Skill UI

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.

Opens a named, traced browser session bound to an RVF cognitive container, recording every action as a ruvector trajectory step. It is the primitive that browser extraction, form-fill and testing skills compose on, enabling session replay and analysis.

A comprehensive guide to building robust, reliable, and maintainable End-to-End (E2E) test suites using Playwright. This includes implementing the Page Object Model (POM), configuring advanced Playwright settings for CI/CD, managing artifacts (screenshots, traces), and strategies for handling flaky tests to ensure high test stability.

This skill provides comprehensive operational controls for long-lived, cloud-hosted agent workloads. It manages the entire lifecycle—from deployment and scaling to observability and security. Features include detailed monitoring (logs, metrics, traces), safety controls (kill switches, least privilege), and robust change management processes (rollout, rollback, auditing). Ideal for mission-critical microservices and background agents.

Diagnose why a GAIA benchmark question failed: extract the trace, classify the failure mode via a taxonomy (tool gap, reasoning miss, extraction bug, loop, dataset shift, timeout), and propose a targeted fix before resubmitting.

A comprehensive guide to professional Git version control. It covers best practices for branching strategies (GitHub Flow, GitFlow, Trunk-Based), writing standardized commit messages using Conventional Commits, managing merges vs. rebases, and defining effective Pull Request workflows. Ideal for any development team looking to improve code collaboration and maintain a clean, traceable history.

Collects distributed trace spans for a specific task ID, building a comprehensive, visual trace tree. Users can analyze the timing of individual spans, identify performance bottlenecks, understand agent coordination, and synthesize a narrative summary of the task's execution flow. Ideal for debugging complex multi-agent systems and improving system observability.

This skill executes the final two phases of the SPARC methodology: Refinement and Completion. It systematically reviews the implemented feature through rigorous code reviews, expands test coverage (unit, integration, edge cases), validates performance, generates a traceability matrix, creates comprehensive documentation, and confirms deployment readiness. It ensures the feature is robust, tested, and production-ready.

This workflow guides developers through Test-Driven Development (TDD) principles. It mandates writing tests (unit, integration, E2E) before writing implementation code, ensuring minimum 80% test coverage. The structured process (Red -> Green -> Refactor) guarantees robust code quality, effective bug fixing, and safe feature addition, while integrating Git checkpoint commits for full traceability.

This skill helps researchers strengthen AAAI paper submissions by ensuring reproducibility standards are met. It covers experimental traceability, seed reporting, compute disclosure, and dataset licensing. Users learn to map claims to evidence, audit checklists, and prepare artifacts for Phase-1 reviewers to validate rigor across AI scopes.

This skill guides the systematic gathering and structuring of literature for comprehensive academic reviews, such as an Annual Review of Psychology (ARPsych). It ensures broad coverage by executing traceable searches (database sweeps, citation chasing) and building a detailed evidence matrix (one row per finding). This establishes a robust, documented base of evidence, allowing the user to focus their analytical contribution later without sacrificing completeness.

This skill guides art historians on gathering, weighing, and citing non-visual, documentary, and material evidence for academic articles. It covers citing primary sources (archives, contracts), reconstructing provenance chains, interpreting technical reports, and ensuring that all scholarly claims are rigorously traceable to verifiable sources rather than mere assertion.