An automated utility for system health monitoring and memory optimization within the Kim Assistant environment.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install active-maintenance
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 active-maintenance 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 Active Maintenance Skill?
The Active Maintenance Skill is an essential utility designed to provide automated system health and memory metabolism for Openclaw Skills. Inspired by the ClawIntelligentMemory project, this skill ensures that the Kim Assistant environment remains clean and efficient by managing disk usage and refining stored data. Its primary purpose is to maintain a high-performance workspace by preventing resource bloat and ensuring memory remains dense and actionable.
By implementing this skill, developers can ensure their agents operate in a stable environment. The tool handles the heavy lifting of system administration and data pruning, allowing the AI to focus on execution while maintaining a transparent audit trail of all maintenance activities through dedicated decision logging.
Active Maintenance Skill Use Cases
- Automating nightly system health checks to monitor disk usage and critical resource availability.
- Pruning aged temporary files and build artifacts to maintain workspace cleanliness.
- Consolidating memory fragments through deduplication and distillation of dense notes into core insights.
- Creating a transparent audit trail of system changes and optimizations for administrative review.
How Active Maintenance Skill Works
- The skill initiates a system health check to monitor disk usage and resource levels against defined thresholds.
- It identifies and removes temporary files and artifacts based on user-defined age configurations.
- The Memory Metabolism (M3) process triggers to deduplicate memory fragments and distill complex information into core insights.
- Every maintenance action and system decision is recorded in a structured log for full auditability and transparency.
Active Maintenance Skill Setup
To perform a manual maintenance cycle, execute the optimization script via the CLI:
python3 /root/.openclaw/workspace/scripts/nightly_optimizer.py
Configuration is handled within scripts/nightly_optimizer.py. You can customize the following parameters:
TEMP_DIRS: Define the list of directories to be monitored for cleanup.threshold: Set the disk usage percentage that triggers system warnings.days: Specify the maximum age for temporary files before they are purged.
To integrate decision logging into custom workflows, use the provided Python module:
from decision_logger import log_decision
log_decision(title="Maintenance Update", ...)
Active Maintenance Skill Data Schema & Taxonomy
The Active Maintenance Skill organizes its data and configuration using a structured taxonomy to ensure Openclaw Skills remain organized:
| Path | Type | Description |
|---|---|---|
MEMORY/DECISIONS/ |
Directory | Stores structured logs of every maintenance cycle and system decision. |
scripts/nightly_optimizer.py |
Configuration | The central file for defining cleanup paths and resource thresholds. |
TEMP_DIRS |
Environment | User-defined locations for temporary file storage and automated purging. |
Active Maintenance Skill Advanced Features
- Memory Metabolism (M3) for high-efficiency data deduplication and fragment management.
- Resource distillation capabilities that summarize dense data sets into actionable intelligence.
- Automated threshold-based alerting for critical disk and system resource usage.
- Integrated decision logging framework for maintaining a permanent history of environment optimizations.
SKILL.md
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