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yevhens-hueyevhens-hue/claude-skills-starter-kit/skills/signal-based-ai-agent/SKILL.md

signal-based-ai-agent

Skill4 starsChanged 3 months agoMITView on GitHub

Signal-Driven Autonomous AI Agent Architecture. Implements event-driven triggers, market/lead signal classification, state machine transitions, and automated multi-step tool execution with audit trails.

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SKILL.md

⚡ Signal-Based Autonomous AI Agent Architecture

Production framework for building reactive, event-driven AI agents that execute multi-step workflows based on incoming telemetry, market signals, lead actions, and webhook events.


🏛️ System Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│ ⚡ SIGNAL-DRIVEN AGENT ENGINE (Python / FastAPI / Redis / LangGraph)         │
├─────────────────────────────────────────────────────────────────────────────┤
│ 1. SIGNAL INGESTION & EVENT BUS:                                            │
│    • Webhook Triggers: Stripe charge, Intercom conversation, Form submit    │
│    • Polling / Streaming: RSS Feeds, Market Tickers, Video Open Telemetry    │
│    • Idempotent Message Ingestion (Deduplication via SHA-256 + Redis Lock)  │
├─────────────────────────────────────────────────────────────────────────────┤
│ 2. SIGNAL EVALUATOR & CLASSIFICATION (Fast / Deterministic):                │
│    • Fast Filter (Claude 3.5 Haiku / Rule Engine):                          │
│      - Priority Score (0-100)                                               │
│      - Intent Type: {CHURN_RISK, BUYING_SIGNAL, SUPPORT_ESCALATION, FRAUD} │
├─────────────────────────────────────────────────────────────────────────────┤
│ 3. AGENTIC STATE MACHINE & TOOL EXECUTION:                                  │
│    • LangGraph State Graph with Dynamic Branching                           │
│    • Safe Tool Invocation with Rate Limiting & Schema Validation            │
│    • Human-in-the-Loop (HITL) Gate for High-Risk Actions (> $500 impact)    │
├─────────────────────────────────────────────────────────────────────────────┤
│ 4. OBSERVABILITY & AUDIT TRAIL:                                             │
│    • Full Telemetry: Latency, Token Usage, Tool Call Diffs                  │
│    • Real-Time Dispatch: Slack Alerts, Telegram Digest, CRM Update          │
└─────────────────────────────────────────────────────────────────────────────┘

💻 Python Reference Implementation (LangGraph + Redis)

from typing import Dict, Any, TypedDict
from langgraph.graph import StateGraph, END
import asyncio
import logging

logger = logging.getLogger("signal-agent")

class SignalState(TypedDict):
    signal_id: str
    event_type: str
    payload: Dict[str, Any]
    priority_score: int
    intent: str
    action_taken: str
    requires_human_review: bool

def classify_signal(state: SignalState) -> SignalState:
    event = state["event_type"]
    payload = state["payload"]
    
    # Deterministic rule + LLM extraction
    if event == "high_value_video_view":
        state["priority_score"] = 95
        state["intent"] = "HOT_LEAD_ENGAGEMENT"
    elif event == "payment_failed":
        state["priority_score"] = 80
        state["intent"] = "CHURN_PREVENTION"
    else:
        state["priority_score"] = 40
        state["intent"] = "GENERAL_NOTIFICATION"
        
    state["requires_human_review"] = state["priority_score"] > 90
    return state

def execute_action(state: SignalState) -> SignalState:
    intent = state["intent"]
    if intent == "HOT_LEAD_ENGAGEMENT":
        # Trigger real-time sales alert & schedule follow-up
        state["action_taken"] = "DISPATCHED_INSTANT_TELEGRAM_AND_CRM_LEAD_STAGE_UPDATE"
    elif intent == "CHURN_PREVENTION":
        state["action_taken"] = "SENT_GRACEFUL_CARD_UPDATE_SMS_AND_EMAIL"
    else:
        state["action_taken"] = "LOGGED_TO_ANALYTICS_EVENT_STREAM"
    return state

# Compile State Graph
workflow = StateGraph(SignalState)
workflow.add_node("classify_signal", classify_signal)
workflow.add_node("execute_action", execute_action)
workflow.set_entry_point("classify_signal")
workflow.add_edge("classify_signal", "execute_action")
workflow.add_edge("execute_action", END)

signal_agent_engine = workflow.compile()

📌 Standard Proposal Framework for Signal & Event Agent Jobs ($40–$65/hr)

  • Milestone 1: $100 Paid Discovery & Architecture Prototype
    • Deliverable: Signal ingestion endpoint + LangGraph classification state machine proof ($100.00).
  • Milestone 2: Multi-Source Webhook Connectors & Tool Integration ($45.00/hr)
  • Milestone 3: Observability Dashboard, Sentry Alerts & Handover ($45.00/hr)

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