Skill UI

A comprehensive methodology for pinpointing the root cause of complex bugs, regressions, or unexpected behavior in large codebases. Techniques include binary search debugging, Git bisect for tracking regressions across commits, strategic data/control flow logging, component isolation, and advanced breakpoint strategies (conditional breakpoints, watch expressions). Ideal for scenarios where the bug source is unclear or difficult to reproduce.

Automates Wrike project management via Rube MCP’s Composio toolkit, covering secure connection setup, task/folder creation, blueprint launches, progress tracking, and workspace/member control while always fetching schema info first.

Automates Zoom meeting scheduling, webinar handling, cloud recording retrieval, and participant tracking through the Rube MCP toolkit, ensuring you first fetch current schemas and maintain an active OAuth connection for paid accounts.

This skill enables personal execution of implementation plans task-by-task in the current session without spawning subagents per task. It employs Test-Driven Development (TDD) for verification, tracks progress via a ledger, and resolves conflicts autonomously. Suitable for inline execution choices or when subagent tools are unavailable, it reduces context costs while ensuring quality through final review.

This skill helps users maintain a structured and comprehensive record of their daily activities, progress, key decisions, and learning outcomes. It creates a dedicated file for each day using the ISO date format (yyyy-mm-dd), ensuring that all entries are organized chronologically. By adhering to concise, third-person reporting, it helps users build a valuable, reviewable knowledge base of their journey and accomplishments.

This advanced research technique is designed for deep dives into football topics. Instead of relying solely on the original query, it mandates tracking every discovered entity—such as player names, club names, or coaches—from search results. By treating these entities as new leads, the user must formulate follow-up searches using the discovered names and remaining constraints, ensuring nothing relevant is overlooked and building a comprehensive knowledge chain.

This technique is designed for identifying target Indian actors when the clues are indirect, often pointing to a relative (father, spouse, grandfather) instead of the actor directly. It requires systematic, step-by-step tracking through the family lineage to deduce the identity. Mastering this method allows users to solve complex Bollywood trivia questions by following the family connections.

This technique guides users on how to solve highly complex questions that require tracking a chain of related entities (e.g., Author -> Article -> Mentioned Person). Instead of attempting a single search, the process must be broken down into sequential steps, using the result of one search as an anchor point for the next, thereby building a comprehensive knowledge chain.

A comprehensive, adaptive mathematics tutor covering K-12 topics from arithmetic through calculus. This skill utilizes proven pedagogical strategies—such as Socratic questioning, CPA approach, and error analysis—to ensure students build deep mathematical understanding rather than just procedural fluency. It adapts to multiple global curricula (SAT, A-Level, Gaokao) and tracks progress to target weak spots.

This technique is designed for advanced academic research, enabling users to search for theses by running two parallel tracks: a Topic Track (focusing on discipline, methodology, and materials) and a People Track (focusing on university, advisor, degree type, and year). By switching between these two methodologies, users can cross-validate results and significantly improve the accuracy and completeness of their literature review.

LaminDB is an open-source lineage-native lakehouse designed for biological datasets and models. It enables queryable, traceable, and validated data management across various storage formats like S3 and Postgres. Users can track computational workflows, curate data with ontologies via Bionty, and ensure FAIR compliance. Ideal for managing scRNA-seq, spatial transcriptomics, and integrating ML pipelines while maintaining reproducibility and collaboration.

This guide provides comprehensive instructions for setting up monitoring and observability for Qdrant deployments. It helps users track critical performance metrics, check system health, and diagnose complex production issues such as memory growth or slow query requests. Use cases include setting up Prometheus, Grafana, and implementing robust health checks.