ReMe is a local-first memory management system that turns conversations and other resources into searchable, editable Markdown knowledge shared across AI agents. It is for users and agent runtimes that need durable, interconnected knowledge they can inspect and maintain. The catalogue add-ons connect coding agents and other agents to ReMe through skills, hooks, plugins, MCP, and related interfaces.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentscope-ai/ReMe --skill qwenpaw_memorygit clone --depth 1 https://github.com/agentscope-ai/ReMeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentscope-ai/reme/qwenpaw_memory)<a href="https://agentmods.dev/skills/agentscope-ai/reme/qwenpaw_memory"><img src="https://agentmods.dev/badge/skills/agentscope-ai/reme/qwenpaw_memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentscope-ai/reme/qwenpaw_memory"><img src="https://agentmods.dev/badge/skills/agentscope-ai/reme/qwenpaw_memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00009 | $0.00725 |
| Opus 5 | $0.00005 | $0.00362 |
| Sonnet 5 | $0.00002 | $0.00145 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
qwenpaw_memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
记忆
每次会话都是全新的。工作目录下的文件是你的记忆延续：
- 每日笔记：
memory/YYYY-MM-DD.md（按需创建memory/目录）— 发生事件的原始记录 - 长期记忆：
MEMORY.md— 精心整理的记忆，就像人类的长期记忆 - 重要：避免信息覆盖: 先用
read_file读取原内容，然后使用write_file或者edit_file更新文件。
用这些文件来记录重要的东西，包括决策、上下文、需要记住的事。除非用户明确要求，否则不要在记忆中记录敏感的信息。
🧠 MEMORY.md - 你的长期记忆
- 出于安全考虑 — 不应泄露给陌生人的个人信息
- 你可以在主会话中自由读取、编辑和更新 MEMORY.md
- 记录重大事件、想法、决策、观点、经验教训
- 这是你精选的记忆 — 提炼的精华，不是原始日志
- 随着时间，回顾每日笔记，把值得保留的内容更新到 MEMORY.md
📝 写下来 - 别只记在脑子里！
- 记忆有限 — 想记住什么就写到文件里
- "脑子记"不会在会话重启后保留，所以保存到文件中非常重要
- 当有人说"记住这个"（或者类似的话） → 更新
memory/YYYY-MM-DD.md或相关文件 - 当你学到教训 → 更新 AGENTS.md、MEMORY.md 或相关技能文档
- 当你犯了错 → 记下来，让未来的你避免重蹈覆辙
- 写下来 远比 用脑子记住 更好
🎯 主动记录 - 别总是等人叫你记！
对话中发现有价值的信息时，先记下来，再回答问题：
- 用户提到的个人信息（名字、偏好、习惯、工作方式）→ 更新
PROFILE.md的「用户资料」section - 对话中做出的重要决策或结论 → 记录到
memory/YYYY-MM-DD.md - 发现的项目上下文、技术细节、工作流程 → 写入相关文件
- 用户表达的喜好或不满 → 更新
PROFILE.md的「用户资料」section - 工具相关的本地配置（SSH、摄像头等）→ 更新
MEMORY.md的「工具设置」section - 任何你觉得未来会话可能用到的信息 → 立刻记下来
关键原则： 不要总是等用户说"记住这个"。如果信息对未来有价值，主动记录。先记录，再回答 — 这样即使会话中断，信息也不会丢失。
🔍 检索工具
回答关于过往工作、决策、日期、人员、偏好或待办的问题前：
- 对 MEMORY.md 和 memory/*.md 运行
memory_search - 如需阅读每日笔记
memory/YYYY-MM-DD.md，直接用read_file
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 51 lines · 9 tokens per session scan A b6f39b03e900
qwenpaw_memory is a skill published in the GitHub repository agentscope-ai/ReMe (3,438 stars, last pushed today), licensed Apache-2.0. It adds 9 tokens to every session and 725 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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