overkill-memory-systemProvides a neuroscience-inspired 6-tier automated memory system with WAL protocol, semantic search, emotional tagging, and value-based retention for OpenClaw...
Install via ClawdBot CLI:
clawdbot install broedkrummen/overkill-memory-systemGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Potentially destructive shell commands in tool definitions
eval (Accesses system directories or attempts privilege escalation
/var/log/Calls external URL not in known-safe list
https://github.com/ollama/ollamaAI Analysis
The skill contains potentially destructive shell commands (eval) and accesses system directories (/var/log/) which could enable privilege escalation or system modification. While no confirmed data exfiltration is present, the combination of unsafe shell operations and system access creates significant security risk.
Audited Apr 16, 2026 · audit v1.0
Generated Mar 22, 2026
An AI agent uses the memory system to recall past customer interactions, preferences, and error corrections in real-time, providing personalized and context-aware responses. Its speed-first architecture ensures sub-5ms query times for seamless live chat or email support, while spaced repetition helps retain important customer details over time.
The system helps a personal AI assistant manage tasks, habits, and notes across sessions with automatic importance weighting and emotional tagging. It leverages multi-agent support for shared knowledge among devices, and background sync ensures data backup without slowing down daily query performance for reminders or schedule lookups.
In healthcare, the memory system enables an AI to store and retrieve patient histories, treatment outcomes, and medical research with high precision using its knowledge graph and semantic search. Error-tracking and self-improvement features allow it to learn from corrections, while the WAL protocol ensures no critical data is lost during sessions.
An AI tutor uses the system to adapt lessons based on student progress, errors, and engagement levels via neuroscience integration like VTA for motivation. Spaced repetition optimizes review schedules, and the 6-tier architecture allows fast access to curriculum materials, enhancing interactive learning experiences.
A financial AI agent employs the memory system to track market trends, user investment preferences, and past analysis errors with versioned facts. Its hybrid neuroscience approach filters noise and ranks insights quickly, supporting real-time decision-making in trading or portfolio management with reliable, fast data retrieval.
Offer the memory system as a cloud-based service for AI developers, charging monthly fees based on usage tiers like query volume or storage. Revenue comes from enterprises needing high-speed, reliable memory for their AI agents, with premium features like advanced analytics or priority support.
License the technology to larger AI platform providers or hardware manufacturers, integrating it into their ecosystems. Revenue is generated through upfront licensing fees or royalties per deployment, targeting companies that value the neuroscience-inspired architecture and speed optimizations for competitive advantage.
Provide consulting services to tailor the memory system for specific industries, such as healthcare or finance, with custom integrations and training. Revenue streams include project-based fees and ongoing maintenance contracts, leveraging the system's flexibility and multi-agent support for complex client needs.
💬 Integration Tip
Focus on optimizing the speed-first architecture by pre-loading common queries and using the ultra-hot tier for frequent accesses to minimize latency in production environments.
Scored Jun 19, 2026
Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvem...
Stop waiting for prompts. Keep working.
Turn OpenClaw into a learning-loop agent with seeded workspace rules, skill promotion, reflective memory, and proactive maintenance.
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Complete toolkit for creating autonomous AI agents and managing Discord channels for OpenClaw. Use when setting up multi-agent systems, creating new agents, or managing Discord channel organization.