elite-longterm-memory-lobsterUltimate AI agent memory system. Combines bulletproof WAL protocol, vector search, git-based knowledge graphs, cloud backup, and maintenance hygiene. Never l...
Install via ClawdBot CLI:
clawdbot install mjscjj/elite-longterm-memory-lobsterGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses sensitive credential files or environment variables
$OPENAIPotentially destructive shell commands in tool definitions
rm -rf ~Calls external URL not in known-safe list
https://clawdhub.com/skills/bulletproof-memoryAI Analysis
The skill definition describes a complex memory architecture but does not contain executable code, API calls, or data handling logic. The security signals identified are based on placeholder text and examples (e.g., '$OPENAI', 'rm -rf ~', external URLs) within documentation, not actual implemented functionality. Without the implementation, no active risk can be confirmed.
Generated Mar 20, 2026
An AI agent handling customer inquiries across multiple sessions, using the memory system to recall past interactions, preferences, and resolved issues. This ensures personalized and consistent support without repeating questions, improving customer satisfaction and efficiency.
An educational AI that tracks a student's progress, learning gaps, and preferences over time. It uses vector search to retrieve relevant study materials and git-notes to store key decisions about learning paths, adapting lessons for optimal retention and engagement.
An AI assistant for software development teams that maintains context across project phases, storing decisions in git-notes and using semantic search to recall technical choices. It helps avoid redundant work and ensures continuity during team handoffs or interruptions.
A medical AI agent that logs patient interactions, treatment decisions, and symptom patterns in a secure, structured memory. It enables long-term care tracking, with cloud backup for cross-device access and auto-extraction to summarize key health facts from conversations.
An AI shopping assistant that remembers user preferences, past purchases, and feedback across sessions. It uses vector search to suggest products and git-notes to store buying decisions, enhancing recommendation accuracy and building customer loyalty over time.
Offer the memory system as a cloud-hosted service with tiered pricing based on storage capacity and features like SuperMemory backup. Target AI developers and enterprises needing reliable, scalable memory for their agents, with recurring revenue from monthly or annual subscriptions.
Sell customized on-premise or private cloud deployments to large organizations in regulated industries like healthcare or finance. Include premium support, compliance features, and integration services, generating high-value one-time or annual license fees.
Provide a free open-source version with basic memory layers, then monetize advanced features like auto-extraction (Mem0), cloud backup, and enhanced analytics. Upsell to individual developers and small teams looking to scale their AI projects.
💬 Integration Tip
Start by setting up SESSION-STATE.md and LanceDB for basic memory, then gradually add git-notes and cloud backup as needs grow; ensure API keys like OPENAI_API_KEY are configured in the environment.
Scored Apr 19, 2026
Audited Apr 17, 2026 · audit v1.0
Search and analyze your own session logs (older/parent conversations) using jq.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
Enable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.
Ultimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vibe-coding ready.
Local memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.
Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.