Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 NousResearch/hermes-agent --skill lambda-labsgit clone --depth 1 https://github.com/NousResearch/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/nousresearch/hermes-agent/lambda-labs)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/lambda-labs"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/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/nousresearch/hermes-agent/lambda-labs"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/lambda-labs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 309 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high YARA Match · line 309 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium Data Exfiltration · line 232 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 245 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00013 | $0.03256 |
| Opus 5.5 | $0.00005 | $0.01302 |
| Sonnet 5.5 | $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 1mo 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 Copies of this mod
8 near-identical copies found in the catalogue:
- lambda-labs — 100% identical, 0 lines differ
- lambda-labs — 100% identical, 0 lines differ
- lambda-labs-gpu-cloud — 100% identical, 6 lines differ
- lambda-labs-gpu-cloud — 97% identical, 11 lines differ
- lambda-labs-gpu-cloud — 95% identical, 9 lines differ
- lambda-labs-gpu-cloud — 95% identical, 9 lines differ
- lambda-labs-gpu-cloud — 95% identical, 9 lines differ
- lambda-labs-gpu-cloud — 94% identical, 11 lines differ
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.
- 1mo ago First seen · 550 lines · 13 tokens per session scan C 7cfe3a21940c
lambda-labs is a skill published in the GitHub repository NousResearch/hermes-agent (254,951 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 3,256 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 2 findings (reaches for credential files, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
serving-llms-vllm
Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
mle-workflow
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude…
caveman-discover
Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.
claude-api-in-prototypes
Call Claude from your HTML artifacts via window.claude.complete.