156 skills · sorted by relevance
Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"
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Navigate unfamiliar code using GitNexus knowledge graph
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Review a PR or branch diff using the knowledge graph for full structural context. Outputs a structured review with blast-radius analysis.
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Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.
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Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"
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Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: "How does X work?", "What calls this function?", "Show me the auth flow"
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Use when the user needs to run GitNexus CLI commands like analyze/index a repo, check status, clean the index, generate a wiki, or list indexed repos. Examples: "Index this repo", "Reanalyze the codebase", "Generate a wiki"
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Build or update the code review knowledge graph. Run this first to initialize, or let hooks keep it updated automatically.
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Generate images using nano-banana-2 MCP tools with best-practice prompting. Use this skill when the user asks to create, generate, or edit images.
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Process PDFs, Office documents, HTML, and images through the Nutrient DWS API. Convert formats, extract text and tables, recognize text in scans with OCR, remove sensitive information, add watermarks or digital signatures, and fill PDF forms.
Build and maintain MCP servers that let AI assistants use tools, read data, and access reusable prompts. Covers Node/TypeScript implementation, input validation, transport choices for local or remote clients, and registration or connection troubleshooting.
Guides agents in choosing Cherry Studio tools for research, document conversion, memory, notifications, and other tasks. It explains when to use app tools or local runtimes, with checks for availability, configuration, and approval.
Plan and build an MCP server, a connection that lets Claude use external tools. Choose hosting, tool structure, authentication, and a framework, then scaffold a server or hand off to specialized packaging and interface skills.
Add interactive panels to an MCP server so users can select records, view charts, or follow progress inside a conversation. The skill guides tool connections, interface implementation, sandbox restrictions, and testing in hosts that support these panels.
Generate a PHP server for MCP, a protocol that lets AI applications access tools and data. Build a project with the official PHP SDK, including requested capabilities, configuration files, PHPUnit tests, and setup documentation.
Build a Java MCP server, a service that lets AI agents call Java code, APIs, or databases. The skill prepares a Maven or Gradle project, implements tools, adds tests, and configures client connections.
Create a Kotlin MCP server, which connects AI agents to external tools, using the official SDK. Generate a Gradle project with tool implementations, input validation, configuration, basic tests, and setup documentation.
Create a Go server for MCP, a protocol that connects AI agents to tools and data. Generate a structured project using the official SDK, with typed tools, configuration, error handling, tests, and setup documentation.
Audit MCP servers, which provide external tools to AI agents, for hidden instructions, changes after approval, unsafe internal requests, and missing authentication. Use mcp-scan and manual checks to document risks and recommended fixes.
Create and customize Qwen Code extensions using templates for context, commands, skills, agents, and MCP servers that connect external tools. Validate configuration and referenced files, review executable behavior, and prepare the extension for local testing.
Turn an OpenAPI specification into starter code for an MCP server, which connects AI agents to API tools. Support Python or TypeScript implementations, validate tool definitions, and plan changes that preserve compatibility.
Expose REST APIs as tools for AI agents through Google Cloud API Gateway using MCP, a protocol for connecting agents to tools. Covers OpenAPI migration, authentication, client connections, validation, and troubleshooting.
Diagnose why an AI agent skips tools, passes incorrect arguments, or repeats calls. Get guidance on tool descriptions, schemas that define accepted inputs, execution flow, and mock tests before connecting to a real model.
Build remote MCP servers, which let AI agents call external tools, on Cloudflare Workers. This skill covers server code, tool definitions, OAuth authentication, local testing, deployment, and connecting a Codex client.