sessionmemoryYour agent forgets everything after compaction? This fixes it. Built by the AI Advantage community — the world's leading AI learning platform (aiadvantage.ai...
Install via ClawdBot CLI:
clawdbot install moltbotmolty-del/sessionmemoryGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://aiadvantage.aiAudited Apr 18, 2026 · audit v1.0
Generated Apr 20, 2026
A project manager uses an AI agent to track weekly meetings, decisions, and action items across a multi-month software development project. The agent compacts old messages to manage context limits, but this skill ensures all specific details, like who agreed to what deadline or why a feature was deprioritized, remain searchable via the glossary and session transcripts.
A support team employs an AI agent to handle ongoing customer inquiries over weeks. The agent summarizes past interactions to free up context, but this skill preserves full conversation transcripts and auto-generates a glossary of customers, issues, and resolutions, enabling the agent to recall exact problem details and previous solutions without manual note-taking.
Researchers use an AI agent to document brainstorming sessions, experimental decisions, and literature reviews for a scientific study. As sessions compact, this skill converts all discussions into searchable Markdown files and builds a glossary of key terms, people, and timelines, ensuring no critical insights or references are lost during long-term projects.
A legal firm integrates an AI agent to assist with case preparation by recording client meetings, strategy discussions, and legal research. The skill automatically indexes sessions into a structured glossary of parties, dates, and decisions, allowing the agent to quickly retrieve specific statements or precedents even after context compaction, improving accuracy and efficiency.
A content marketing team uses an AI agent to plan and draft articles over multiple sessions. The agent compacts old brainstorming notes, but this skill maintains full transcripts and a glossary of topics, projects, and editorial decisions, enabling seamless continuity and reference to past ideas without manual archiving.
Offer this skill as part of a premium AI agent platform subscription, targeting businesses that rely on long-running AI assistants for operations. Revenue comes from monthly fees based on usage tiers, such as the number of sessions indexed or glossary size, with additional support for custom entity detection.
Provide bespoke setup and customization services for enterprises needing tailored memory systems. This includes configuring known people/projects, optimizing cron jobs, and integrating with existing workflows, generating revenue through project-based contracts or hourly consulting rates.
Distribute the core skill for free to build a community and attract users from platforms like ClawHub. Monetize by offering advanced features, such as enhanced analytics, priority support, or cloud-based auto-indexing, through a paid tier while leveraging the open-source base for adoption.
💬 Integration Tip
Ensure cron jobs are set up with incremental options to avoid reprocessing all sessions, and customize the glossary scripts with known entities relevant to your domain for better accuracy.
Scored Apr 20, 2026
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...
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.
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.
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.
You MUST use this for gathering contexts before any work. This is a Knowledge management for AI agents. Use `brv` to store and retrieve project patterns, dec...