Monitoring Skill

Predicts CPU spikes using Random Forest regressor, monitors system resources, saves metrics, and generates Excel reports.

Security Scan

The skill's code and instructions are consistent with a local predictive monitoring tool: it reads local process and system metrics, stores them in a local SQLite DB, trains a Random Forest model, and writes Excel reports; it does not request credentials or make obvious external network calls.

Purpose & Capability

Name and description (predictive monitoring, CPU spikes, Excel reports) match the included code and declared Python dependencies (psutil, pandas, scikit-learn, openpyxl). Capturing process CPU/memory and storing to a local SQLite DB is expected for this purpose.

Instruction Scope

SKILL.md only instructs the agent to run the bundled monitoring.py (with an optional --predict flag) and to check monitoring.db in the skill directory. The script's actions (psutil to enumerate processes, sqlite writes, local Excel file creation) are limited to local system state and files and align with the stated purpose. There are no instructions to read unrelated system files or to send data externally.

Install Mechanism

Registry shows 'instruction-only' (no install spec) but the bundle contains an install.yaml file that lists an install_path under an npm-global location and a 'read: system-info' permission. The presence of requirements.txt (Python libs) is expected. Because there's no active install spec in the registry, nothing will be automatically downloaded or executed beyond running the provided Python script, but you should confirm how the platform will handle the included install.yaml if used.

Credentials

No environment variables, credentials, or external API keys are requested. The script uses socket.gethostname() and records process/application names and metrics — local but potentially sensitive information — which is proportionate to a monitoring tool.

Persistence & Privilege

The skill is not marked always:true and does not request elevated platform privileges. It writes its own monitoring.db and alert records in the skill directory, which is expected for local persistence. Autonomous invocation (default) is allowed by platform but not, by itself, a red flag here.

Assessment

This skill appears internally consistent with a local monitoring/prediction tool, but review these before installing: 1) The script collects process names and resource usage and stores them locally (monitoring.db) — this can reveal running application names and should only be run on hosts you control. 2) Requirements install pandas, scikit-learn, psutil and openpyxl via pip; install these packages from trusted indexes and consider running in a virtualenv. 3) The bundle includes install.yaml (with an npm-like install_path) even though the registry lists no install spec — confirm your platform won't execute that file unexpectedly. 4) The provided file listing is truncated in the prompt; if possible, inspect the complete monitoring.py for any network calls or hidden behavior before granting runtime. 5) Run the skill in an isolated environment first (or on a non-production host) if you have any doubt.

Predictive Monitoring Skill

This skill monitors system resources and predicts future CPU spikes based on past data.

Commands

/collect-metrics

Triggers a collection of the top 10 CPU and Memory consuming processes. The results are saved to a local SQLite database monitoring.db.

/predict-usage

Analyzes collected CPU metrics, trains a Random Forest model, and predicts CPU behavior for the next 24 hours. High CPU usage alerts (Amber, Red) are saved to the Alert table.

/generate-report

Generates an Excel report of the latest captured metrics.

Instructions for Agent

To collect metrics (without prediction), run python {baseDir}/monitoring.py. To collect metrics and run predictive analysis, run python {baseDir}/monitoring.py --predict. To verify the database, look for monitoring.db in the skill directory.