12306-mcp
This is a 12306 ticket search server based on the Model Context Protocol (MCP).
AI Atlas + Technical Lab
Find agent-native AI tools by category, then verify the stack.
Browse by the job first: coding, automation, data, research, communication, and security. Then filter by MCP, Skill, CLI, Workflow, compatible agent, source trust, stars, and freshness.
01
Start from AI categories.
MCP / Skill / CLI / Workflow remain important, but they work better as filters than as the first door.
02
Newly published tools.
Cards show value first, then technical type, source evidence, and a quick path to the detail page.
A2V
A2V: Next-Gen AI Value Compute Protocol.
Acontext
Agent Skills as a Memory Layer
aderyn
Solidity Static Analyzer that easily integrates into your editor
adk-java
An open-source, code-first Java toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
03
Agent compatibility comes next.
Use agent pages after picking the problem space. They explain which tools, skills, MCPs, and workflows fit each agent.
global Claude Code
Anthropic's terminal-native coding agent for large repositories, MCP-connected tools, skills, hooks, and team workflows.
china CodeGeeXA China-origin IDE coding assistant focused on completion, explanation, and editor-side developer assistance.
global CodexOpenAI's coding agent for repository implementation, review, verification, and tool-assisted engineering work.
global CursorAn editor-first AI coding environment with agentic IDE workflows, rules, memories, MCP, and cursor-agent CLI support.
global Hermes AgentNous Research's open-source, terminal-oriented agent with skills, MCP integration, local execution, and a large Skills Hub ecosystem.
china Kimi CLI / Kimi AgentMoonshot AI's Kimi agent surface for long-context research, document-heavy workflows, and Kimi Code CLI development tasks.
04
Signals and Learn support the directory.
They explain why the ecosystem is moving, without taking over the tool-first homepage.
open_license
Hybrid LLM-RL Red Teaming Framework Exposes AI Security Gaps
This paper introduces an autonomous red teaming framework that combines large language models with reinforcement learning to generate adaptive, multi-stage attack campaigns against AI-enabled security systems. Testing in high-fidelity enterprise simulations reveals that standalone LLM agents cannot sustain complex attacks, while hybrid LLM-RL approaches achieve significantly higher compromise rates, exposing critical vulnerabilities in current AI security defenses.
open_licenseAgent Memory Goes Infrastructure: Memori at 14K Stars
Memori represents a new category: agent-native memory infrastructure. It's LLM-agnostic, turning agent execution traces and conversations into structured, persistent state for production systems. At 14K stars, it signals that memory is becoming a standalone infrastructure concern, separate from the agent runtime.
learnRead the field guides
Short explanations for MCP, Skills, Workflows, and agent selection.