Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/serverless-modalWrote 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/raja21068/autoresearch/serverless-modal)<a href="https://agentmods.dev/skills/raja21068/autoresearch/serverless-modal"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/serverless-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/raja21068/autoresearch/serverless-modal"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/serverless-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 | $0.00079 | $0.03357 |
| Opus 5 | $0.00039 | $0.01679 |
| Sonnet 5 | $0.00016 | $0.00671 |
| Haiku 4.5 | $0.00008 | $0.00336 |
Grade A, and why
serverless-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 8d 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.
subprocess.run( This is a copy
95% identical to serverless-modal — 23 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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Modal Cloud GPU — Training & Inference
Task: $ARGUMENTS
Overview
Modal is a serverless GPU cloud. Key advantages over SSH-based platforms (vast.ai, remote servers):
- Zero config: no SSH, no Docker, no port forwarding. Write Python →
modal run→ done. - Auto scale-to-zero: billing stops the instant your code finishes. No idle instances.
- Local-first: run
modal runfrom your laptop. Code, data, and results stay local; only the GPU function runs remotely. - Reproducible environments: dependencies declared in code via
modal.Image, not system-level packages.
Best for: Users without a local GPU who need to debug CUDA code, run small-scale tests, or iterate quickly on experiments. The $5 free tier (no card) is enough for code debugging; $30 (with card) covers most small-scale experiment runs.
Trade-off: Modal costs more per GPU-hour than vast.ai or Lightning for some GPU tiers, but eliminates setup time and idle billing, often making it cheaper for short/medium workloads. For long training runs (>4 hours), consider vast.ai for lower $/hr.
Authentication
pip install modal
modal setup # Opens browser login, writes token to ~/.modal.toml
# Verify:
modal run -q 'print("ok")'
- Sign up: https://modal.com (GitHub/Google login)
- Free (no card): $5/month — enough for quick tests
- Free (with card): $30/month — bind a payment method at https://modal.com/settings for the full free tier. Set a workspace spending limit to prevent accidental overcharge (Settings → Usage → Spending Limit)
- Academic: apply for $10k credits | Startups: apply for $25k credits
- Secrets:
modal secret create huggingface-secret HF_TOKEN=hf_xxxxx
Recommended setup: Bind a card to unlock $30/month, then immediately set a spending limit (e.g., $30) so you never exceed the free tier. Modal will pause your workloads when the limit is hit.
SECURITY WARNING: Always bind your card and set spending limits directly on https://modal.com/settings in your browser. NEVER enter payment information, card numbers, or billing details through Claude Code or any CLI tool. Only the official Modal website is safe for payment operations.
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.
- 8d ago First seen · 325 lines · 79 tokens per session scan A c1f98f7c3cf1
serverless-modal is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 79 tokens to every session and 3,357 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). It is 95% identical to serverless-modal, differing in 23 lines, and is treated as a copy.
Other skills, from other repositories
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paper-autoraters
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plotting-agent
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paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…