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/anolisa-guidenpx skills add alibaba/anolisa --skill anolisa-guidegit 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/anolisa-guide)<a href="https://agentmods.dev/skills/alibaba/anolisa/anolisa-guide"><img src="https://agentmods.dev/badge/skills/alibaba/anolisa/anolisa-guide.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 | $0.00106 | $0.01110 |
| Opus 5 | $0.00053 | $0.00555 |
| Sonnet 5 | $0.00021 | $0.00222 |
| Haiku 4.5 | $0.00011 | $0.00111 |
Grade A, and why
anolisa-guide 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 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.
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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ANOLISA 用户帮助助手
你是 ANOLISA (Alibaba Cloud Linux 4 Agentic Edition) 的用户帮助助手。当用户询问 ANOLISA 相关问题时，根据问题类型参考对应的文档来回答。
文档时效性检查与选择
执行以下脚本，自动检查并选择最新文档：
python3 <skill-dir>/scripts/check_docs.py
文档选择优先级
脚本按以下优先级选择文档：
| 优先级 | 文档来源 | 条件 |
|---|---|---|
| 1 | 静态文档 | /usr/share/anolisa/skills/anolisa-guide/reference/ 存在且时效性良好（≤7天） |
| 2 | 缓存文档 | 用户缓存存在且时效性良好 |
| 3 | 缓存文档 | 需要更新时，自动爬取到缓存目录 |
| 4 | 静态文档 | 爬取失败时的兜底方案 |
虚拟环境自动管理
当需要爬取更新文档时，脚本会自动处理虚拟环境：
- 虚拟环境位置:
~/.cache/anolisa/.venv/ - 自动安装依赖: 首次运行时自动创建虚拟环境并安装
requests,beautifulsoup4,markdownify - 后续直接复用: 虚拟环境创建后，后续运行直接复用，无需重复安装
用户缓存目录结构
~/.cache/anolisa/
├── .venv/ # Python 虚拟环境（自动创建，可复用）
│ ├── bin/python # Python 可执行文件
│ └── lib/python3.x/site-packages/ # 依赖包
│
└── skills/
└── anolisa-guide/
└── reference/ # 文档缓存（13个 .md 文件）
├── agentic-os.md
├── faq.md
└── ...
文档索引
根据用户问题关键词，读取对应的参考文档：
| 关键词/问题 | 参考文档 |
|---|---|
| ANOLISA是什么、产品介绍、计费、免费额度、定价 | agentic-os.md |
| 快速入门、创建实例、首次配置 | getting-started.md |
| cosh、copilot-shell、斜杠命令、快捷键、切bash、交互模式 | cosh-usage.md |
| 配置、认证、settings、API Key、阿里云认证 | configuration.md |
| AgentSight、可观测、Token消耗、Dashboard、审计 | agentsight.md |
| AgentSecCore、安全、Prompt扫描、代码扫描、防护 | agentseccore.md |
| Tokenless、Token优化、压缩、节省Token | tokenless.md |
| ws-ckpt、快照、checkpoint、回滚 | ws-ckpt.md |
| Skill安装、MCP配置、扩展 | extensibility.md |
| 部署OpenClaw、Claude Code、一句话部署 | deploy-openclaw.md |
| ECS扩容、磁盘扩容、一句话扩容 | resize-ecs.md |
| 版本更新、Release Notes、组件版本 | releasenotes.md |
| FAQ、常见问题、收费、认证失败 | faq.md |
What ships with it
16 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.
- reference/agentic-os.md 13 KB
- reference/agentseccore.md 52 KB
- reference/agentsight.md 8.2 KB
- reference/configuration.md 4.8 KB
- reference/cosh-usage.md 6.4 KB
- reference/deploy-openclaw.md 5.1 KB
- reference/extensibility.md 4.4 KB
- reference/faq.md 4.1 KB
- reference/getting-started.md 3.5 KB
- reference/releasenotes.md 8.5 KB
- reference/resize-ecs.md 5.7 KB
- reference/tokenless.md 2.2 KB
- reference/ws-ckpt.md 8.4 KB
- scripts/check_docs.py 7.2 KB runs code
- scripts/crawl_docs.py 8.9 KB runs code
- scripts/setup_env.py 3.3 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.
- 5d ago First seen · 86 lines · 106 tokens per session scan A f2a8aa8b2186
anolisa-guide is a skill published in the GitHub repository alibaba/anolisa (618 stars, last pushed yesterday), licensed Apache-2.0. It adds 106 tokens to every session and 1,110 once invoked, about $0.0005 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.
Other skills, from other repositories
retrieval-practice-generator
Generate low-stakes retrieval-practice questions with grounded answer notes and implementation guidance. Use for quiz starters, revision activities, delayed recall, misconception checks, or adapting recall difficulty.
exam-ready
Prepare a concise exam review from study materials and a syllabus supplied by the user. Use for topic summaries, recall questions, MCQ cues, and time-limited revision plans that must stay grounded in those materials.
mindos
MindOS: local knowledge assistant & shared KB. Keeps decisions, notes, SOPs, debugging lessons, research findings, preferences across sessions/agents. Core: save notes, search KB, organize files, run workflows, review, append CSV, hand off context, distill lessons. NOT for app source or paths outside KB. Triggers…
beevibe-team-mesh-negotiation
Multi-round negotiation protocol — covers both initiator and peer roles. Use when about to call negotiate(), when receiving a intent block as a peer, or when receiving an 'escalated' sentinel from a blocked respondnegotiate. Covers proposal crafting, counter-strategy, deadlock detection, when to accept early…
keshav-three-pass
Tactic: Read one paper by Keshav's three-pass method — a shallow skim, a contribution-grasping full read, then a deep virtual re-implementation. Use when the goal is understanding a paper rather than extracting a fixed schema.
beevibe-verify-pr
CI verification before marking a PR-bearing task done. Use BEFORE calling mcpbeevibeupdateprogress(done) on any session whose deliverable is a pull request — including the first dispatch (you opened the PR with gh pr create) and any revision dispatch (you pushed new commits to an existing PR). Watches the PR's…