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Found 2,169 Skills
Attempt to read data from exposed tables to verify actual data exposure and RLS effectiveness.
Expert in data pipelines, ETL processes, and data infrastructure
Validate startup ideas using Hexa's Opportunity Memo framework and Perceived Created Value (PCV) methodology. Assess problem-solution fit, market opportunity, and determine if an idea is worth pursuing.
Validate existing offers using Hormozi's Value Equation. Scores offers, exposes weaknesses, and provides actionable fixes. Activates for "validate my offer," "rate my offer," or "is my offer good."
Validate traffic strategy against Traffic Secrets principles. Assess Dream 100, Work In vs. Buy In, traffic temperature, and audience ownership. Activates for "validate my traffic plan" or traffic assessment questions.
Advanced TypeScript patterns and best practices for 2025
Testing strategies for LangChain4j-powered applications. Mock LLM responses, test retrieval chains, and validate AI workflows. Use when testing AI-powered features reliably.
Unit tests for @ExceptionHandler and @ControllerAdvice for global exception handling. Use when validating error response formatting and HTTP status codes.
Visual feedback from humans via screenshot annotations. Use this skill CONSTANTLY — any time you need visual context, want to verify UI changes, need to confirm layout, debug a visual issue, check styling, validate a design, or show your work. Capture the screen, look at it, figure out what you need feedback on, annotate it, and ask. Do not ask the user what to capture — just capture and look.
Guidance for counting tokens in datasets, particularly from HuggingFace or similar sources. This skill should be used when tasks involve counting tokens in datasets, understanding dataset schemas, filtering by categories/domains, or working with tokenizers. It helps avoid common pitfalls like incomplete field identification and ambiguous terminology interpretation.
Detects data integrity issues including orphaned records, broken foreign key relationships, constraint violations, and provides automated fix migrations. Use for "data integrity", "orphaned records", "broken relationships", or "data quality".
Worker that checks DRY/KISS/YAGNI/architecture compliance with quantitative Code Quality Score. Validates architectural decisions via MCP Ref: (1) Optimality - is chosen approach the best? (2) Compliance - does it follow best practices? (3) Performance - algorithms, configs, bottlenecks. Reports issues with SEC-, PERF-, MNT-, ARCH-, BP-, OPT- prefixes.