Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
neuroboost-elixir v5.1.1 Changelog - Updated MARKETING.md with new information or improvements. - Updated package.json metadata to reflect latest changes. - No core functional or behavioral changes to the skill itself in this version.
What This Skill Does
A system prompt framework that adds cognitive optimization, persistent cross-session memory, automated health monitoring, self-healing, performance metrics, and multi-agent coordination to AI agents. It packages 25 configuration-level optimizations into a single CLAUDE.md or system prompt block. The framework was developed during a 30+ day continuous autonomous agent deployment.
It combines memory persistence, self-healing, performance metrics, and multi-agent coordination into a single copy-paste system prompt block, removing the need to build each capability separately.
When to use it
- Recovering agent task state automatically after a context reset
- Monitoring agent health with a single composite score across five dimensions
- Running coordinated pipelines across multiple AI agents without manual synchronization
- Reducing token waste by lazy-loading identity and skill files on demand
- Auto-generating repair rules when the same agent error recurs
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: My agent feels slow and tasks keep getting lost between sessions
- 1Loads INDEX.md and metrics.json to retrieve current performance data
- 2Calculates Agent Health Score across Efficiency, Cognition, Memory, Evolution, and Outcome dimensions
- 3Scans .issues/open-* to surface tasks lost during prior context resets
- 4Identifies the lowest-scoring dimension and the specific metrics driving it down
- 5Outputs targeted actions to address the bottleneck and restore task continuity
AHS score with per-dimension breakdown, list of recovered open tasks, and ranked improvement actions