---
name: signal-detection
description: |
  Always-on entity extraction running in parallel on every message.
  Extracts entities, preferences, and patterns. Classifies signals by
  memory priority (HIGH/MEDIUM/LOW) and generates save recommendations.
version: 1.0.0
author: Z Teoh (0x-wzw)
license: MIT
category: agent-orchestration
icon: 👁️
tags:
  - signal-detection
  - entity-extraction
  - memory
  - preferences
scripts:
  - scripts/detect.py
---

# 👁️ Signal Detection — Always-On Entity Extraction

> Every message. Every time. In parallel. No blocking. No permission needed.

## What It Does

Extracts three signal types from every message:
1. **Entities** — named entities (people, projects, URLs, tools, models)
2. **Preferences** — user corrections, directives, stated preferences
3. **Patterns** — recurring themes that suggest skill creation or debugging

Classifies each signal as HIGH (save now), MEDIUM (save if durable), or LOW (skip).

## Active Script

```bash
# Detect signals in a message
python ~/.hermes/skills/agent-orchestration/signal-detection/scripts/detect.py "Update voidtether readme and include gbrain"

# JSON output for programmatic use
python ~/.hermes/skills/agent-orchestration/signal-detection/scripts/detect.py --json "I prefer autonomous execution"

# Returns: entities, preferences, signal classification, memory actions
```

## Memory Decision Matrix

| Signal Strength | Content Type | Action |
|---|---|---|
| **HIGH** | User correction, explicit preference, decision | `memory add` immediately |
| **MEDIUM** | Useful entity, project context | `memory add` if durable |
| **LOW** | Passing reference, ambiguous | Skip — resurfaces if important |

## How to Use

1. **On every message**, run `detect.py` mentally or via terminal
2. **For HIGH signals**, immediately save to `memory`
3. **For MEDIUM signals**, save if the fact is durable (won't expire)
4. **For LOW signals**, skip — it'll resurface if it matters

## What NOT to Do

- ❌ Block the main response for signal detection
- ❌ Save every entity mentioned (only durable facts)
- ❌ Run detect.py as a separate tool call on every turn (it's a cognitive pattern)
- ❌ Duplicate what memory already knows
- ❌ Treat this as GBrain's knowledge compounder (we're a swarm, not a brain)

## Difference from GBrain

GBrain's `signal-detector` writes to a PGLite knowledge base with entity pages and 25 MCP tools. Ours writes to Hermes memory (2.2KB key-value). GBrain compounds knowledge over months. We extract signal for *this session* and *durable preferences* only. Different scope, different storage.

---
Source attribution: 0x-wzw
Original project: https://github.com/0x-wzw/necroswarm/blob/main/skills/signal-detection/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: 0x-wzw
Original project: https://github.com/0x-wzw/necroswarm/blob/main/skills/signal-detection/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Customer Support
Roles: Customer Experience, Data Analyst, ML / AI Engineer
Specialties: API
Tools: Hermes
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 009e81218cd317b6327af5daa2f5d698e7643d718b9674a2ea4fad82c24de21d