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Displays memory usage statistics for the current session and project including counts by category, age distribution, and API latency. Use when checking how many memories exist, reviewing session activity, or auditing memory distribution across categories.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mem0ai/mem0 --skill stats命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Mem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0 config, mem0 import). Also triggers when query includes CLI flags like --user-id, --output, --json, --agent, or describes bash/zsh/terminal/shell usage. DO NOT TRIGGER when: user asks about programmatic SDK integration in Python/TS code (use mem0 skill), or Vercel AI SDK provider (use mem0-vercel-ai-sdk skill).
Integrate Mem0 into an existing repository using a goal-driven, TDD pipeline. Detects the repo's language automatically and asks the user to pick between Mem0 Platform (managed) and Mem0 Open Source (self-hosted). Writes failing tests before any implementation. Produces a local feature branch plus `.mem0-integration/` artifacts consumed by the paired verification skill. TRIGGER when: user says "integrate mem0", "add mem0 to this repo", "wire mem0 into <repo>", or asks how to add memory to an existing project. DO NOT TRIGGER when: the user wants general SDK usage (use skill:mem0), CLI usage (use skill:mem0-cli), or Vercel AI SDK (use skill:mem0-vercel-ai-sdk). After success, invoke skill:mem0-test-integration to verify in the same workspace (loose coupling).
Views or sets the project's memory-extraction policy (what Mem0 remembers vs ignores) stored in mem0.md. Use when the user says set a memory policy, custom instructions, what should mem0 remember, tell mem0 to ignore X, or asks to see/change the current instructions.
基于 SOC 职业分类
正在显示 SKILL.md
| name | stats |
| description | Displays memory usage statistics for the current session and project including counts by category, age distribution, and API latency. Use when checking how many memories exist, reviewing session activity, or auditing memory distribution across categories. |
Show session and lifetime memory statistics.
Run the session stats reporter:
SCRIPT_DIR="${CLAUDE_PLUGIN_ROOT:-${CODEX_PLUGIN_ROOT:-${CURSOR_PLUGIN_ROOT:-}}}/scripts"
python3 "$SCRIPT_DIR/session_stats.py" peek 2>/dev/null || echo "{}"
The peek command returns JSON without clearing the stats file (unlike report).
If the script returns empty or errors, note "No session data available" and continue.
Lifetime stats:
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
Group by:
categories[0] (platform-assigned) — primary groupingmetadata.type (agent-assigned) — secondary if no categoriescreated_at date — for age analysisCategory normalization: Merge auto_capture and uncategorized into a single uncategorized row. These are memories where the platform didn't assign a meaningful content category. Do NOT show auto_capture as its own row in the table.
Session stats (local only):
Session stats come from the local stats file read in Step 1. Do NOT query the API with
run_id or metadata.session_id filters — these return unreliable results because
memories are stored without run_id and metadata filters on session_id are inconsistent.
The local stats file tracks adds and searches for the current session accurately.
Also run a search_memories MCP tool call with query="project", filters={"AND": [{"user_id": "<active_user_id>"}, {"app_id": "<active_project_id>"}]}, top_k=1 to measure round-trip latency. Note the time before and after the MCP call — do NOT attempt raw HTTP calls to the API.
Print a minimal dashboard. No ASCII bar charts — use a clean table layout:
## mem0 stats
**Session** (<session_id, first 12 chars>) — 3 written, 5 searches, categories: decision, convention
**Project: my-project** — 55 memories, API: 84ms
| Category | Count |
|----------------------|-------|
| decision | 24 |
| convention | 15 |
| anti_pattern | 6 |
| task_learning | 5 |
| user_preference | 3 |
| session_state | 2 |
**Age** — oldest: 2026-02-15, newest: 2026-05-23
< 7 days: 5 · 7–30d: 12 · 30–90d: 10 · > 90d: 8
**Identity** — user: kartik · project: my-project · branch: main
Display rules:
created_atWhen invoked with --weekly (e.g., /mem0:stats --weekly), append a weekly
activity digest after the standard stats dashboard:
Call search_memories in parallel with time-scoped queries:
query="decisions made this week", filters={"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"created_at": {"gte": "<7 days ago YYYY-MM-DD>"}}]}, top_k=20query="bugs errors fixes", same time filter, top_k=20query="patterns conventions learnings", same time filter, top_k=20Merge by ID. Group into "New this week" by categories[0] or metadata.type.
Calculate: memories added last 7 days, most active categories, most active day.
Append after the standard stats:
### This week (May 16 – May 23)
+12 memories — most active: Wednesday (5)
| Category | New |
|---------------|-----|
| decision | 5 |
| task_learning | 4 |
| bug_fix | 3 |
**Highlights**
- <2-3 sentence summary of most important decisions/learnings this week>
Write to ~/.mem0/weekly-digest.md (overwrite). Append one-line to
~/.mem0/digest-history.log:
<YYYY-MM-DD> | <project_id> | +<new_count> memories | top: <top_category>
If no new memories in 7 days:
No new memories in the past week. Total: <N> memories in <project_id>.