personal-brand-agent
Autonomous multi-agent LinkedIn system using LangGraph and Claude Opus 4.5 for trend research, content creation, voice profiling, and analytics-driven optimization.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
139 skills found
Autonomous multi-agent LinkedIn system using LangGraph and Claude Opus 4.5 for trend research, content creation, voice profiling, and analytics-driven optimization.
Direct access to the Opper REST API for LLM orchestration, model management, task execution, and seamless migration from OpenAI, Anthropic, or OpenRouter.
Fetch and parse transcripts from YouTube and Bilibili videos for summarization, QA, and content extraction using yt-dlp.
Expert LangGraph architect skill for designing stateful, multi-actor AI agent workflows with robust persistence, conditional branching, and ReAct patterns.
VVM (Vibe Virtual Machine) is a language for agentic programs where the LLM acts as the runtime. Orchestrate multi-agent workflows, manage state, and build resilient AI pipelines.
Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.
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.
Multi-perspective AI consultation for technical architecture, complex refactoring, and structured debugging.
A unified interface for integrating and managing LLM chat providers like OpenAI, Anthropic, Google, Azure, and Bedrock within LangChain applications.
Generate high-quality text-to-speech audio using Microsoft Edge's neural voice engine via uvx edge-tts.
AI-powered browser automation server for web interaction, data extraction, and research using the Model Context Protocol.
Comprehensive AI-generated text detection framework. Features multi-layer analysis of vocabulary, structural patterns, model-specific fingerprints, and technical metadata artifacts to identify AI authorship.