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/aivrar/portable-hermes-agent/lambda-labsnpx skills add aivrar/portable-hermes-agent --skill lambda-labsgit clone --depth 1 https://github.com/aivrar/portable-hermes-agentWrote 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/aivrar/portable-hermes-agent/lambda-labs)<a href="https://agentmods.dev/skills/aivrar/portable-hermes-agent/lambda-labs"><img src="https://agentmods.dev/badge/skills/aivrar/portable-hermes-agent/lambda-labs.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 | $0.00013 | $0.03256 |
| Opus 5 | $0.00006 | $0.01628 |
| Sonnet 5 | $0.00003 | $0.00651 |
| Haiku 4.5 | $0.00001 | $0.00326 |
Grade C, and why
lambda-labs 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 yesterday.
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
100% identical to lambda-labs — 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 — 550 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lambda Labs GPU Cloud
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.
- yesterday First seen · 550 lines · 13 tokens per session scan C 7cfe3a21940c
lambda-labs is a skill published in the GitHub repository aivrar/portable-hermes-agent (216 stars, last pushed yesterday), licensed MIT. It adds 13 tokens to every session and 3,256 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 2 findings (reaches for credential files, makes network calls). It is 100% identical to lambda-labs, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
modal-serverless-gpu
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cloudflare-temporary-deploy
Deploy a Worker live, no account, via wrangler --temporary.
pinggy-tunnel
Zero-install localhost tunnels over SSH via Pinggy.
stripe-projects
Provision SaaS services + sync creds via Stripe Projects.
architecture-diagram
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
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.