Smart Home KI-SYSTEM Assistant

Expert assistant for developing and maintaining the KI-SYSTEM smart home automation platform - a self-learning AI system that controls lighting, heating, and bathroom automation across Home Assistant and Homey Pro platforms.

What This Skill Does

This skill provides comprehensive guidance for working with KI-SYSTEM, including:

Step-by-Step Instructions

1. Understand Project Context First

Before making any changes:

2. Environment Setup and Testing

Always validate the environment before development:

```bash

Quick validation (preferred for rapid testing)

./quick_test.sh

Full integration test with live platform

python3 main.py test

Check current system status

python3 main.py status

```

3. Core Architecture Navigation

When working with specific features, understand these key components:

**Decision Engine** (`src/decision_engine/engine.py`):

**Platform Abstraction** (`src/data_collector/`):

**ML Models** (`src/models/`):

**Database** (`src/utils/database.py`):

4. Common Development Workflows

**Adding a New Sensor:**

1. Update `config.yaml` under `data_collection.sensors`

2. Modify platform collector's `get_sensor_data()` if needed

3. Update ML model's `prepare_training_data()` for new features

4. Collect data in learning mode (2-3 days)

5. Retrain: `python3 main.py train`

**Supporting a New Platform:**

1. Create collector inheriting from `SmartHomeCollector`

2. Implement: `test_connection()`, `get_state()`, `turn_on()`, `turn_off()`, `set_temperature()`

3. Register in `PlatformFactory.PLATFORMS`

4. Add config section to config.yaml

5. Test with `python3 main.py test`

**Modifying Decision Logic:**

1. Rules are in `config.yaml` under `decision_engine.rules`

2. Custom logic goes in `DecisionEngine.run_cycle()`

3. Safety checks in `_apply_safety_checks()`

4. Always log decisions to database

**Adding a New ML Model:**

1. Create file in `src/models/` (e.g., `blinds_model.py`)

2. Define features (brightness, time, temperature)

3. Implement: `train()`, `predict()`, `save()`, `load()`

4. Initialize in `DecisionEngine.__init__()`

5. Add prediction logic to `run_cycle()`

6. Add config section to config.yaml

5. Debugging Platform Connections

When connection issues occur:

1. Verify `.env` has correct `PLATFORM_TYPE`, URL, TOKEN (no trailing slashes)

2. Run diagnostic: `python3 main.py test`

3. For Home Assistant:

- Test with: `curl -H "Authorization: Bearer TOKEN" http://URL:8123/api/`

- Check token hasn't been revoked

- Review Home Assistant logs

4. For Homey:

- Re-authenticate: `athom login`

- Get new token: `athom user --bearer`

- Tokens expire periodically

5. Check logs: `logs/ki_system.log`

6. Database Operations

Working with data:

```bash

Direct access

sqlite3 data/ki_system.db

Useful queries

SELECT * FROM sensor_data ORDER BY timestamp DESC LIMIT 10;

SELECT * FROM decisions WHERE executed=1 ORDER BY timestamp DESC LIMIT 20;

SELECT * FROM bathroom_events ORDER BY start_time DESC LIMIT 10;

Backup before changes

cp data/ki_system.db data/ki_system.db.backup

```

7. Configuration Management

Two-tier system:

Access via `ConfigLoader`:

8. Running and Testing

```bash

Single cycle

python3 main.py run

Continuous daemon (5-minute intervals)

python3 main.py daemon --interval 300

Train models manually

python3 main.py train

Web interface

python3 main.py web --host 0.0.0.0 --port 5000

```

9. Bathroom Automation System

Key components:

Features: Auto-detects showers via humidity spikes, controls dehumidifiers, learns optimal thresholds

10. Safety and Best Practices

**Safety Mechanisms:**

**Deployment Workflow:**

1. Start with `mode: "learning"` (2-3 days)

2. Switch to `mode: "manual"` (approval-based)

3. Finally `mode: "auto"` (full automation)

**Design Patterns:**

Usage Examples

**Example 1: Adding temperature sensor to heating model**

```

User: Add outdoor temperature sensor to the heating prediction model

Assistant steps:

1. Read config.yaml to identify current sensors

2. Add new sensor entity ID under data_collection.sensors.temperature

3. Modify temperature_model.py to include outdoor temp in feature vector

4. Update prepare_training_data() method

5. Run ./quick_test.sh to validate

6. Suggest running in learning mode for 2-3 days

7. Provide command: python3 main.py train

```

**Example 2: Debugging Home Assistant connection**

```

User: Getting connection errors to Home Assistant

Assistant steps:

1. Check .env file for PLATFORM_TYPE, HA_URL, HA_TOKEN

2. Run: python3 main.py test

3. Test API manually with curl command

4. Check logs/ki_system.log for specific errors

5. Verify Home Assistant is accessible and token is valid

6. Suggest regenerating long-lived access token if needed

```

**Example 3: Analyzing bathroom automation performance**

```

User: Want to see bathroom automation insights

Assistant steps:

1. Direct user to web UI at /bathroom/analytics

2. Query bathroom_events table for recent events

3. Check bathroom_learned_parameters for current thresholds

4. Review bathroom_device_actions for control history

5. Suggest running bathroom_optimizer.py for optimization

6. Show sample queries for detailed analysis

```

Important Constraints

Key Files to Reference