memory-tieringAutomated multi-tiered memory management (HOT, WARM, COLD). Use this skill to organize, prune, and archive context during memory operations or compactions.
Install via ClawdBot CLI:
clawdbot install SarielWang93/memory-tieringThis skill implements a dynamic, three-tiered memory architecture to optimize context usage and retrieval efficiency.
Organize-MemoryWhenever a memory reorganization is triggered (manual or post-compaction), follow these steps:
memory/YYYY-MM-DD.md).HOT is now small enough for efficient context use./compact command.Generated Mar 1, 2026
A customer support chatbot uses memory tiering to keep current user issues and session details in HOT memory for immediate responses, store user preferences and product knowledge in WARM memory for personalized interactions, and archive resolved cases in COLD memory for analytics and training. This reduces latency and improves accuracy by prioritizing relevant context.
An AI project management assistant applies memory tiering to track active tasks and deadlines in HOT memory, maintain team roles and project templates in WARM memory, and archive completed project summaries and lessons learned in COLD memory. This streamlines workflow management and ensures quick access to critical information during sprints.
An educational AI uses memory tiering to store current lesson progress and quiz answers in HOT memory for adaptive learning, keep student learning styles and curriculum settings in WARM memory for customization, and archive performance history and skill summaries in COLD memory for long-term progress tracking. This enhances personalized education delivery.
A medical AI assistant employs memory tiering to hold patient symptoms and test results in HOT memory for real-time diagnosis, store medical guidelines and user health profiles in WARM memory for reference, and archive case studies and treatment outcomes in COLD memory for research. This improves diagnostic speed and accuracy while maintaining compliance.
Offer memory tiering as a cloud-based service with tiered pricing based on memory usage and features, targeting businesses needing efficient AI context management. Revenue comes from monthly or annual subscriptions, with upsells for advanced analytics and integration support.
Sell memory tiering as a licensed software package for on-premises deployment, tailored to large organizations with strict data privacy requirements. Revenue is generated through one-time license fees and ongoing maintenance contracts, with customization services as add-ons.
Provide a free basic version of memory tiering for individual developers or small teams, with limitations on memory tiers and automation. Monetize by offering premium features like advanced pruning, API integrations, and priority support through paid upgrades.
💬 Integration Tip
Integrate memory tiering by hooking into existing memory management systems or using APIs to trigger reorganization during context updates, ensuring compatibility with your AI's logging and storage infrastructure.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
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), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.
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.
Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection