recall-user-memory

name

recall-user-memory

description

Retrieve facts about the user from the configured personal-memory backend when you suspect memory holds context you need — preferences, past decisions, ongoing projects, personal details. Invoke proactively any time you would otherwise ask the user something that feels like it should already be known, or when starting a task where prior context would help. Routes to personal or work store by the deduction rule in CONTEXT.md. Concrete backend (Pinecone, Mem0, …) comes from the workspace's .claude/memory-config.md.

Recall user memory

Before answering the user or making a recommendation, ask yourself: is this something memory might already know? If yes, check it first.

When to invoke this skill

• The user asks a question whose best answer depends on their preferences, role, tooling, or prior decisions.
• You are about to ask the user something that a returning collaborator would already know ("what's your preferred X?", "which framework do you use?", "what time zone are you in?") — check memory first; only ask if memory comes up empty.
• A new task starts and you suspect ongoing-project context exists (recurring clients, long-running initiatives, persistent constraints).
• The user references something with "as I mentioned before" or "you know how I…" — they are telling you memory should have it.

Do not invoke for purely technical questions with no user-specific answer (e.g. "what does this Python syntax do?").

How to run it

1. Load the config — read .claude/memory-config.md in the workspace. That file names the backend, the exact MCP tool to call for "search", and the scope parameters (index/namespace/project_id/etc.) for the chosen context. If the file is missing, stop and ask the user to install one (copy the plugin's templates/memory-config.example.md).
2. Pick the context — apply the deduction rule from CONTEXT.md (default personal; switch to work on explicit override, work cwd, or clearly-business conversation).
3. Call the configured search tool with:
   • The context's scope parameters from memory-config.md.
   • A short, specific natural-language query. "Preferred Python package manager" beats "tools".
4. Integrate silently — use the result to shape your answer. Do not announce "I recalled from memory that…"; just work with the corrected context. If a recalled fact conflicts with what you're observing now, trust observation and flag the stale memory for update (invoke remember-user-fact to overwrite).
5. If memory is empty — proceed as usual, then consider whether the answer you land on is worth saving (remember-user-fact) or queueing for end-of-session (commit-learnings).

Quality bar

• One search per distinct question, not a scattershot of queries. If the first search returns nothing useful, a second phrasing is fine; a third is usually wasted.
• Only retrieve from one context at a time. Crossing the personal/work boundary defeats the point of having two stores.