skills.rest
kinetas
kinetasCOMMUNITY
Explore all skills by this author →
GitHub

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
One-click install
npx skills add https://github.com/kinetas/harness_engineering --skill companymonitering
Or copy as Structured Prompt for Agent▼
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.

Related Skills

Explore More Product & Management Skills→
kinetaskinetas

interceptBoss

Switch Boss AI to standby mode while current Sub AI batches complete.

Community
Intermediate
mindcockpit-aimindcockpit-ai
11

workspace-monitor

Detect errors, warnings, and anomalies in project logs and build artifacts.

Official
Advanced
leobessaleobessa

ai-system-governance

Codify AI standards, guardrails, audit trails, and policies into organizational governance.

Community
Advanced
awinogradovawinogradov
5

pr:monitor

Monitor pull requests for review approval and CI check status.

Community
Intermediate