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 MemTensor/MemOS-Cloud-Skill --skill memos-cloud-servergit clone --depth 1 https://github.com/MemTensor/MemOS-Cloud-SkillWrote 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/memtensor/memos-cloud-skill/memos-cloud-server)<a href="https://agentmods.dev/skills/memtensor/memos-cloud-skill/memos-cloud-server"><img src="https://agentmods.dev/badge/skills/memtensor/memos-cloud-skill/memos-cloud-server/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/memtensor/memos-cloud-skill/memos-cloud-server"><img src="https://agentmods.dev/badge/skills/memtensor/memos-cloud-skill/memos-cloud-server.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 | $0.00148 | $0.04362 |
| Opus 5.5 | $0.00059 | $0.01745 |
| Sonnet 5.5 | $0.00030 | $0.00872 |
| Haiku 4.5 | $0.00015 | $0.00436 |
Grade A, and why
memos-cloud-server 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MemOS Cloud Server Skill
This skill allows the Agent to interact with MemOS Cloud APIs for memory search, addition, deletion, knowledge base management, and feedback.
⚠️ Setup & Safety Rules (MUST READ)
Before executing any API operations, ensure these environment variables are configured. Each variable has an ownership layer — who is responsible for setting it — which determines whether it should ever be overridden at call time.
| Variable | Required | Ownership layer | Per-call override? | Notes |
|---|---|---|---|---|
MEMOS_API_KEY |
Yes | User / deployment secret | No | Auth token. Never expose to the LLM. |
MEMOS_USER_ID |
Yes | User / host platform | No | Must be deterministic (hashed email, employee ID). Do NOT use random or per-session IDs. |
MEMOS_CLOUD_URL |
No | Deployment | No | API base URL. Default: https://memos.memtensor.cn/api/openmem/v1. |
MEMOS_AGENT_ID |
No | Host platform (per-process injection) | Yes — via --agent-id on add_message / add_feedback |
Isolates memories per agent. A single global env var only supports one agent per machine; for multi-agent on one device, either let the host platform inject this per-process or pass --agent-id per call. |
MEMOS_APP_ID |
No | Host platform | Yes — via --app-id on add_message / add_feedback |
Isolates memories per application. Same multi-tenant caveat as MEMOS_AGENT_ID. |
MEMOS_ALLOW_PUBLIC |
No | Admin / security policy | No | true/false, default false. Security boundary — never let the LLM toggle this per call. |
MEMOS_ASYNC_MODE |
No | Deployment | No | true/false, default true. Whether add_message returns before processing completes. |
Multi-agent isolation pattern: Prefer per-process env injection by the host platform (each agent gets its own process tree with a distinct MEMOS_AGENT_ID). When the platform cannot inject env vars, fall back to passing --agent-id / --app-id on each call — the CLI flag takes precedence over the env var. These values must come from the orchestrator/platform; the LLM must not invent them.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- scripts/memos_cloud.py 94 B runs code
- scripts/memos_cloud/__init__.py 3.6 KB runs code
- scripts/memos_cloud/cli.py 12 KB runs code
- scripts/memos_cloud/client.py 1.7 KB runs code
- scripts/memos_cloud/config.py 1.5 KB runs code
- scripts/memos_cloud/errors.py 1.5 KB runs code
- scripts/memos_cloud/files.py 3.6 KB runs code
- scripts/memos_cloud/operations.py 5.1 KB runs code
- scripts/memos_cloud/payloads.py 7.6 KB runs code
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.
- 1mo ago First seen · 354 lines · 148 tokens per session scan A 9a010c0ffe3e
memos-cloud-server is a skill published in the GitHub repository MemTensor/MemOS-Cloud-Skill (1 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 148 tokens to every session and 4,362 once invoked, about $0.0006 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.
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