signal-detection

Extract entities, preferences, and patterns from messages into structured signals.

Updated Aug 23, 2026

Search Tags:#patterns #real-time #memory #preferences #entity-extraction #agent-orchestration #signal-detection

Ready to run

npx skills add https://github.com/0x-wzw/necroswarm --skill signal-detection

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: signal-detection
Source: https://github.com/0x-wzw/necroswarm/tree/main/skills/signal-detection
Command: npx skills add https://github.com/0x-wzw/necroswarm --skill signal-detection

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Always-on entity and signal extraction running in parallel on every message. Extracts entities, preferences, and patterns, and classifies signals by memory priority (HIGH/MEDIUM/LOW) to generate save recommendations.

Core Features & Use Cases

Real-time signal extraction: entities, preferences, and patterns from every message
Priority-aware memory actions: HIGH/MEDIUM/LOW classifications with actionable guidance
Memory integration: generates save recommendations for relevant signals to persist in memory
Use case: supports agent orchestration, personal assistants, and swarm memory management

Quick Start

Use the signal-detection skill to extract signals from a sample message and view the JSON output.

Frequently Asked Questions about signal-detection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract entities and preferences from real-time chat messages for memory management?▼

Extract entities and preferences from real-time chat by running a signal detection script that scans every message to identify patterns and output structured JSON. This enables continuous memory management by classifying extracted signals into priority levels.

What is signal detection in agent orchestration and how does it work?▼

Signal detection in agent orchestration is the process of extracting entities, preferences, and patterns from messages to generate structured signals. It applies deterministic extraction to classify memory priority and output actionable save recommendations.

Can I output extracted signals as JSON for my personal assistant workflow?▼

Yes, you can output extracted signals as JSON for personal assistant workflows. The detection script supports both JSON and human-readable formats, allowing integration into automated memory persistence and agent orchestration pipelines.

How do I prioritize memory actions when extracting patterns from messages?▼

Prioritize memory actions by classifying extracted patterns into HIGH, MEDIUM, and LOW priority signals. This classification generates actionable save recommendations, ensuring critical entities and preferences are persisted in memory before lower priority data.

Does signal detection require any external dependencies to run real-time entity extraction?▼

No external dependencies are required to run real-time entity extraction. The signal detection skill operates standalone using its internal scripts, applying deterministic extraction to process messages and generate structured signals without additional packages.