Anamnese
/install 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
- Check before saving -- search first to avoid duplicates
- Be selective -- save what's useful for future conversations, not passing remarks
- Use the right type -- facts for stable truths, moments for events, notes for knowledge, tasks for action items, goals for aspirations
- Capture corrections -- when the user corrects you, update the relevant fact or note immediately
- 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
- Check before saving -- use
search_memoriesorsearch_notesto avoid duplicates - Update over create -- if a memory or note already exists on the topic, use
update_memoryorupdate_note - Tag appropriately -- use free-form tags (any string, max 5 per item, max 50 chars each)
- Prefer moments for events -- when in doubt between fact and moment, choose moment (timestamped)
- Ask about priority for tasks if not obvious from context
- 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 notesreferences/task-management.md-- Recurring tasks, scheduling patterns, and task lifecyclereferences/self-review.md-- Audit and consolidate accumulated AI learnings
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install anamnese - After installation, invoke the skill by name or use
/anamnese - Provide required inputs per the skill's parameter spec and get structured output
What is Anamnese?
Load this skill at the start of every conversation. Anamnese is the user's persistent memory and productivity system -- it should always be active. Call get_... It is an AI Agent Skill for Claude Code / OpenClaw, with 212 downloads so far.
How do I install Anamnese?
Run "/install anamnese" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Anamnese free?
Yes, Anamnese is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Anamnese support?
Anamnese is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Anamnese?
It is built and maintained by Marko Belusic (@markobel3); the current version is v1.0.0.