jlens-mcp
Expert-level Java codebase analysis and Maven dependency management skill. Enables deep bytecode inspection, multi-version dependency conflict resolution, and automated project building via MCP integration.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
155 skills found
Expert-level Java codebase analysis and Maven dependency management skill. Enables deep bytecode inspection, multi-version dependency conflict resolution, and automated project building via MCP integration.
Build no-code MCP servers that orchestrate tools as directed graphs using YAML for data transformation, conditional routing, and automated workflows.
An advanced development guide for Claude Code, covering REPL environments, MCP integration, development workflows, and best practices for AI-assisted coding.
Lints, validates, and auto-fixes AI agent configuration files like SKILL.md, CLAUDE.md, and MCP configs.
A structured workflow for co-authoring documentation, technical specs, and proposals, guiding users through context gathering, collaborative refinement, and reader verification.
Orchestrate complex multi-agent swarms with topologies like mesh, hierarchical, and star for research, development, and testing workflows.
A command-line interface for the Model Context Protocol (MCP) to discover, inspect, and execute tools from MCP servers directly in your terminal.
Migrate existing OpenAI Apps SDK applications to the MCP Apps SDK, including step-by-step guidance, API mapping tables, and CSP investigation workflows.
Standardizes project context by managing artifacts (product, tech-stack, workflow, tracks) in a conductor/ directory. Supports project scaffolding, artifact synchronization, and AI alignment for greenfield and brownfield projects.
Build AI agents, multi-agent systems, and workflows using the OpenAI Agents SDK for TypeScript/JavaScript. Supports tools, handoffs, guardrails, MCP, and realtime voice.
A standardized template for creating and documenting modular Agent Skills to ensure consistent, efficient context engineering across AI agent systems.
Structured, template-driven workflow for end-to-end feature development including coding, automated testing, verification, and session-based improvement.