compute-allocator

Name: compute-allocator
Availability: InStock
Author: InnerScopeHearing

Recommend agent count, model selection, and critic-pass usage from risk signals and effort-scale baseline.

Updated May 30, 2026

Search Tags:#risk management #task dispatch #compute allocation #advisory system #compute optimization

Ready to run

npx skills add https://github.com/InnerScopeHearing/otchealth-claude-tools --skill compute-allocator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: compute-allocator
Source: https://github.com/InnerScopeHearing/otchealth-claude-tools/tree/main/skills/compute-allocator
Command: npx skills add https://github.com/InnerScopeHearing/otchealth-claude-tools --skill compute-allocator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps optimize task dispatch by providing advisory compute routing based on risk signals and effort-scale baseline, aiding in efficient resource allocation.

Core Features & Use Cases

Advisory Compute Routing: Combines risk signals with effort-scale baseline to suggest optimal compute allocation for tasks.
Risk-Based Decision Making: Uses live risk signals to recommend additional resources for tasks under higher risk.
Use Case: For instance, when a task requires more scrutiny due to a recent error spike, this Skill suggests increasing the number of agents and running a critic pass.

Quick Start

Use the compute-allocator skill to get a compute allocation recommendation for the task "Analyze the performance of the database cluster".

Frequently Asked Questions about compute-allocator

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

FAQPage Schema

How does risk-based compute allocation optimize task dispatch?▼

Risk-based compute allocation optimizes task dispatch by evaluating live risk signals against an effort-scale baseline to recommend the optimal agent count, model selection, and critic-pass usage for mitigating execution risks.

How do I determine the optimal agent count for high-stakes tasks?▼

You determine the optimal agent count by feeding task parameters into an advisory compute router, which cross-references live risk signals and effort baselines to recommend scaling up agents and applying critic passes for higher-risk scenarios.

When should I use a critic pass for compute routing?▼

You should use a critic pass for compute routing when live risk signals indicate elevated task risk, such as an error spike; the advisory system recommends applying critic passes to increase scrutiny and mitigate high-stakes execution failures.

Does this advisory compute router require external signal integrations?▼

Yes, this advisory compute router requires external integrations; it consumes risk signals from signal-radar, effort-scale baselines from fleet-dispatch, and model routing from fleet-telemetry to generate its compute allocation recommendations.

What is the best way to allocate compute resources for error spike scenarios?▼

The best way to allocate compute resources during error spike scenarios is to deploy an advisory compute router that cross-references live risk signals to recommend scaling up agent counts and enabling critic passes for added scrutiny.

What are the limitations of using an advisory system for task dispatch?▼

The main limitation of an advisory system for task dispatch is its reliance on external telemetry; without accurate, live inputs from signal-radar, fleet-dispatch, and fleet-telemetry, the compute allocation recommendations for risk mitigation may be constrained.