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.
399 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.
Agentic AI-powered JSON i18n file translator with auto-terminology, format preservation, and incremental updates to streamline global software deployment.
Automated security skill for identifying and validating XSS vulnerabilities, including Reflected, Stored, and DOM-based attacks across various contexts.
Expert code reviewer for Rust projects. Performs comprehensive quality, security, performance, and architectural analysis using Bazel and project-specific conventions.
Framework for automated n8n integration testing including API contract validation, authentication flows, rate limit handling, and error scenario coverage.
Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.
An advanced development guide for Claude Code, covering REPL environments, MCP integration, development workflows, and best practices for AI-assisted coding.
IDE-grade project scaffolding wizard for 70+ types of web, mobile, desktop, and backend projects, featuring interactive setup for SDKs, databases, and DevOps configurations.
Automate GitLab repository management with this API-based tool. Perform file operations, branch management, and project tracking directly through your AI agent.
Automate Excel report generation from CSVs, databases, or data structures using pandas and openpyxl. Supports chart creation, custom styling, template-based workflows, and data analysis.
Control and monitor Xiaomi Mijia smart home devices including status switching, device discovery, automation scenes, and environmental statistics.
PyTorch Lightning skill for scalable deep learning: automates model training, multi-GPU orchestration, data pipelines, and distributed training strategies like DDP, FSDP, and DeepSpeed.