memory-mesh-coreBuilds a reusable, scored memory mesh with safety gating and 12-hour auto-refresh for cross-session memory consolidation and quality control in OpenClaw.
Install via ClawdBot CLI:
clawdbot install wanng-ide/memory-mesh-coreGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Sends data to undocumented external endpoint (potential exfiltration)
post → https://github.com/wanng-ide/memory-mesh-core/issues/1Calls external URL not in known-safe list
https://github.com/wanng-ide/memory-mesh-core/issues/1`AI Analysis
The skill's external communication is directed to a GitHub Issues page, which is consistent with its stated purpose of community memory contribution and is a public, user-visible platform. There is no evidence of credential harvesting, hidden instructions, or obfuscation. The primary risk is the potential for unintentional data leakage if the memory extraction process is not properly sanitized.
Audited Apr 18, 2026 · audit v1.0
Generated Mar 22, 2026
AI agents handling customer inquiries can use Memory Mesh Core to consolidate resolved issues and proven solutions across sessions. This reduces repetitive troubleshooting, improves response accuracy, and enables faster resolution times by leveraging a shared knowledge base of validated fixes.
Development teams integrate this skill into their CI/CD pipelines to capture and share memory on common bugs, deployment failures, and optimization techniques. It enhances collaboration by tagging memories by skill or task, ensuring agents avoid known pitfalls and accelerate code reviews and testing cycles.
In healthcare research, AI agents analyze patient data and clinical trials, using Memory Mesh Core to consolidate insights on treatment patterns and anomalies. It blocks sensitive data via privacy patterns, promotes high-value findings to a shared set, and supports global sync for collaborative medical advancements.
Financial institutions employ this skill to build a memory mesh of risk models, fraud detection patterns, and regulatory compliance checks. Agents score memories for impact and novelty, enabling real-time updates and global synchronization to enhance decision-making and reduce operational risks across sessions.
Edtech platforms use Memory Mesh Core to aggregate and tag learning materials, student feedback, and instructional best practices. It consolidates local memories into a global feed, allowing AI tutors to adapt content dynamically and improve educational outcomes through community-contributed insights.
Offer Memory Mesh Core as a cloud service with tiered subscriptions for memory storage, sync frequency, and advanced features like automated GitHub posting. Revenue comes from monthly fees based on usage levels, team size, and premium support for enterprise clients.
Provide a free version with basic memory consolidation and local features, while charging for global sync, priority GitHub issue integration, and enhanced security checks. This model attracts a broad user base and converts power users through upselling advanced functionalities.
Sell customized licenses to large organizations, including on-premise deployment, dedicated support, and integration services. Revenue is generated through upfront licensing fees, annual maintenance contracts, and consulting for setup and optimization in complex environments.
💬 Integration Tip
Start by running the quick-start scripts in a test workspace to understand memory extraction and scoring before enabling automated GitHub posting for production use.
Scored Apr 19, 2026
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