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This skill should be used when creating or modifying frontend dashboard components in the ClaudeCode Sentiment Monitor project. Specifically trigger this skill for tasks involving React 19 components, Next.js 15 App Router pages, shadcn/ui components, Recharts visualizations, SWR data fetching, or Tailwind CSS styling.
Tracks financial transactions, categorizes expenses, monitors budgets, and audits subscriptions. Use when processing bank transactions, categorizing expenses, tracking revenue, or analyzing financial data.
Generate comprehensive PySpark-based data quality validation tests for Databricks tables. Use when creating automated tests for data completeness, accuracy, consistency, and conformity, or when user mentions test generation, data validation, quality monitoring, or PySpark test frameworks.
Generate detailed architectural descriptions of IT systems from short prompts. Produces structured output with overview, components list, connections/integrations, and ASCII diagrams with annotated arrows. Use when user asks to describe, explain, or document architecture of any IT system - specific platforms (Camunda, Kafka, Kubernetes), cloud services (AWS, GCP, Azure), databases, microservices, or custom systems.
Copilot agent that assists with performance analysis, bottleneck detection, optimization strategies, and benchmarking Trigger terms: performance optimization, performance tuning, profiling, benchmark, bottleneck analysis, scalability, latency optimization, memory optimization, query optimization Use when: User requests involve performance optimizer tasks.
Investigation Analyst persona for systematic troubleshooting and debugging. ACTIVATE when messages contain debug, investigate, troubleshoot, analyze, diagnose, error, bug, root-cause, RCA, trace, or inspect keywords.
AI-powered RCA Copilot for root cause analysis and incident explanation. Use when: (1) Building incident context retrieval from Neptune and DynamoDB, (2) Implementing evidence ranking and root cause candidate generation, (3) Creating natural language incident explanations, (4) Generating recommended remediation actions. Triggers: "explain incident", "find root cause", "diagnose data issue", "what caused the alert", "RCA for incident".
Executes structured, factual research runs across any domain (e.g., algorithms, competitors, AI models, audience behavior). Creates timestamped, reproducible artifacts with inline citations, confidence scoring, and adaptive freshness control.
Map non-linear connections across files, thoughts, and projects. Reveal hidden patterns in seemingly unrelated work. Use when feeling scattered, when working across multiple domains, or when sensing connections but unable to articulate them.
Create new AI-powered features using xAI Grok. Use when user mentions "new AI feature", "add Grok", "create prompt", "AI analysis", or "generate with AI".
Comprehensive guide to A/B testing, multivariate testing, statistical significance, and experiment analysis for data-driven product decisions
Extracts, transforms, and analyzes NBA statistics using the nba_api Python library. Use when working with NBA player stats, team data, game logs, shot charts, league statistics, or any NBA-related data engineering tasks. Supports both stats.nba.com endpoints and static player/team lookups.
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
Framework for capturing, storing, and comparing AI evaluations to measure consistency and completeness. Use when: comparing reviews, measuring evaluation quality, running reproducibility tests, auditing AI outputs, validating findings across runs. Triggers: "compare evaluations", "measure consistency", "evaluation framework", "reproducible review", "compare reviews", "validate findings", "audit evaluation".
Analyze completed experiments and craft executive-ready summaries with insights and recommendations.
Create onboarding, training, documentation, and enablement flows for admins, data teams, and business users of the InsightPulse Superset Data Lab.
Plotly visualization patterns for statistical and scientific charts. Use when creating interactive visualizations, statistical plots (scatter, box, violin, heatmaps), UpSet plots for set intersections, network graphs, or exporting figures to HTML/PNG/PDF/SVG formats. Covers both Plotly Express (high-level) and Graph Objects (low-level) APIs.
Database maintenance and health checks. Use when user needs migration safety, data integrity, index optimization, or says "check database", "optimize DB", "migration rollback", "data consistency", "database health".
Evidence framework data dashboard with DuckDB. Write SQL in markdown to create visualizations.
