Skill UI

A comprehensive guide covering best practices for building and debugging Nuxt 4 applications. It details patterns for ensuring hydration safety, optimizing data fetching with `useFetch` and `useAsyncData`, managing complex routing rules, and implementing lazy loading for superior performance across SSR and client-side environments.

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 guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

A comprehensive guide for bringing RuView CSI sensing nodes (ESP32-S3/C6) online. This skill covers the full lifecycle: building the embedded firmware, flashing the device, provisioning WiFi credentials, and confirming the Continuous Sensing Image (CSI) data stream. Essential for physical hardware deployment and debugging of complex sensing systems.

A comprehensive tool for finding solutions to software errors, API usage issues, and compatibility problems by searching major developer communities, official documentation, GitHub, Stack Exchange, and various Chinese technical forums. Ideal for debugging and discovery workflows, it aggregates real-world workarounds and technical Q&A, but results should not be cited as academic evidence.

A comprehensive guide for enhancing existing agentic web applications built on the CopilotKit frontend and Microsoft Agent Framework running on Azure AI Foundry. Covers advanced topics such as adding and gating agent tools, implementing human-in-the-loop (HITL) approvals, managing shared state, debugging event streams, and safely deploying agent updates. This skill is for maintenance and extension, not for initial project scaffolding.

This operator skill provides comprehensive control over the OpenViking context engine, enabling agents to manage long-term memory, session archives, and external resources within OpenClaw. It facilitates semantic recall, context querying, resource import, and recall trace debugging by communicating with a remote OpenViking server. Use this plugin when your agent requires advanced, persistent context management.

This skill activates 'Steno Mode,' providing highly compressed, expert-level responses by eliminating filler words and superfluous tokens (up to 40% reduction). It maintains absolute technical precision, making it ideal for technical communications like code reviews, debugging Q&A, and architecture summaries. Users can select four defined levels (lite, brief, court, machine) to control the density, with exactness always taking top priority.

Exa MCP provides advanced neural search capabilities across the web, specialized code repositories (like GitHub), and corporate/personnel databases. Use it for deep background research, finding the latest news, debugging complex code patterns, or conducting thorough market and competitor analysis.

This skill provides an in-depth, entity-level scorecard for clinical or biomedical Named Entity Recognition (NER) models. It calculates precision, recall, and F1 scores (both strict and partial match modes) using a user-supplied gold corpus. Key features include per-label error breakdown, a confusion matrix, and detailed False Negative/False Positive analysis, essential for debugging model performance in highly sensitive medical domains.

This skill facilitates cross-version symbol migration and binary diffing. When existing symbols or reverse engineering results from an old version need to be quickly mapped to a new binary version, this tool is used. It leverages LLMs to perform structured structural comparisons between corresponding functions in different versions, enabling the programmatic transfer of function names and offsets even when original PDB files are lost. Ideal for kernel debugging, application updates, and binary analysis.

A specialized skill for analyzing and reproducing complex, timing-dependent bugs in concurrent systems. Use this to investigate race windows, ordering bugs, lock gaps, stale reads, or state inconsistencies where the final outcome depends critically on request timing or asynchronous execution order. Essential for deep security auditing and debugging of distributed transactions.