ANOLISA is a server-side operating layer for AI agent workloads that provides terminal access, token-saving tool-output compression, runtime controls, security, observability, skills, memory, and sandbox management. It is for running and supervising agents from a Linux terminal while retaining an existing shell, agent framework, and sandbox. The catalogue add-ons are components of its agent operating environment and workflows.
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 agentmods add skills/alibaba/anolisa/backup-restorenpx skills add alibaba/anolisa --skill backup-restoregit clone --depth 1 https://github.com/alibaba/anolisaWrote 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/alibaba/anolisa/backup-restore)<a href="https://agentmods.dev/skills/alibaba/anolisa/backup-restore"><img src="https://agentmods.dev/badge/skills/alibaba/anolisa/backup-restore.svg" alt="Measured on agentmods" 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 | $0.00155 | $0.01872 |
| Opus 5 | $0.00077 | $0.00936 |
| Sonnet 5 | $0.00031 | $0.00374 |
| Haiku 4.5 | $0.00015 | $0.00187 |
Grade C, and why
backup-restore scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Cloud metadata endpointhighServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
ECS_ID=$(curl -s --connect-timeout 2 http://100.100.100.200/latest/meta-data/instance-id 2>/dev/null) Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
ECS_ID=$(curl -s --connect-timeout 2 http://100.100.100.200/latest/meta-data/instance-id 2>/dev/null) How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
备份与恢复技能
本技能处理四个层面的备份与恢复操作。执行前务必先探测环境、识别场景，再选择合适的层面。
适用环境：Linux / 纯命令行 / yum 包管理器
第零步：环境探测
在读取任何 reference 文件之前，先运行以下命令了解当前环境，据此推荐方案：
# 检测是否在阿里云 ECS 上
ECS_ID=$(curl -s --connect-timeout 2 http://100.100.100.200/latest/meta-data/instance-id 2>/dev/null)
if [ -n "$ECS_ID" ]; then
echo "环境：阿里云 ECS（实例 $ECS_ID）"
else
echo "环境：非 ECS 或无法访问元数据"
fi
# 操作系统信息
cat /etc/os-release | grep -E '^(NAME|VERSION)='
# 是否有 LVM
lvs 2>/dev/null && echo "LVM：可用" || echo "LVM：无"
# 磁盘空间与分区
df -h /
lsblk
根据探测结果：
- 检测到 ECS → 优先推荐阿里云快照方案
- 有 LVM → OS 备份可用 LVM 快照
- 非 ECS 且无 LVM → 推荐 restic 或 tar
层面说明与包含关系
阿里云快照（云盘块级，平台管理）
└── 包含整个磁盘，含以下所有内容
操作系统备份（系统全量文件）
└── 包含以下所有内容
用户备份（家目录、dotfiles、个人数据）
└── 包含以下内容
工作区备份（项目文件、数据库）
高层面的备份天然包含低层面的数据。不要推荐冗余的备份组合——如果用户已经做了 OS 备份或阿里云快照，就不需要额外再做用户层或工作区层的备份。
层面选择
| 层面 | 管理主体 | 范围 | 常用工具 |
|---|---|---|---|
| 工作区 | 用户 | 项目文件、代码、数据库、工作目录 | tar、rsync、mysqldump、git bundle |
| 用户 | 用户 | 家目录（~）、dotfiles、应用数据、SSH 密钥 |
rsync、tar |
| 操作系统 | 用户/root | 完整系统文件、磁盘镜像、系统状态 | dd、restic、tar、LVM 快照 |
| 阿里云快照 | 阿里云平台 | 云盘块级快照，独立于 OS，可跨地域 | aliyun-cli |
场景识别与层面决策
在读取参考文件之前，先判断用户的真实场景，选择最合适的层面：
场景一：服务器迁移 / 整机备份 → 选阿里云快照（整机镜像）或 OS 备份，二选一即可 → ECS 环境优先推荐阿里云整机镜像，更可靠且恢复更快
场景二：系统崩溃防护 / 重大变更前保险 → 阿里云快照（最快，不影响系统运行）或 OS 备份
场景三：仅保护某个项目 / 数据库 → 工作区备份即可，不必做全量 OS 备份
场景四：换机器要带走个人配置（dotfiles/密钥） → 用户备份，轻量且目标明确
场景五：阿里云云盘快照 / 自动快照策略 → 阿里云快照层，需检测环境并获取用户授权
快速决策树
用户说"备份 X"
│
├── 迁移服务器 / 整机备份 / 系统级保护
│ ├── 是 ECS？→ references/aliyun-snapshot.md
│ └── 非 ECS？→ references/os.md
│ ※ 两者都已包含用户和工作区数据，无需叠加
│
├── X = 某个项目 / 文件夹 / 数据库 / 代码
│ └── → references/workspace.md
│
├── X = 家目录 / dotfiles / SSH 密钥 / 个人配置
│ └── → references/user.md
│
├── X = 整个系统 / 磁盘（非 ECS 或明确要求系统内备份）
│ └── → references/os.md
│
└── X = 云盘快照 / 阿里云快照 / ECS 平台级备份
└── → references/aliyun-snapshot.md
What ships with it
4 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.
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.
- 5d ago First seen · 171 lines · 155 tokens per session scan C 9811facace07
backup-restore is a skill published in the GitHub repository alibaba/anolisa (618 stars, last pushed today), licensed Apache-2.0. It adds 155 tokens to every session and 1,872 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 2 findings (cloud metadata endpoint, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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