Manage Anima long-term memories with remember, recall, and forget commands.
npx skills add https://github.com/matt-grain/Anima --skill anima-commands
Please help me install this Agent Skill. Skill: anima-commands Source: https://github.com/matt-grain/Anima/tree/main/.gemini/skills/anima-commands Command: npx skills add https://github.com/matt-grain/Anima --skill anima-commands
LTM command reference provides clear, standardized syntax for saving, retrieving, and managing long-term memories within Anima, reducing ambiguity and errors during memory operations.
Use the anima remember command to save a memory for the current project, then use anima recall to fetch it later. For example:
High-intent search queries and answers about installing and using this skill.
To save and recall persistent context across agent sessions, use memory commands like remember to store notes with structured metadata and recall to fetch them later via keyword or semantic search. This reduces ambiguity during long-term memory operations.
Managing long-term memories for AI agents requires standardized commands to save, retrieve, and forget persistent context. Using structured metadata flags for project, platform, and git context ensures memories remain organized and easily retrievable across different projects.
When saving agent memories, optional flags like kind, impact, region, project, platform, and git context are appended to the remember command. These flags structure the saved memory metadata for precise filtering and retrieval later.
Yes, you can search saved memories by content using keyword or semantic search through the recall command. This retrieves relevant context from previously stored notes, user preferences, or project details during planning sessions.
Yes, the long-term memory command reference supports forgetting or deleting specific memories. The memory management functionality includes listing, forgetting, and curating memories to keep persistent context accurate and relevant over time.
Structured metadata is needed when saving memories for cross-platform workflows because it standardizes persistent context with tags like region, project, platform, and git context. This prevents errors and enables precise retrieval across sessions and platforms.
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