From mem0
Browses all stored memories grouped by category with full content display. Useful for reviewing project decisions, conventions, and learnings, onboarding, or getting an overview of captured knowledge.
How this skill is triggered — by the user, by Claude, or both
Slash command
/mem0:tourThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Show the user what mem0 has stored for the current project.
Show the user what mem0 has stored for the current project.
When invoked with --all-projects (e.g., /mem0:tour --all-projects or
/mem0:tour --all-projects auth middleware), search across ALL projects:
get_memories with filters={"AND": [{"user_id": "<active_user_id>"}]}, page_size=200 — no app_id filter.search_memories with query=<query>,
filters={"AND": [{"user_id": "<active_user_id>"}]}, top_k=20 — again no app_id.app_id first, then by category within each project.## <app_id_1> (<N> memories) ← current
**Architecture Decisions** — <memory content>
...
## <app_id_2> (<N> memories)
...
<N> memories across <M> projects
← (current) in the heading.If --all-projects is NOT present, use the standard single-project flow below.
When /mem0:tour receives a search query argument (e.g., /mem0:tour auth middleware)
WITHOUT --all-projects, run in peek mode — compact one-liner results:
search_memories calls:
query=<query>, filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}, top_k=10, rerank=truequery=<query>, filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "decision"}}]}, top_k=5, rerank=true## mem0 search: "<query>" (<N> results)
1. [decision] Auth module uses JWT with RS256 keys (2025-05-15) [mem0:a3f8b2c1]
2. [anti_pattern] Don't use symmetric HS256 — leaked in env (2025-05-10) [mem0:7e2d9f4a]
3. [convention] All middleware in src/middleware/ (2025-05-08) [mem0:c4d5e6f7]
Format: <number>. [<type>] <content, 80 chars> (<date>) [mem0:<short_id>]No memories matching "<query>" for project <project_id>.If no query argument and no --all-projects flag, use the full tour flow below.
Call get_memories to fetch all memories for this project:
filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}, page_size=100
In parallel, run these search_memories calls to get relevance-ranked results for key topics:
query="architecture decisions design choices", filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}, top_k=10, rerank=truequery="bugs errors failures anti-patterns", filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}, top_k=10, rerank=truequery="project setup tooling conventions preferences", filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}, top_k=10, rerank=trueDo NOT filter by metadata.type in these calls. The platform auto-assigns categories — filtering on metadata.type misses memories that were auto-categorized but don't have an explicit metadata.type.
Merge all results by memory ID (deduplicate). For each memory, determine its group using this priority:
categories field (array on each memory, auto-assigned by Mem0). Use the first category value.metadata.type field (if present, set explicitly by hooks/agent). Use as fallback if no categories.Map category names to display names:
| Platform category / metadata.type | Display name |
|---|---|
architecture decisions, architecture_decisions, decision | Architecture Decisions |
anti patterns, anti_patterns, anti_pattern | Anti-Patterns |
task learnings, task_learnings, task_learning | Task Learnings |
coding conventions, coding_conventions, convention | Coding Conventions |
user preferences, user_preferences, user_preference | User Preferences |
project profile, project_profile | Project Profile |
tooling setup, tooling_setup, environmental | Tooling & Setup |
technology, professional_details | Tooling & Setup |
session_state | Session State |
compact_summary | Compact Summaries |
| anything else | Other |
Sort groups by descending memory count. For each group that has results, print:
## <display_name> (<count> memories)
- <full_memory_content> (score: <similarity_score_if_available>)
- ...
Show the full memory text for each entry — do NOT truncate. If a group has more than 10 entries, show top 10 by recency (or similarity score if from a search call) and note ... and <N> more.
For groups with zero results, skip them entirely — don't print empty groups.
<N> memories across <M> categories — project: <project_id>, branch: <active_branch>
If zero memories found for this project, print:
No memories stored yet for project <project_id>.
Run /mem0:onboard to import project files, or start working — mem0 captures learnings automatically.
npx claudepluginhub mem0ai/mem0 --plugin mem04plugins reuse this skill
First indexed Jun 11, 2026
Implements 3-layer memory search workflow to recall past work, decisions, errors, and project history token-efficiently via layered functions.
Recalls past work, decisions, error solutions, and project history via a 3-layer memory search workflow for token-efficient retrieval.
Manages AI memories: saves decisions/patterns/facts with categories/tags, searches context, lists/deletes entries, rescans project architecture via MCP tools and /remember /forget.