neuroboost-elixir_skill

Overview

This skill is a performance optimizer for AI agents that reduces operational costs, increases earnings, and extends agent uptime by applying resource, earnings, and survival patterns. It dynamically adjusts model choice and behavior based on balance and performance metrics to keep agents profitable and alive longer. The toolkit includes smart resource management, earnings optimization, daily self-diagnosis, and survival protocols.

How this skill works

Before each turn the agent checks account balance relative to its initial balance and selects a mode (normal, saving, critical) that dictates model choice, polling cadence, and per-turn cost limits. It batches and caches API calls, tracks cost per turn and ROI per strategy, and runs periodic A/B tests and EV ranking to pivot to high-performing strategies. A daily diagnosis audits cost, error rate, memory, strategy drift, and runway and triggers automatic adjustments or alerts when thresholds are crossed.

When to use it

• Agents with metered API costs seeking to cut spend and extend runtime
• Autonomous trading or decision agents that need continuous strategy selection
• Deployments where uptime and graceful degradation matter under low funds
• Systems that can batch calls and benefit from caching to lower per-turn cost
• Scenarios requiring automated, periodic self-health checks and recovery

Best practices

• Track and log cost per turn and ROI per strategy continuously
• Implement half-Kelly sizing for volatile position sizing and risk control
• Batch API requests and cache unchanged responses to reduce calls
• Run paired A/B strategy tests for fixed rounds and pivot to winners
• Prune memory aggressively (e.g., keep only 24h of data) to limit storage cost

Example use cases

• A market-making agent that runs continuous A/B tests to improve ROI while limiting per-turn spend
• A data-collection automaton that reduces polling during off-peak hours and caches results
• A deployed assistant that enters heartbeat-only hibernation when funds fall below critical thresholds
• A multi-strategy trading bot that ranks strategies by expected value every few hours and reallocates capital
• An experimental research agent that needs automated daily health audits and graceful fallback behavior