xns-ai-cookbooks
Working recipes for AI pipelines on XNS — S3-compatible storage, zero egress.
Install / Use
claude mcp add xns-cloud -- npx -y github:xns-cloud/xns-ai-cookbooksIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of xns-ai-cookbooks
xns-ai-cookbooks scores 71/100 on our quality scale, 2643rd of 2,864 Automation skills we index.
Its MCP Server is 2.5 KB long, split into 5 sections with 2 code examples: a solid amount of guidance for an agent.
It has 3 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 2 months ago, so xns-ai-cookbooks is actively maintained.
- Our last check on 2026-09-06 found the source still online.
- It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 87/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-10-06. Automated pattern scan on 2026-10-06. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
xns-ai-cookbooks compared with similar skills
All 4 of these similar skills score higher than xns-ai-cookbooks; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| xns-ai-cookbooks (this skill)by xns-cloud | 71 | 3 | 54d ago | MCP Server |
| Agent-Reachby Panniantong | 100 | 105.5k | 3d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.1k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.7k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 90.2k | today | MCP Server |
Frequently asked questions
- How do I install xns-ai-cookbooks?
- Run
claude mcp add xns-cloud -- npx -y github:xns-cloud/xns-ai-cookbooks. The install tabs above show the steps for each supported agent. - Which AI agents does xns-ai-cookbooks work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is xns-ai-cookbooks safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 87/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is xns-ai-cookbooks still maintained?
- The repository was last updated about 2 months ago, so xns-ai-cookbooks is actively maintained.
Skill content
View source on GitHubXNS AI Cookbooks
Working recipes for AI pipelines on XNS — S3-compatible storage with no per-read charge.
Every time a RAG pipeline re-reads a corpus, a training job pulls a checkpoint, or an agent fetches a shared artifact, the storage side of that read is free — compute and model API costs are yours as usual. These recipes target the workflows where repeated reads dominate the storage bill.
Each recipe states its limitations explicitly. These are starter recipes — single-process, happy-path — and each one says exactly where that stops being enough.
Recipes
| Recipe | Pipeline | Status | |--------|----------|--------| | Multimodal RAG | Video speech + frames → transcripts + vision captions cached in XNS → query | Ready | | Agentic Document Parsing | PDFs/spreadsheets → local Docling parse → structured JSON, cached per document | Ready | | Fine-Tune Checkpointing | Model weights ↔ GPU clusters via S3 multipart | Ready | | Agent Workspace | CrewAI agents exchanging artifacts through a shared bucket | Ready |
Each recipe includes three things:
- Architecture blueprint — a text diagram showing where XNS sits in the pipeline and why reads being free matters at that point.
- Runnable script — Python, under 60 seconds on a laptop once prerequisites are in place.
- Config block — JSON to wire XNS into Claude Desktop, Cursor, or any MCP client.
Prerequisites
You need a running XNS Relayer (the S3 gateway). Two paths:
Docker Compose (if you have a Linux host with Docker):
git clone https://github.com/xns-cloud/relayer-quickstart
cd relayer-quickstart
docker compose up -d
AI-assisted setup (the MCP server walks you through it):
claude mcp add relayer -- npx @xns-cloud/relayer-mcp@latest
Then ask the agent to "set up XNS storage." It handles prerequisites, account
registration, install, and credential provisioning. Either path writes
~/.xns/credentials, which every recipe reads automatically.
Links
- XNS — self-hosted S3-compatible storage, zero egress
- relayer-quickstart — Docker Compose setup
- relayer-mcp — MCP server for AI code assistants
- langchain-xns — LangChain loaders and stores
- S3 compatibility — conformance test results
License
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
