Find the perfect capability for your agent.
General-purpose agent for researching complex questions and executing multi-step tasks. Versatile problem-solver that combines research capabilities, analytical thinking, and systematic task execution. Use for complex research projects, multi-step workflows, cross-domain analysis, and tasks requiring multiple tools and approaches.
Dashboard symbol_signals uses parallel lists (symbols[], signal_values[], gate_statuses[]) not dict keyed by symbol. Trigger when: (1) 'list' object has no attribute 'get', (2) .items() on symbol_signals fails.
Generate, optimize, and explain SQL queries with best practices. Use when writing database queries or optimizing SQL performance.
Generate professional HTML presentation slides from Markdown files or raw content. Use when creating presentations, slide decks, or when asked to make slides. Fully automated workflow - Markdown analysis → Layout selection → HTML generation → Thumbnail generation → Surge deployment. Supports 16 layouts, Chart.js graphs, and interactive features.
R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis
Interactive dashboard design workflow. Analyzes data, asks clarifying questions, generates visualizations, and saves reusable configs. Use when user has raw data and wants a dashboard created collaboratively.
Database query optimization strategies. Use when improving query performance.
Compact single-cell analysis patterns with AnnData and Scanpy. Essential patterns for QC, preprocessing, clustering, and visualization.
Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.
Professional rules for AI-driven data modeling and creation. Use this skill when users need to create and manage MySQL databases, design data models using Mermaid ER diagrams, and implement database schemas.
Comprehensive guide for TAR UMT Data Science (RDS) students completing their Final Year Project. Use when RDS students need help with (1) understanding FYP processes and requirements, (2) structuring FYP reports and deliverables, (3) writing research-based chapters and thesis, (4) selecting appropriate data science projects, (5) understanding research methodology and theoretical frameworks, (6) conducting experiments and statistical analysis, (7) preparing for system testing and presentations, (8) meeting submission deadlines and formats, (9) report formatting and structure requirements, or (10) any other FYP-related guidance specific to Data Science students at TAR UMT.
Expert in creating database diagrams and visual representations. Use when generating ERDs, schema diagrams, or visualizing database relationships with Mermaid.js.
Use when animating charts, graphs, dashboards, data transitions, or any information visualization work.
Generate detailed token usage report with threshold analysis and recommendations
Lightning-fast DataFrame library written in Rust for high-performance data manipulation and analysis. Use when user wants blazing fast data transformations, working with large datasets, lazy evaluation pipelines, or needs better performance than pandas. Ideal for ETL, data wrangling, aggregations, joins, and reading/writing CSV, Parquet, JSON files.
Design, deploy, upgrade, and operate the InsightPulseAI Superset-based BI platform on the user's infrastructure with secure, stable, scalable configs.
For leaders evaluating dashboards, metrics, or AI-generated reports to determine if they measure reality or generate confident-looking noise (Potemkin maps). Helps identify gaming potential, blind spots, and whether you should trust a metric more, less, or differently. Use when implementing new metrics, questioning existing dashboards, evaluating vendor claims, or when something feels off about your data. Keywords: dashboard audit, metrics, KPIs, gaming, Goodhart's law, AI reports, data quality, measurement validity, blind spots, Potemkin, are my metrics real, can I trust this data
A comprehensive collection of Agent Skills for software engineering, database operations, and productivity automation. Use when building, optimizing, or debugging software systems with AI assistance.
Copilot agent for database schema design, ER diagrams, normalization, DDL generation, and performance optimization Trigger terms: database design, schema design, ER diagram, normalization, DDL, database modeling, relational database, NoSQL design, data modeling, migration plan Use when: User requests involve database schema designer tasks.
