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/remember

Stores a memory verbatim from user input with appropriate type classification and metadata. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a decision, preference, convention, or learning.

From plugin
mem0
63k32 skills1 MCP
Install
$ npx -y skills add mem0ai/mem0 --skill remember --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/remember

Context preview

The summary Claude sees to decide when to auto-load this skill.

Stores a memory verbatim from user input with appropriate type classification and metadata. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a decision, preference, convention, or learning.

SKILL.md

remember.SKILL.md
name: remember
description: Stores a memory verbatim from user input with appropriate type classification and metadata. Use when the user says remember this, save this, store this, note that, or explicitly asks to record a decision, preference, convention, or learning.

Mem0 Remember

Store a fact or learning directly into mem0.

Execution

Step 1: Extract the content

The user provides the content as an argument: `/mem0:remember <text>`

If no text was provided, ask: "What should I remember?"

Step 2: Classify the memory

Based on the content, pick the best `metadata.type`:

| Content signal | Type | |---|---| | "we decided...", "always use...", "never..." | `decision` | | "X doesn't work because...", "don't try..." | `anti_pattern` | | "I prefer...", "use X instead of Y" | `user_preference` | | "the convention is...", "we always..." | `convention` | | "learned that...", "figured out..." | `task_learning` | | setup, env, tooling, config | `environmental` | | anything else | `task_learning` |

Step 3: Store

Call `add_memory` with:

  • `text="<the user's text>"`
  • `user_id=<active_user_id>`
  • `app_id=<active_project_id>`
  • `metadata={"type": "<classified_type>", "branch": "<active_branch>", "confidence": 1.0, "source": "remember_command"}`
  • `infer=False`

`infer=False` because the user stated the fact explicitly — no extraction needed. `confidence=1.0` because the user explicitly asked to store this.

Step 4: Confirm

The `add_memory` response returns `event_id` (not `memory_id`) because writes are async. Call `get_event_status(event_id=<event_id>)` once.

  • If status is `SUCCEEDED`: print the memory ID from the result.
  • If status is `PENDING` or `processing`: print with the event ID as fallback.
Remembered as <type>: "<content, first 80 chars>"
Memory ID: <id from event status>

Append `...` only if content was truncated (longer than 80 chars).

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Ships withmem0

Mem0 ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions.

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Python
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Apache-2.0
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2d ago
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3y ago
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Repo: mem0ai/mem0

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