Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting 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 K-Dense-AI/scientific-agent-skills --skill modalgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/modal)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/modal"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/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/k-dense-ai/scientific-agent-skills/modal"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/modal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk fail
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Rogue Agent · line 43 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00065 | $0.04052 |
| Opus 5 | $0.00032 | $0.02026 |
| Sonnet 5 | $0.00013 | $0.00810 |
| Haiku 4.5 | $0.00006 | $0.00405 |
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 7d 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 / custom servers:** Some patterns here (multi-GPU training launchers, `@modal.web_server` apps) call `subprocess.run`/`subprocess.Popen` or shell commands during builds. Keep argument lists fixed and hardc How it starts
The opening of the file, as written. The whole thing — 486 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Modal
Overview
Modal is a cloud platform for running Python code serverlessly, with a focus on AI/ML workloads. Key capabilities:
- GPU compute on demand (T4, L4, A10, L40S, A100, H100, H200, B200)
- Serverless functions with autoscaling from zero to thousands of containers
- Custom container images built entirely in Python code
- Persistent storage via Volumes for model weights and datasets
- Web endpoints for serving models and APIs
- Scheduled jobs via cron or fixed intervals
- Sub-second cold starts for low-latency inference
Everything in Modal is defined as code — no YAML, no Dockerfiles required (though both are supported).
When to Use This Skill
Use this skill when:
- Deploy or serve AI/ML models in the cloud
- Run GPU-accelerated computations (training, inference, fine-tuning)
- Create serverless web APIs or endpoints
- Scale batch processing jobs in parallel
- Schedule recurring tasks (data pipelines, retraining, scraping)
- Need persistent cloud storage for model weights or datasets
- Want to run code in custom container environments
- Build job queues or async task processing systems
Installation and Authentication
Install
uv pip install modal
The Modal Python SDK supports Python 3.10–3.14. This skill targets the stable modal>=1.0 API (current release: 1.4.x).
Authenticate
Prefer existing credentials before creating new ones. Only the two Modal-specific
variables below are relevant — do not read, load, or expose any other environment
variables or .env file contents:
- Check whether
MODAL_TOKEN_IDandMODAL_TOKEN_SECRETare already set in the current environment. - If not, look up only those two keys in a local
.envfile (ignore all other entries) and load them if appropriate for the workflow. - Only fall back to interactive
modal setupor generating fresh tokens if neither source already provides those two values.
modal setup
This opens a browser for authentication. For CI/CD or headless environments, use environment variables:
What ships with it
12 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.
- references/api_reference.md 6.7 KB
- references/examples.md 7.3 KB
- references/functions.md 5.7 KB
- references/getting-started.md 4.0 KB
- references/gpu.md 4.9 KB
- references/images.md 6.5 KB
- references/resources.md 2.5 KB
- references/scaling.md 4.4 KB
- references/scheduled-jobs.md 3.7 KB
- references/secrets.md 2.8 KB
- references/volumes.md 5.7 KB
- references/web-endpoints.md 5.7 KB
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.
- 7d ago First seen · 486 lines · 65 tokens per session scan A 81fd4eaea47e
modal is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 4d ago), licensed MIT. It adds 65 tokens to every session and 4,052 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
uptime-monitor
Monitor website uptime - check availability, response times, and status.
Cloud Security & Container Hardening
AWS/Azure/GCP security auditing, container and Kubernetes hardening, Infrastructure as Code scanning, and cloud compliance assessment.
AI & LLM Security
LLM and AI application security testing — prompt injection, jailbreak resistance, OWASP LLM Top 10 (2025), RAG and agent/tool-use security, model supply chain, and AI red teaming for authorized assessments.
go-tooling-security
Use when setting up Go modules/workspaces, configuring golangci-lint v2, running govulncheck, or building a Go CI quality gate. Not for app logic or non-Go audits.
laravel-ai-sdk
Use when integrating AI agents, tool calling, embeddings, structured output, or streaming in Laravel 13 via the laravel/ai package.