dory-memoryFile-based memory system for AI agents that forget between sessions. Implements the "Dory-Proof" pattern for continuity across context resets. Use when setting up agent memory, building workspace structure, implementing task tracking, or preventing context-loss errors. Triggers on "memory system", "remember between sessions", "Dory pattern", "agent continuity", or "workspace setup".
Install via ClawdBot CLI:
clawdbot install justinhartbiz/dory-memoryGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Mar 1, 2026
A business deploys an AI agent for handling customer inquiries across multiple sessions. This skill ensures the agent remembers past interactions, unresolved tickets in HOLD.md, and follows exact customer instructions to prevent miscommunication. It maintains continuity in support workflows despite context resets.
An AI agent assists teams by tracking project tasks, deadlines, and decisions. Using the Dory-Proof pattern, it records exact user requests in ACTIVE.md, monitors blocked items in HOLD.md, and archives historical data in memory/ for long-term reference across planning sessions.
A marketing team uses an AI agent to generate and edit content drafts. The skill helps the agent remember brand guidelines from MEMORY.md, track ongoing edits in STAGING.md, and ensure exact client feedback is preserved in ACTIVE.md to avoid content drift between revisions.
Researchers employ an AI agent to collect and analyze data over extended periods. The memory system stores findings in dated memory/ files, logs decisions in DECISIONS.md, and uses scoring to retain critical insights in MEMORY.md, preventing loss during long-term projects.
An individual uses an AI agent for personal task management and habit tracking. The skill enables the agent to remember daily goals via ACTIVE.md, note blocked habits in HOLD.md, and maintain long-term progress in MEMORY.md, ensuring consistency across daily sessions.
Offer a cloud-based platform where businesses subscribe to deploy AI agents with built-in Dory-Proof memory. Revenue comes from monthly fees based on usage tiers, providing features like automated backups, analytics on memory usage, and integration with existing tools.
Provide consulting services to help organizations implement this memory system into their existing AI workflows. Revenue is generated through project-based fees for setup, training, and ongoing support, tailored to specific industry needs like customer support or project management.
Sell pre-configured workspace templates, such as those for customer service or content creation, through an online marketplace. Revenue comes from one-time purchases or licenses, with additional income from premium support and updates for advanced features.
💬 Integration Tip
Start by copying the template files to establish the workspace structure, then customize SOUL.md and USER.md to align the agent's identity and user context for seamless adoption.
Scored Apr 19, 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...
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.