AI Research Skills Library is a collection of reusable instructions that guide AI agents through research and machine-learning engineering tasks, from finding ideas and writing papers to training, evaluation, and deployment. It is for configuring agents such as Claude Code, Codex, and Gemini to perform research 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 skills add Orchestra-Research/AI-Research-SKILLs --skill lambda-labsgit clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLsWrote 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/orchestra-research/ai-research-skills/lambda-labs)<a href="https://agentmods.dev/skills/orchestra-research/ai-research-skills/lambda-labs"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/lambda-labs/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/orchestra-research/ai-research-skills/lambda-labs"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/lambda-labs.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 | $0.00047 | $0.03273 |
| Opus 5 | $0.00023 | $0.01636 |
| Sonnet 5 | $0.00009 | $0.00655 |
| Haiku 4.5 | $0.00005 | $0.00327 |
Grade C, and why
lambda-labs-gpu-cloud 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 9d 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.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
echo 'ssh-rsa AAAA...' >> ~/.ssh/authorized_keys Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
## CLI with curl This is a copy
91% identical to lambda-labs — 14 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 — 546 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lambda Labs GPU Cloud
Comprehensive guide to running ML workloads on Lambda Labs GPU cloud with on-demand instances and 1-Click Clusters.
When to use Lambda Labs
Use Lambda Labs when:
- Need dedicated GPU instances with full SSH access
- Running long training jobs (hours to days)
- Want simple pricing with no egress fees
- Need persistent storage across sessions
- Require high-performance multi-node clusters (16-512 GPUs)
- Want pre-installed ML stack (Lambda Stack with PyTorch, CUDA, NCCL)
Key features:
- GPU variety: B200, H100, GH200, A100, A10, A6000, V100
- Lambda Stack: Pre-installed PyTorch, TensorFlow, CUDA, cuDNN, NCCL
- Persistent filesystems: Keep data across instance restarts
- 1-Click Clusters: 16-512 GPU Slurm clusters with InfiniBand
- Simple pricing: Pay-per-minute, no egress fees
- Global regions: 12+ regions worldwide
Use alternatives instead:
- Modal: For serverless, auto-scaling workloads
- SkyPilot: For multi-cloud orchestration and cost optimization
- RunPod: For cheaper spot instances and serverless endpoints
- Vast.ai: For GPU marketplace with lowest prices
Quick start
Account setup
- Create account at https://lambda.ai
- Add payment method
- Generate API key from dashboard
- Add SSH key (required before launching instances)
Launch via console
- Go to https://cloud.lambda.ai/instances
- Click "Launch instance"
- Select GPU type and region
- Choose SSH key
- Optionally attach filesystem
- Launch and wait 3-15 minutes
Connect via SSH
# Get instance IP from console
ssh ubuntu@<INSTANCE-IP>
# Or with specific key
ssh -i ~/.ssh/lambda_key ubuntu@<INSTANCE-IP>
GPU instances
Available GPUs
| GPU | VRAM | Price/GPU/hr | Best For |
|---|---|---|---|
| B200 SXM6 | 180 GB | $4.99 | Largest models, fastest training |
| H100 SXM | 80 GB | $2.99-3.29 | Large model training |
| H100 PCIe | 80 GB | $2.49 | Cost-effective H100 |
| GH200 | 96 GB | $1.49 | Single-GPU large models |
| A100 80GB | 80 GB | $1.79 | Production training |
| A100 40GB | 40 GB | $1.29 | Standard training |
| A10 | 24 GB | $0.75 | Inference, fine-tuning |
| A6000 | 48 GB | $0.80 | Good VRAM/price ratio |
| V100 | 16 GB | $0.55 | Budget training |
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.
- 9d ago First seen · 546 lines · 47 tokens per session scan C 9d1dc3fcb83b
lambda-labs-gpu-cloud is a skill published in the GitHub repository Orchestra-Research/AI-Research-SKILLs (12,412 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 3,273 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 2 findings (reaches for credential files, makes network calls). It is 91% identical to lambda-labs, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.