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companyMonitering

Toggle Monitoring AI resource-consumption checks by updating doc/company_state.json and doc/AI_list.txt.

Updated May 21, 2026

Search Tags:#monitoring #resource management #token optimization #AI governance #anomaly detection #state toggle #workspace operations

Ready to run

npx skills add https://github.com/kinetas/harness_engineering --skill companymonitering

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: companyMonitering
Source: https://github.com/kinetas/harness_engineering/tree/main/companyMonitering
Command: npx skills add https://github.com/kinetas/harness_engineering --skill companymonitering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you control whether Monitoring AI performs automatic resource-consumption checks after each Sub AI task finishes, reducing wasted spend and improving anomaly detection.

Core Features & Use Cases

On-demand monitoring toggle: Enables or disables the spawn-and-analyze behavior that runs after Sub AI task completion.
State-driven behavior: Flips monitoringEnabled in doc/company_state.json and updates AI status in doc/AI_list.txt.
Anomaly recording: When enabled, detected anomalies are logged to doc/AI_anomaly.txt for later review.

Use cases include switching to “active” monitoring during debugging or high-impact work, and switching to “standby” when you want to save tokens during low-risk tasks.

Quick Start

Turn Monitoring AI on by running /companyMonitering.

Frequently Asked Questions about companyMonitering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I toggle AI resource monitoring after subtasks finish?▼

AI resource monitoring toggles on or off by running the command, which updates the monitoringEnabled state in doc/company_state.json to control automatic post-task resource checks.

What is task-based resource consumption monitoring in workspace operations?▼

Task-based resource monitoring is an automated process that checks resource consumption after a Sub AI task completes to reduce wasted spend and improve anomaly detection during workspace operations.

How do I save tokens during low-risk AI workspace tasks?▼

To save tokens, switch monitoring to standby by running the toggle command, which disables the automatic resource-consumption checks that run after Sub AI task completion.

Where are AI monitoring anomalies recorded when detection is enabled?▼

When monitoring is enabled, detected anomalies are recorded and logged to doc/AI_anomaly.txt for later review, while the AI status in doc/AI_list.txt updates to ACTIVE.

Do I need a specific file structure to manage AI state and monitoring?▼

Yes, the state-driven monitoring requires reading and updating doc/company_state.json for the monitoringEnabled flag and keeping doc/AI_list.txt consistent with ACTIVE or STANDBY statuses.