Store and recall session context via Muninn MCP with subagents.
npx skills add https://github.com/likesjx/philotic-stack --skill muninn-memory-habit
Please help me install this Agent Skill. Skill: muninn-memory-habit Source: https://github.com/likesjx/philotic-stack/tree/main/skills/muninn-memory-habit Command: npx skills add https://github.com/likesjx/philotic-stack --skill muninn-memory-habit
This Skill addresses the challenge of maintaining context and continuity in AI interactions by acting as a persistent working memory, ensuring that past decisions and user preferences are consistently recalled and applied.
Use the muninn memory habit skill to bootstrap the AI's working memory for the current session.
High-intent search queries and answers about installing and using this skill.
To maintain AI working memory and context continuity, the system recalls and stores session-specific context using a Muninn MCP bootstrap gate. It operates via subagents for asynchronous memory operations, retrieving project context and past decisions at the start of every session.
To persist decisions made during an AI session, the system writes them to the memory store after verifying the bootstrap gate. This logs important choices asynchronously via subagents, ensuring past decisions and user preferences are recalled and applied in future interactions.
Yes, you can use subagents for asynchronous memory operations. The system operates via subagents to handle recall and write operations after verifying the bootstrap gate, ensuring that storing session-specific context and decisions does not block the main process.
Yes, a Muninn MCP is required to bootstrap AI memory. The system integrates with a Muninn MCP for bootstrap and requires verification of this bootstrap gate before proceeding with any recall or write operations.
The best way to personalize AI interactions is by recalling user preferences and collaboration styles from a persistent memory. This approach retrieves stored context at the start of a session to tailor responses based on past decisions and specific user habits.
VetCodersBootstrap agent sessions with History, Eyes, and Verify context layers.
dlowdIdentify and report structural flaws in plans, designs, or code through adversarial review.
cpfifferManage agents and subagents via the Letta Python client.
Percona-LabStore and retrieve persistent memory using Notion pages.
