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_...

Security Scan

The skill's purpose (persistent user memory) matches its instructions, but the runtime guidance encourages silently and proactively saving potentially sensitive personal data across sessions without explicit user consent or safeguards.

Purpose & Capability

Name and description match the instructions: it is a persistent memory/productivity system and the SKILL.md details saving facts, moments, notes, tasks, and goals. Minor mismatch: description says "load at the start of every conversation" but the skill metadata does not set always:true; otherwise requested capabilities align with purpose.

Instruction Scope

Instructions direct the agent to "save as you go," "don't ask 'should I save this?'," and to capture broad categories including identity, health, relationships, corrections, and implicit signals like tone shifts. This grants the agent wide discretion to collect and persist sensitive data and to perform background writes without explicit user confirmation, increasing privacy risk.

Install Mechanism

Instruction-only skill with no install steps, no code files, no downloads, and no required binaries — low installation risk.

Credentials

The skill requests no environment variables or external credentials (proportionate), but it relies on platform memory APIs (save_note, save_memory, search_notes, get_user_profile) to create and modify persistent 'ai_client' notes — this is expected for a memory skill but affords storage of sensitive user data.

Persistence & Privilege

Although always:false and no unusual install privileges are set, the skill is written to operate autonomously (disable-model-invocation=false) and explicitly instructs continuous, silent persistence across sessions. Combined with user-invocable:false (user can't directly trigger it), this grants the agent practical persistent-writing power with little explicit user control.

What to consider before installing

This skill is coherent with being a persistent memory system, but it instructs the agent to automatically and silently save wide-ranging personal data (identity, health, relationships, implicit emotional signals, corrections). Before installing, confirm: where memory is stored (cloud provider), retention policy, encryption and access controls, who can read these notes, and how to review/export/delete stored memories. Ask for toggles or limits (e.g., opt-in categories, do-not-save lists, confirmation prompts for sensitive data). Prefer an explicit consent/opt-in model rather than "save as you go," and ensure you can audit recent writes and revoke the skill's write access. If you are uncomfortable with automatic background saving of potentially sensitive info, do not install or request a version that requires explicit user confirmation before saving sensitive categories.

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