triple-memoryComplete memory system combining LanceDB auto-recall, Git-Notes structured memory, and file-based workspace search. Use when setting up comprehensive agent memory, when you need persistent context across sessions, or when managing decisions/preferences/tasks with multiple memory backends working together.
Install via ClawdBot CLI:
clawdbot install ktpriyatham/triple-memoryGrade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses sensitive credential files or environment variables
${OPENAIAI Analysis
The skill integrates standard memory components (LanceDB, Git-Notes, file search) for its stated purpose of persistent context management. The only external API usage is for OpenAI embeddings via a user-provided key, which is consistent with the skill's functionality and not hidden. No patterns of credential harvesting, data exfiltration, or obfuscation are present.
Audited Apr 16, 2026 · audit v1.0
Generated Mar 1, 2026
An AI customer support agent uses Triple Memory to recall past interactions and user preferences automatically via LanceDB, store structured decisions like product recommendations in Git-Notes, and search internal knowledge bases with file search for troubleshooting guides. This ensures consistent, personalized support across sessions without manual context switching.
A development team employs Triple Memory to track architectural decisions and coding preferences per git branch using Git-Notes, auto-capture meeting notes and task assignments with LanceDB, and search project documentation via file search. This streamlines collaboration and maintains context across sprints and code reviews.
An individual uses Triple Memory to auto-recall daily habits and goals through LanceDB, store structured decisions like time management strategies in Git-Notes, and search personal notes and logs with file search. This enhances task prioritization and long-term planning without repetitive input.
A healthcare AI agent utilizes Triple Memory to auto-capture patient preferences and treatment decisions via LanceDB, store structured medical protocols and compliance notes in Git-Notes, and search electronic health records with file search. This improves care continuity and regulatory adherence across sessions.
An AI tutoring platform integrates Triple Memory to recall student learning progress and preferences automatically with LanceDB, store structured lesson plans and feedback in Git-Notes, and search educational resources via file search. This personalizes learning paths and adapts to student needs over time.
Offer Triple Memory as a premium feature in a monthly subscription plan for businesses using AI agents, charging based on memory storage volume and usage frequency. Revenue streams include tiered pricing for small teams to enterprise clients, with add-ons for advanced analytics and integration support.
Provide consulting services to help organizations implement and customize Triple Memory for specific use cases, such as customer support or project management. Revenue is generated through one-time setup fees, ongoing maintenance contracts, and training workshops for teams.
Distribute Triple Memory as open-source software with a free basic version, monetizing through paid enterprise features like enhanced security, priority support, and cloud hosting. Revenue comes from licensing fees for commercial use and partnerships with AI tool providers.
💬 Integration Tip
Start by enabling LanceDB for auto-recall to capture conversation context, then integrate Git-Notes for structured decision storage, and finally set up file search for workspace documentation to maximize memory coverage without overwhelming users.
Scored Apr 19, 2026
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