synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill modalgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/modal)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/modal"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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/synthetic-sciences/openscience/modal"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/modal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.00831 |
| Opus 5.5 | $0.00022 | $0.00332 |
| Sonnet 5.5 | $0.00011 | $0.00166 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
modal-serverless-gpu scanned grade A with 0 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Modal through compute_job
Modal is an OpenScience compute target, not an agent-controlled SDK. Prepare ordinary workspace files and call compute_job with target { kind: "modal" }. The JobBroker owns credentials, the reviewed dispatch card, sandbox lifecycle, logs, cancellation, recovery, and output delivery.
Contract
- Read
<compute-capability>for the configured/disabled/unconfigured state. Do not probe availability by launching work. - Never inspect Modal credentials or
~/.modal.toml; never install or invoke the Modal Python SDK or CLI. - Commands are ordinary shell commands inside
/workspace, such aspython analysis.py. - The
packageslist builds Python dependencies into the reviewed image before execution. Pin important versions. - With no
cwd, omitteduploadsstages safe ordinary files from session scratch. With acwd, it stages from that directory. Pass explicit globs to narrow inputs; passuploads: []only when the job needs no workspace files. - Declare every output needed after the run in
artifacts. Undeclared files are not delivered. - Use GPU
nonefor CPU work. Choose GPU count, CPU, memory, and timeout from the workload; do not invent price or duration guarantees. - Network
noneis the safe default. Use unrestricted network only when the command genuinely needs remote access and the approval card shows it. - Refer to stored secrets only by symbolic name. Never place secret values in commands, files, prompts, logs, or plans.
- The
compute_jobcard is the authorization boundary. Do not request a second prose approval or treat chat text as dispatch authorization.
Workflow
- Prepare a reproducible script and small inputs in session scratch.
- Call
compute_jobwithaction: "plan"when parameters still need inspection, otherwiseaction: "start". - Include a clear name, purpose, command, target, packages, resources, upload selection, and artifact globs.
- After approval, use the returned job ID for status, logs, artifacts, cancellation, delivery retry, or release. Do not create a duplicate job merely because a long job is still running.
- Report only states and outputs returned by
compute_job. Preserve failures and uncertainty.
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 Changed · +8 lines e53bf74b6416
- 1mo ago First seen · 54 lines · 54 tokens per session scan A 5b61a97ac609
modal-serverless-gpu is a skill published in the GitHub repository synthetic-sciences/openscience (4,082 stars, last pushed yesterday), licensed Apache-2.0. It adds 54 tokens to every session and 831 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
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.
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.
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.
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.
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.
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.