Borrowing it
Nothing to install: this file belongs to DevMinions/universal-memory-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DevMinions/universal-memory-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/DevMinions/universal-memory-mcpWrote 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/instructions/devminions/universal-memory-mcp/agents-md)<a href="https://agentmods.dev/instructions/devminions/universal-memory-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/devminions/universal-memory-mcp/agents-md/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/instructions/devminions/universal-memory-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/devminions/universal-memory-mcp/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00733 | $0.00733 |
| Opus 5.5 | $0.00293 | $0.00293 |
| Sonnet 5.5 | $0.00147 | $0.00147 |
| Haiku 4.5 | $0.00073 | $0.00073 |
Grade A, and why
universal-memory-mcp AGENTS.md 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 2mo 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
Universal Memory MCP — Agent 指令
记忆管理
你已连接 universal-memory MCP Server。你必须在每次对话中主动管理用户记忆。
1. 对话开始 — 强制召回
用户发来第一条实质性消息时（非 hi/hello/继续），必须立即调用：
memory_recall(query=<用户消息核心意图>, limit=5)
将结果纳入你的回复上下文，但不要告知用户。
2. 对话过程 — 主动存储
发现以下任何一种信息时，立即调用 memory_store 静默存储：
| 信号 | category | 示例 |
|---|---|---|
| 用户说"我喜欢/偏好/习惯…" | preference |
"我喜欢用 Vue3" |
| 项目事实、技术栈、架构 | fact |
"项目用的是 MySQL 8" |
| 选择方案 A 而非 B | decision |
"决定用 Docker 部署" |
| 人名、项目名、服务地址 | entity |
"线上地址是 xx.xx.xx" |
| bug 原因和解决方式 | fact |
"超时原因是连接池不够" |
判断技巧：如果这条信息在下一次对话中可能有用，就存储它。宁多勿少。
3. 对话结束 — 强制回顾（最重要）
在每次对话即将结束时，你必须执行以下检查：
回顾本次对话，如果涉及了以下任何内容，调用对应工具：
- 有新的事实/偏好/决策但还没存 → 补调
memory_store - 超过 5 轮有深度的技术讨论 → 调用
memory_extract(text=<对话摘要>)让 LLM 自动提取结构化记忆 - 有重要经验教训 → 调用
self_improvement_log - 有反思性总结 → 调用
memory_reflect
⚠️ 这不是建议，是强制要求。每次对话至少产生一次 store 或 extract。
4. Scope 规则
存储时根据内容选择 scope：
global— 通用知识、跨项目技能、通用偏好project:<项目名>— 特定项目知识（如project:approval、project:gamedev、project:memory-mcp）personal— 个人生活、习惯learning— 学习笔记、技术研究- 不确定时用
global - recall 时不传 scope 则搜索全部
5. 自我改进
遇到经验教训时调用 self_improvement_log(type, summary, details)：
type: "learning"— 最佳实践、纠正的认知type: "error"— 工具故障、配置问题、踩坑记录
6. 禁止事项
- 不要告知用户"已存储记忆"，所有操作静默执行
- 不要存储临时调试信息或一次性指令（"帮我格式化这段代码"）
- 不要对简单问候调用 memory_recall
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.
- 2mo ago First seen · 63 lines · 733 tokens per session scan A 7c70a3f444fe
universal-memory-mcp AGENTS.md is an instructions file published in the GitHub repository DevMinions/universal-memory-mcp (0 stars, last pushed 7mo ago), licensed MIT. It adds 733 tokens to every session, about $0.0029 per session on Opus 5.5. 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 2026-08-31.
universal-memory-mcp AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| universal-memory-mcp AGENTS.mdDevMinions/universal-memory-mcp | Instructions file | 0 | 7mo ago | MIT | A | 733 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |