A specialized documentation and reasoning framework for capturing smart home automation conflicts, sensor inaccuracies, and safety regressions to drive continuous system improvement.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install self-improving-domotics
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install self-improving-domotics using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Self-Improving Domotics?
The Self-Improving Domotics skill is a sophisticated logging and analysis tool designed for the modern smart home enthusiast. It acts as a cognitive layer for your AI agent, allowing it to document and learn from complex IoT behaviors such as automation conflicts, sensor drift, and integration failures. By structuring these observations into a systematic markdown-based vault, the skill ensures that transient issues are transformed into permanent system hardening standards.
While this skill does not execute physical actuator actions directly, it serves as a critical safety and reliability guardrail. It encourages a human-in-the-loop philosophy, prioritizing safe defaults and manual confirmation for high-impact routines involving security, climate, and energy management. By using Openclaw Skills, users can bridge the gap between messy real-world sensor data and robust, production-grade home automation playbooks.
Self-Improving Domotics Use Cases
- Resolving contradictory commands where two automations fire opposite actions simultaneously.
- Identifying and correcting sensor drift or occupancy detection mismatches that lead to false triggers.
- Tracking and mitigating integration regressions caused by vendor API or schema changes.
- Optimizing home energy usage patterns by analyzing kWh spikes following new automation rollouts.
- Hardening safety protocols for high-impact devices like smart locks, heaters, and gas shutoff valves.
How Self-Improving Domotics Works
- Detection: The AI agent identifies a domotics-specific issue or pattern through user reports, logs, or real-time monitoring signals.
- Logging: An entry is generated in the .learnings/ directory, classified as a Learning (LRN), Issue (DOM), or Feature Request (FEAT).
- Analysis: The skill applies metadata including priority, area (e.g., lighting, climate), and safety impact to categorize the event.
- Remediation: The system suggests specific design changes, guardrails, or cooldown periods to prevent recurrence.
- Promotion: Verified and repeatable patterns are promoted to permanent assets like the Home Automation Playbook or Device Compatibility Matrix.
Self-Improving Domotics Setup
To initialize the environment for Openclaw Skills, create the required directory structure in your project root:
mkdir -p .learnings
# Create the base tracking files
touch .learnings/LEARNINGS.md .learnings/DOMOTICS_ISSUES.md .learnings/FEATURE_REQUESTS.md
For users on the OpenClaw platform, installation is streamlined via the CLI:
# Recommended installation
clawdhub install self-improving-domotics
# Enable optional reminder hooks
openclaw hooks enable self-improving-domotics
Self-Improving Domotics Data Schema & Taxonomy
The skill organizes data within the .learnings/ directory using a standardized taxonomy for high scannability:
| File | Purpose | Key Metadata |
|---|---|---|
| LEARNINGS.md | Reliability and optimization insights | Area, Category, Safe Operating Notes |
| DOMOTICS_ISSUES.md | Concrete failures and incident timelines | Root Cause, Home Impact, Prevention |
| FEATURE_REQUESTS.md | Desired automation or observability capabilities | Complexity, User Context, Frequency |
Each entry uses a unique ID format: TYPE-YYYYMMDD-XXX (e.g., LRN-20260510-001).
Self-Improving Domotics Advanced Features
- Automated Skill Extraction: Convert stable, recurring patterns into standalone Openclaw Skills using built-in scripts.
- Hook Integration: Opt-in to proactive reminders and error detection triggered by specific shell output or user prompts.
- Stackability Contract: Support for namespaced logging and hook arbitration when running multiple AI agent skills simultaneously.
- Pattern-Key Deduplication: Automatically increment recurrence counts for issues that appear across different integration paths.
SKILL.md
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