smithery/neversight

faion-ai-agents

AI agents: autonomous agents, multi-agent systems, LangChain, LlamaIndex, MCP.

Installation

$ npx skills add smithery/neversight --skill faion-ai-agents

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsRead, Write, Edit, Glob, Grep, Bash, Task, AskUserQuestion, TodoWrite

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 6,242 B
  • docs SUMMARY.md 101 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Entry point: /faion-net — invoke this skill for automatic routing to the appropriate domain.

AI Agents Skill

Communication: User's language. Code: English.

Purpose

Specializes in AI agent development and orchestration. Covers autonomous agents, multi-agent systems, frameworks, and MCP.

Scope

Area Coverage
Agent Patterns ReAct, plan-and-execute, reasoning-first
Autonomous Agents Agent loops, memory, tool use
Multi-Agent Coordination, communication, delegation
Frameworks LangChain, LlamaIndex agent implementations
MCP Model Context Protocol, Claude tools
Governance EU AI Act compliance, safety

Quick Start

Task Files
Basic agent ai-agent-patterns.md → agent-patterns.md
Autonomous agent autonomous-agents.md → agent-architectures.md
Multi-agent multi-agent-basics.md → multi-agent-patterns.md
LangChain agents langchain-agents-architectures.md
MCP integration mcp-model-context-protocol.md → mcp-ecosystem-2026.md

Methodologies (26)

Agent Fundamentals (4):

  • ai-agent-patterns: Core patterns, memory, planning
  • agent-patterns: ReAct, chain-of-thought, reflection
  • agent-architectures: System design, components
  • autonomous-agents: Loops, decision-making, persistence

Multi-Agent (4):

  • multi-agent-basics: Fundamentals, communication
  • multi-agent-patterns: Delegation, collaboration
  • multi-agent-design-patterns: Hierarchical, peer-to-peer

LangChain (7):

  • langchain-basics: Setup, chains, components
  • langchain-chains: LCEL, sequential, routing
  • langchain-memory: Conversation, summary, entity
  • langchain-workflows: Complex flows, branching
  • langchain-agents-architectures: Agent types, tools
  • langchain-agents-multi-agent: Multi-agent with LangChain
  • langchain-patterns: Production patterns

LlamaIndex (3):

  • llamaindex-basics: Data connectors, indexes
  • llamaindex-indexes-queries: Query engines, retrievers
  • llamaindex-agents-eval: Agent implementation, evaluation

MCP & Tooling (4):

  • mcp-model-context-protocol: Protocol fundamentals
  • model-context-protocol: Specification
  • mcp-ecosystem: Available servers, tools
  • mcp-ecosystem-2026: Latest developments

Governance (2):

  • ai-governance-compliance: Frameworks, best practices
  • eu-ai-act-compliance: Risk tiers, requirements
  • eu-ai-act-compliance-2026: Latest updates

Advanced (2):

  • agentic-rag: Agent-driven retrieval (duplicated in RAG)
  • reasoning-first-architectures: Extended thinking patterns

Agent Architectures

ReAct Pattern

Input → Thought → Action → Observation → Thought → ... → Answer

Plan-and-Execute

Input → Plan → Execute Step 1 → Execute Step 2 → ... → Synthesize

Reasoning-First

Input → Extended Thinking → Plan → Execute → Answer

Code Examples

Basic ReAct Agent (LangChain)

from langchain.agents import create_react_agent, AgentExecutor
from langchain_openai import ChatOpenAI
from langchain.tools import Tool

tools = [
    Tool(
        name="Calculator",
        func=lambda x: eval(x),
        description="Math calculator"
    )
]

llm = ChatOpenAI(model="gpt-4o")
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({"input": "What is 25 * 17?"})

Multi-Agent System

from langchain.agents import initialize_agent, Tool
from langchain_openai import ChatOpenAI

# Define specialized agents
researcher = ChatOpenAI(model="gpt-4o")
writer = ChatOpenAI(model="gpt-4o")

# Orchestrator delegates tasks
orchestrator = initialize_agent(
    tools=[
        Tool(name="research", func=research_agent),
        Tool(name="write", func=writer_agent)
    ],
    llm=ChatOpenAI(model="gpt-4o"),
    agent="zero-shot-react-description"
)

result = orchestrator.invoke("Research AI trends and write a summary")

MCP Server Integration

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=[{
        "name": "get_weather",
        "description": "Get weather data",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            }
        }
    }],
    messages=[{"role": "user", "content": "Weather in NYC?"}]
)

LlamaIndex Agent

from llama_index.agent import ReActAgent
from llama_index.llms import OpenAI
from llama_index.tools import QueryEngineTool

llm = OpenAI(model="gpt-4o")

tools = [
    QueryEngineTool.from_defaults(
        query_engine=query_engine,
        name="docs",
        description="Documentation search"
    )
]

agent = ReActAgent.from_tools(tools, llm=llm)
response = agent.chat("How do I use embeddings?")

Multi-Agent Patterns

Pattern Use Case
Hierarchical Manager delegates to specialists
Peer-to-Peer Agents collaborate as equals
Sequential Chain of agents, each refines
Parallel Multiple agents work simultaneously

MCP Ecosystem (2026)

Server Purpose
filesystem File operations
postgres Database queries
puppeteer Web automation
github GitHub API access
slack Slack integration

EU AI Act Compliance

Risk Tier Requirements
Unacceptable Banned (social scoring, manipulation)
High-risk Conformity assessment, documentation
Limited-risk Transparency obligations
Minimal-risk No obligations

Related Skills

Skill Relationship
faion-llm-integration Provides LLM APIs
faion-rag-engineer Agentic RAG integration
faion-ml-ops Agent evaluation

AI Agents v1.0 | 26 methodologies