Cap concurrent high-cost reasoning sessions and reclaim idle compute slices.
npx skills add https://github.com/AesopScott/mojo --skill attention-governor
Please help me install this Agent Skill. Skill: attention-governor Source: https://github.com/AesopScott/mojo/tree/main/harnesses/skills/attention-governor Command: npx skills add https://github.com/AesopScott/mojo --skill attention-governor
This Skill addresses runaway AI costs and inefficient resource allocation by providing a structured framework to cap concurrent high-cost reasoning sessions and reclaim idle compute slices.
Use the attention-governor skill to inspect the current Orchestrator cost boundary and propose a new concurrency limit for the project.
High-intent search queries and answers about installing and using this skill.
You can cap concurrent reasoning sessions by defining strict boundary parameters for token usage and API calls within the Orchestrator harness. This applies a hard limit on concurrent high-cost operations to maintain strict cost efficiency and prevent budget overruns.
Resource reclamation for idle compute slices is the process of automatically identifying and preempting stalled or idle AI reasoning sessions. This prevents compute waste by reclaiming unused resources within the Orchestrator harness to optimize operational costs.
You set token budget boundaries by defining strict operational limits for token usage, storage, and API calls within the Orchestrator harness. These defined boundary parameters allow the system to cap high-cost reasoning sessions and maintain financial limits.
You need to implement a hard cap when a project exceeds its allocated token budget. Capping concurrent reasoning sessions ensures the system remains within financial and operational limits during high-cost AI operations.
Yes, the attention-governor Skill requires defined boundary parameters and failure mode identification. It also requires verification of positive and boundary test cases to properly adjust system-level policy and resource allocation.
A key limitation is the requirement for predefined boundary parameters and failure mode identification before implementation. Without verifying positive and boundary test cases, automated resource reclamation may inadvertently preempt necessary idle processes.
AesopScottValidate budget constraints before dispatching tasks in the Cost harness.
AesopScottRegister evidence types and sufficiency thresholds for the Proof Authority harness.
AesopScottSuspend idle sessions and compact state data in Session Manager harnesses.
AesopScottEnforce token usage ceilings per route in AI dispatch harnesses.
