NanoResearch is an autonomous AI research system that turns research ideas into executable experiments and LaTeX papers supported by results from real training runs. It is for researchers validating prototypes, running GPU experiments, generating benchmarks, analyzing logs, and preparing paper drafts. The catalogue add-ons support its research pipeline and agent 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/openraiser/nanoresearch/skypilotnpx skills add OpenRaiser/NanoResearch --skill skypilotgit clone --depth 1 https://github.com/OpenRaiser/NanoResearchWrote 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/openraiser/nanoresearch/skypilot)<a href="https://agentmods.dev/skills/openraiser/nanoresearch/skypilot"><img src="https://agentmods.dev/badge/skills/openraiser/nanoresearch/skypilot.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 5.1 | $0.00051 | $0.02725 |
| Opus 5 | $0.00026 | $0.01362 |
| Sonnet 5 | $0.00010 | $0.00545 |
| Haiku 4.5 | $0.00005 | $0.00272 |
Grade A, and why
skypilot-multi-cloud-orchestration 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 6d 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.
This is a copy
100% identical to skypilot-multi-cloud-orchestration — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SkyPilot Multi-Cloud Orchestration
Comprehensive guide to running ML workloads across clouds with automatic cost optimization using SkyPilot.
When to use SkyPilot
Use SkyPilot when:
- Running ML workloads across multiple clouds (AWS, GCP, Azure, etc.)
- Need cost optimization with automatic cloud/region selection
- Running long jobs on spot instances with auto-recovery
- Managing distributed multi-node training
- Want unified interface for 20+ cloud providers
- Need to avoid vendor lock-in
Key features:
- Multi-cloud: AWS, GCP, Azure, Kubernetes, Lambda, RunPod, 20+ providers
- Cost optimization: Automatic cheapest cloud/region selection
- Spot instances: 3-6x cost savings with automatic recovery
- Distributed training: Multi-node jobs with gang scheduling
- Managed jobs: Auto-recovery, checkpointing, fault tolerance
- Sky Serve: Model serving with autoscaling
Use alternatives instead:
- Modal: For simpler serverless GPU with Python-native API
- RunPod: For single-cloud persistent pods
- Kubernetes: For existing K8s infrastructure
- Ray: For pure Ray-based orchestration
Quick start
Installation
pip install "skypilot[aws,gcp,azure,kubernetes]"
# Verify cloud credentials
sky check
Hello World
Create hello.yaml:
resources:
accelerators: T4:1
run: |
nvidia-smi
echo "Hello from SkyPilot!"
Launch:
sky launch -c hello hello.yaml
# SSH to cluster
ssh hello
# Terminate
sky down hello
Core concepts
Task YAML structure
# Task name (optional)
name: my-task
# Resource requirements
resources:
cloud: aws # Optional: auto-select if omitted
region: us-west-2 # Optional: auto-select if omitted
accelerators: A100:4 # GPU type and count
cpus: 8+ # Minimum CPUs
memory: 32+ # Minimum memory (GB)
use_spot: true # Use spot instances
disk_size: 256 # Disk size (GB)
# Number of nodes for distributed training
num_nodes: 2
# Working directory (synced to ~/sky_workdir)
workdir: .
# Setup commands (run once)
setup: |
pip install -r requirements.txt
# Run commands
run: |
python train.py
What ships with it
2 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.
- 6d ago First seen · 510 lines · 51 tokens per session scan A de1be4556d8a
skypilot-multi-cloud-orchestration is a skill published in the GitHub repository OpenRaiser/NanoResearch (1,364 stars, last pushed 12d ago), licensed MIT. It adds 51 tokens to every session and 2,725 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to skypilot-multi-cloud-orchestration, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.