiq, and Resque.
Analyse Datadog observability data including metrics, logs, monitors, incidents, SLOs, APM traces, RUM, security signals, and more. Use when asked to investigate infrastructure health, query metrics, search logs, check monitors, diagnose errors, or analyse any Datadog data.
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-analysis, write-judge-prompt, or validate-evaluator instead).
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
Use this skill when the user discusses experiment design, ablations, training runs, evaluation, baselines, metrics, failures, or result interpretation that should be logged into Obsidian experiment and result notes.