claude-opus-4-5-migration
Migrate your codebase, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to the advanced Opus 4.5 model with automated configuration adjustments.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
375 skills found
Migrate your codebase, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to the advanced Opus 4.5 model with automated configuration adjustments.
Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.
Analyze and debug fast-agent session histories, tool execution logs, and conversation timing to resolve performance bottlenecks, tool loops, and unexpected session terminations.
Clean up your current Claude Code session and automatically resume in a fresh, optimized terminal window.
Seamlessly publish Markdown to Feishu Docs. Features automatic table conversion, permission management, and intelligent document batch writing.
Semantic Go code navigation and analysis tool using the Language Server Protocol (LSP) for accurate, high-performance project intelligence.
Morph WarpGrep and Fast Apply tools for high-speed agentic code search, deep logic analysis, and efficient AI-driven code editing.
Infrastructure for cross-product HealthSim data persistence, entity correlation via SSN, and DuckDB database operations.
Method-driven planning workflow that intelligently decomposes tasks into structured plan.md files using zen-mcp tools, adapting to user clarity and automation needs.
An intelligent development orchestration skill that provides self-improving code analysis, build error diagnosis, and automated workflow configuration via mcp-prompts integration.
A testing utility for the npm-agentskills framework, designed to validate Nuxt module integration and skill discovery patterns.
Analyzes markdown files to identify token-wasting patterns, providing actionable suggestions to optimize documentation for LLM consumption and token efficiency.