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 graniet/kheish --skill modalgit clone --depth 1 https://github.com/graniet/kheishWrote 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/graniet/kheish/modal)<a href="https://agentmods.dev/skills/graniet/kheish/modal"><img src="https://agentmods.dev/badge/skills/graniet/kheish/modal/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/graniet/kheish/modal"><img src="https://agentmods.dev/badge/skills/graniet/kheish/modal.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 | $0.00036 | $0.02291 |
| Opus 5.5 | $0.00014 | $0.00916 |
| Sonnet 5.5 | $0.00007 | $0.00458 |
| Haiku 4.5 | $0.00004 | $0.00229 |
Grade A, and why
modal scanned grade A with 1 finding 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 2mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
return subprocess.run(["nvidia-smi"], capture_output=True, text=True).stdout This is a copy
78% identical to modal — 59 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 — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal=>bashweb_extract=>web_fetch, plusweb_searchwhen discovery is neededsearch_files=>grep_searchandglob_searchbrowser_*tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
Modal Serverless GPU
Comprehensive guide to running ML workloads on Modal's serverless GPU cloud platform.
When to use Modal
Use Modal when:
- Running GPU-intensive ML workloads without managing infrastructure
- Deploying ML models as auto-scaling APIs
- Running batch processing jobs (training, inference, data processing)
- Need pay-per-second GPU pricing without idle costs
- Prototyping ML applications quickly
- Running scheduled jobs (cron-like workloads)
Key features:
- Serverless GPUs: T4, L4, A10G, L40S, A100, H100, H200, B200 on-demand
- Python-native: Define infrastructure in Python code, no YAML
- Auto-scaling: Scale to zero, scale to 100+ GPUs instantly
- Sub-second cold starts: Rust-based infrastructure for fast container launches
- Container caching: Image layers cached for rapid iteration
- Web endpoints: Deploy functions as REST APIs with zero-downtime updates
Use alternatives instead:
- RunPod: For longer-running pods with persistent state
- Lambda Labs: For reserved GPU instances
- SkyPilot: For multi-cloud orchestration and cost optimization
- Kubernetes: For complex multi-service architectures
Quick start
Installation
pip install modal
modal setup # Opens browser for authentication
Hello World with GPU
import modal
app = modal.App("hello-gpu")
@app.function(gpu="T4")
def gpu_info():
import subprocess
return subprocess.run(["nvidia-smi"], capture_output=True, text=True).stdout
@app.local_entrypoint()
def main():
print(gpu_info.remote())
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.
- 2mo ago First seen · 365 lines · 36 tokens per session scan A dc4be9c3b286
modal is a skill published in the GitHub repository graniet/kheish (217 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 36 tokens to every session and 2,291 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). It is 78% identical to modal, differing in 59 lines, and is treated as a copy.
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.
torchtitan
Pretrain LLMs at scale with PyTorch 4D parallelism.
modal
Serverless GPU cloud for ML jobs and model APIs.
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
distributed-training-ray-train
Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.
infrastructure-skypilot
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.