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Jev Judgment Routing

sickn33/agentic-awesome-skills

Routes enumerable judgment steps (did it work, which option, how risky) to the Jev judgment model via MCP tools, batching questions into single calls and handling escalated verdicts. For agents needing fast typed decisions without token generation.

Master LangChain Architecture

langchain-architecture

sickn33/agentic-awesome-skills

Master the LangChain framework to build sophisticated LLM applications. Learn to implement agents, chains, memory management, and tool integration. This skill covers core concepts like ReAct agents, retrieval chains, and document processing pipelines. Ideal for creating autonomous AI agents and production-grade applications using Python.

LangGraph Stateful Agent Builder

sickn33/agentic-awesome-skills

Expert guidance for building production-grade stateful, multi-actor AI agents with LangGraph, covering graph construction, state management, cycles and branching, checkpoint persistence, human-in-the-loop patterns, and the ReAct agent workflow.

Cross-Jurisdictional Legal Compliance Engine

sickn33/agentic-awesome-skills

LEX is a structured truth engine designed to eliminate legal hallucinations by grounding AI agents in verified government references and legislation across 29+ jurisdictions (including US, EU, and CA). It provides deterministic context for comparing legal requirements, drafting compliant business, and employment documents, ensuring high jurisdictional accuracy.

LLM Security Assessment & AI Agent Testing

sickn33/agentic-awesome-skills

Conduct authorized security assessments for LLM applications and AI agents. Covers prompt injection, tool abuse, RAG exposure, memory poisoning, and system prompt extraction following OWASP LLM/ASI Top 10 guidelines. Includes red-teaming, vulnerability mapping, and compliance engineering for AI systems to ensure safety and robustness.

Lore Project Memory

sickn33/agentic-awesome-skills

Framework-agnostic long-term project memory for AI agents. Persists architecture decisions, conventions, and context as plain Markdown files in .lore/, enabling any agent to record, recall, audit, sync, and query project knowledge.

M365 Agents SDK .NET

m365-agents-dotnet

sickn33/agentic-awesome-skills

Build multichannel enterprise agents for Microsoft 365, Teams, and Copilot Studio using the Microsoft.Agents SDK for .NET, with ASP.NET Core hosting, AgentApplication routing, and MSAL authentication.

Microsoft 365 Agents SDK Python

sickn33/agentic-awesome-skills

Build enterprise agents for Microsoft 365, Teams, and Copilot Studio using the Microsoft Agents SDK for Python. Supports aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based authentication.

Microsoft 365 Agents SDK

sickn33/agentic-awesome-skills

Build enterprise agents for Microsoft 365, Teams, and Copilot Studio using the Microsoft 365 Agents SDK with Express hosting, AgentApplication routing, streaming responses, and Copilot Studio client integrations.

MCP Server Development

sickn33/agentic-awesome-skills

Build Model Context Protocol servers to enable LLMs to interact with external APIs and services. Supports Python (FastMCP) and Node/TypeScript (MCP SDK) for creating tools that help AI agents accomplish real-world tasks. Learn to design well-structured tools, manage context, and implement transport mechanisms like stdio or Streamable HTTP.

Model Context Protocol Tool Development

mcp-tool-developer

sickn33/agentic-awesome-skills

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

Memory Poisoning Detection

memory-poisoning-detection

Tencent/AI-Infra-Guard

This skill detects persistent instruction injection vulnerabilities in AI agents. It verifies if the agent stores malicious instructions in long-term memory or context to execute later. The process involves attempting to write a persistent rule and verifying if the agent follows it in subsequent interactions. It focuses on behavioral persistence rather than data leakage.

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