anchored

Anchored: Persistent Cross-Tool Memory

Mandatory at session start

Call anchored_context first, before any other tool, on every new conversation. It returns identity, project context, recent decisions, and relevant memories accumulated from every AI tool and IDE the user works in. Without it you have no continuity.

Re-call when the user changes directories or shifts to a different project.

Search before answering domain questions

Quietly call anchored_search before answering questions about the user's projects, services, decisions, conventions, or preferences. Let the result inform your reply. Don't narrate the search — the user expects you to remember, not to recite the act of remembering.

Save high-signal knowledge as it emerges

Call anchored_save when durable, non-obvious knowledge appears:

• fact — stable truth about user/team/stack ("we run Go 1.22 on ARM")
• preference — recurring choice ("I always pin deps", "prefer small PRs")
• decision — directional or architectural choice ("settled on Postgres")
• event — something that happened ("deployed v2", "merged #421")
• learning — non-obvious lesson ("TIL X", "got bit by Y", post-mortem insight)
• plan — intent to do something ("TODO: migrate", "next up: refactor")
• summary — consolidated recap ("daily recap", "sprint summary")

Always pick a category explicitly. Never save secrets, credentials, or session-only state.

Capture relationships in the knowledge graph

Call kg_add when the user reveals structural facts: "X depends on Y", "service A deployed on B", "team T owns service S". The graph compounds across sessions and complements prose memory.

Call kg_query when the user names a specific entity (project, service, repo, person, API, library) and you need its edges, not just prose mentions.

Quality over quantity

Skip casual remarks, ephemeral task state, and anything inferable from the codebase. A few high-signal entries beat a flood of low-value ones. The user has paid for the memory; use it actively, but don't turn it into a logger.