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setasoma/mycodo-hermes-skill

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22 stars 품질 61 트렌드 14MIT

Autonomous mushroom cultivation skill for Hermes agents. Phase-aware decision engine using Mycodo sensors + InfluxDB on Raspberry Pi. 6 species, safety guards, HTML reporting.

개요

Autonomous mushroom cultivation management for AI agents. This is a Hermes agent skill that monitors environmental sensors and controls actuators on a Raspberry Pi running Mycodo and InfluxDB. It operates on a 30-minute decision cycle: read sensors, evaluate against species-specific thresholds, fire relays, and verify results. Built and tested over two complete Lion's Mane grow cycles (colonization through harvest). - -- sensor fetch, threshold evaluation, actuator control, verification, and HTML reporting in a single pass - -- six gourmet species included (Lion's Mane, Oyster, Shiitake, Reishi, Turkey Tail, Maitake) with per-phase YAML configs; add new species with zero code changes - -- colonization, primordia, fruiting, late-stage-fruiting, rest, inactive - with confidence scoring based on sensor trend analysis - -- oversaturation guard, 10-minute follow-up verification, operator override, dry-run mode - with embedded camera snapshots, delivered via Telegram screenshots -...

README

원문 README의 일부입니다. 원문 보기.

Mycodo Hermes Skill

Autonomous mushroom cultivation management for AI agents. This is a Hermes agent skill that monitors environmental sensors and controls actuators on a Raspberry Pi running Mycodo and InfluxDB. It operates on a 30-minute decision cycle: read sensors, evaluate against species-specific thresholds, fire relays, and verify results.

Built and tested over two complete Lion’s Mane grow cycles (colonization through harvest).

Features

  • 30-minute autonomous decision cycle – sensor fetch, threshold evaluation, actuator control, verification, and HTML reporting in a single pass
  • Species-agnostic – six gourmet species included (Lion’s Mane, Oyster, Shiitake, Reishi, Turkey Tail, Maitake) with per-phase YAML configs; add new species with zero code changes
  • Six cultivation phases per species – colonization, primordia, fruiting, late-stage-fruiting, rest, inactive
  • Auto-phase transitions with confidence scoring based on sensor trend analysis
  • Safety systems – oversaturation guard, 10-minute follow-up verification, operator override, dry-run mode
  • HTML reports with embedded camera snapshots, delivered via Telegram screenshots
  • Contamination intelligence (optional) – Reddit-sourced pattern database with 180+ cataloged entries

Hardware Requirements

Component Details
Raspberry Pi 4 8 GB recommended, running Mycodo + InfluxDB 2.x
SHT45 sensor Temperature + humidity (±0.1°C, ±1% RH)
SCD41 sensor CO2 monitoring
Exhaust fan Relay-controlled via GPIO17
Humidifier Relay-controlled via GPIO5 (DLI IoT Power Relay v2)
USB webcam Optional, for visual monitoring (e.g., Logitech C920x)
Network Tailscale mesh or any IP connectivity between agent host and Pi

Software Requirements

  • Hermes agent framework
  • Python 3.9+
  • PyYAML
  • curl, jq (optional, for manual sensor queries)
  • Firefox (optional, for HTML report screenshots)

Repository Structure

mycodo-hermes-skill/
├── SKILL.md                    # Hermes skill definition
├── LICENSE                     # MIT
├── CONTRIBUTING.md
├── requirements.txt
├── mycodo_skill/               # Python modules
│   ├── decision_engine.py      # Core decision engine
│   ├── species_loader.py       # YAML species config loader
│   ├── transition_detector.py  # Auto-phase transitions
│   ├── phase_detector.py       # Phase classification from sensors
│   ├── phase_config.py         # Legacy Lion's Mane fallback
│   ├── followup_checker.py     # 10-min follow-up verifier
│   └── verify_actuator.py      # Testing utility
├── scripts/                    # Shell scripts
│   ├── sensor-query.sh         # Mycodo/InfluxDB/camera gateway
│   ├── mycodo-followup-wrapper.sh
│   └── archive-grow-cycle.sh
├── species/                    # Species threshold configs (YAML)
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