Store durable facts and project state in size-capped markdown notes.
npx skills add https://github.com/tinqiao-oss/engramory --skill engramory
Please help me install this Agent Skill. Skill: engramory Source: https://github.com/tinqiao-oss/engramory/tree/main Command: npx skills add https://github.com/tinqiao-oss/engramory --skill engramory
This Skill solves the problem of fragmented, unmanaged agent memory by providing a strict, human-readable, and curated protocol for storing durable facts, project state, and behavioral agreements.
Use the engramory skill to initialize a new memory store in the current project directory and wire the discipline into your agent rules.
High-intent search queries and answers about installing and using this skill.
AI agent memory can persist across sessions using a file-based markdown protocol with a bounded index, storing durable facts and project state in atomic notes instead of an opaque vector database. This approach ensures persistent storage of user preferences and procedural feedback without external dependencies.
A curated memory protocol for AI agents enforces memory hygiene by using a one-file-one-fact approach with a typed ontology. It categorizes facts into user, feedback, project, and reference types, ensuring stored knowledge remains actionable and clean through strict curation discipline and size-capped index validation.
Prevent context window overflow by implementing a size-capped index that bounds the volume of stored facts. This mechanism enforces memory hygiene through strict curation discipline, ensuring critical information is always available while preventing unbounded memory growth that exceeds context limits.
Yes, you can use markdown files to store project-specific coding standards. The protocol uses atomic markdown notes with a typed ontology, allowing you to maintain consistent behavioral agreements and project state across multiple sessions in a human-readable format.
The best way to manage fragmented agent memory is using a strict, human-readable protocol with atomic markdown notes. This approach solves unmanaged memory fragmentation by enforcing curation discipline and a bounded index, keeping stored facts editable and actionable without relying on opaque systems.
AI agents forget user preferences between sessions when they lack a persistent long-term memory protocol. Implementing a file-based memory store with a bounded index and typed ontology ensures user preferences and behavioral agreements are durably stored and retrieved across multiple interactions.
alex-voloshin-devCreates the .ai-skills-memory directory structure with subdirectories for logs, designs, and knowledge sync.
jackspaceStore and retrieve persistent memories across sessions with full-text search.
Zhow01Manage structured agent memory with a typed ontology graph via Python CLI.
shreed27Generate vector embeddings and perform semantic search against collections.
