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.
--- 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.--- 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.
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Includes source scripts; review them before use. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.