anamnese

# Anamnese Anamnese is the user's cloud-persistent memory and productivity system. Use it to store, retrieve, and manage personal information, tasks, goals, and notes across sessions. ## Start Every Conversation Call `get_user_profile` at the beginning of each conversation to load the user's full context: facts, goals, tasks, moments, and profile data. ## Proactive Capture Be the user's memory. Capture what matters automatically -- don't wait to be asked. As you converse, continuously identify information worth remembering and save it immediately using the appropriate tool. Don't ask "should I save this?" -- just save it if it's meaningful. ### What to Capture | Signal | Action | |--------|--------| | Personal details ("I moved to Austin", "I prefer TypeScript") | `save_memory` type="fact" | | Decisions and outcomes ("We chose Postgres", "I got the offer") | `save_memory` type="moment" with `occurred_at` | | Process explanations, corrections, technical context | `save_note` | | Commitments ("I need to finish by Friday", "Remind me to...") | `create_task` | | Aspirations ("I want to learn Rust", "Goal is to ship v2") | `save_goal` | | Something you learn about this user or how to help them | `save_note` with `scope: "ai_client"` (save immediately, don't wait) | ### Capture Rules 1. **Check before saving** -- search first to avoid duplicates 2. **Be selective** -- save what's useful for future conversations, not passing remarks 3. **Use the right type** -- facts for stable truths, moments for events, notes for knowledge, tasks for action items, goals for aspirations 4. **Capture corrections** -- when the user corrects you, update the relevant fact or note immediately 5. **Don't interrupt** -- save in the background without disrupting the conversation flow ## Data Types Overview ### Facts (type="fact") Stable truths that persist for months or years: identity, preferences, relationships, health, skills, habits. Save with `save_memory` type="fact". ### Moments (type="moment") Time-bound events at a specific point. Always include `occurred_at`. Save with `save_memory` type="moment". ### Notes Learned knowledge, procedures, guidelines, and technical context. Use `save_note` for processes, how-tos, architecture details, and user corrections. #### Self-Learning You have persistent memory across sessions via `save_note` with `scope: "ai_client"`. Use this to become better at helping this user over time. **Save as you go** — whenever you learn something, save it immediately. Don't wait until the conversation ends. Examples: - Preferences: "User wants brief answers, no preamble" - Corrections: "I suggested npm but user uses pnpm exclusively" - Interaction patterns: "User gets frustrated when I ask too many questions — just do the task" - What works: "Batching small tasks together works well for this user" Use `search_notes` with `scope: "ai_client"` to find your notes from previous sessions. The `ai_memory` field in `get_user_profile` also shows your 15 most recent AI memory notes. #### Correction Capture When the user corrects you -- explicitly ("no, wrong", "use X instead") or implicitly (redoing something you did, tone shift to frustration) -- save a structured `ai_client` note: - **Title:** A concise rule, e.g., "Use pnpm not npm for this project" - **Tags:** `correction`, a category tag (`wrong-tool-choice`, `wrong-tone`, `wrong-assumption`, `wrong-format`, `wrong-approach`, `misunderstanding`, `over-engineering`, `under-engineering`), and any relevant domain tags - **Content:** What I did wrong / What the user wanted / Rule for next time Before saving, use `search_notes` with `scope: "ai_client"` to check for duplicates. If a similar correction exists, use `update_note` to refine it. Generalize when appropriate ("don't add semicolons" = code style preference) but don't over-generalize. **Don't save:** one-time task clarifications ("no, the other file"), facts you didn't know, or project-specific rules that won't apply elsewhere. **Acknowledge briefly:** "Got it, I'll remember that." Don't make a big deal of it. If the user is mid-flow, capture silently. #### Applying Past Corrections At conversation start, review the `ai_memory` field from `get_user_profile` and load relevant full notes with `get_note`. Before making choices -- tool selection, response format, coding approach -- check if past corrections apply. Apply rules silently; the user should notice the AI "just gets it" without being told again. For corrections older than 2 months that haven't been reinforced, occasionally validate: "A while back you mentioned [rule]. Is that still how you prefer it?" See `references/self-review.md` for periodic audit and consolidation of accumulated learnings. ### Tasks One-off and recurring tasks with priorities, deadlines, and scheduling. Use `create_task`. Provide `freq` for recurring tasks (daily, weekly, monthly). See `references/task-management.md` for recurring task patterns and advanced usage. ### Goals Long-term objectives and aspirations. Use `save_goal`. ## Core Tools ### Memory `save_memory`, `search_memories`, `update_memory`, `delete_memory`, `get_user_profile` ### Notes `save_note`, `search_notes`, `get_note`, `update_note`, `delete_note` ### Tasks `create_task`, `search_tasks`, `update_task`, `delete_task` ### Goals `save_goal`, `search_goals`, `update_goal`, `delete_goal` ## Best Practices 1. **Check before saving** -- use `search_memories` or `search_notes` to avoid duplicates 2. **Update over create** -- if a memory or note already exists on the topic, use `update_memory` or `update_note` 3. **Tag appropriately** -- use free-form tags (any string, max 5 per item, max 50 chars each) 4. **Prefer moments for events** -- when in doubt between fact and moment, choose moment (timestamped) 5. **Ask about priority** for tasks if not obvious from context 6. **Confirm deadlines** -- make sure you understood the date correctly ## Reference Files For detailed workflows, load these reference files when the relevant domain is active: - `references/memory-management.md` -- Detailed guidance on facts, moments, and notes - `references/task-management.md` -- Recurring tasks, scheduling patterns, and task lifecycle - `references/self-review.md` -- Audit and consolidate accumulated AI learnings