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 pass
- 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 | $0.04387 |
| Opus 5.5 | $0.00026 | $0.01755 |
| Sonnet 5.5 | $0.00013 | $0.00877 |
| Haiku 4.5 | $0.00006 | $0.00439 |
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 25d 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 Copies of this mod
7 near-identical copies found in the catalogue:
How it starts
The opening of the file, as written. The whole thing — 497 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 (including H100, H200, B200 and B300)
- 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
- Container reuse and warm pools to reduce cold starts (model loading still takes time)
Modal Apps and Images are defined in Python; existing Dockerfiles are also 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==1.6.0"
Reviewed against SDK 1.6.0 and current release notes. Local SDK construction and selected handlers were tested; remote builds, deployments, GPU inference and cloud limits were not executed. Snippets using model/data placeholders or third-party workloads are illustrative and require their stated dependencies.
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:
- Reuse an existing Modal profile, or check whether both
MODAL_TOKEN_IDandMODAL_TOKEN_SECRETare set; report presence only. - 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 setupif no usable profile or token pair exists. The SDK supports profiles in.modal.toml; environment tokens are optional when a profile authenticates.
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 8.5 KB
- references/examples.md 10 KB
- references/functions.md 8.3 KB
- references/getting-started.md 4.7 KB
- references/gpu.md 6.5 KB
- references/images.md 7.3 KB
- references/resources.md 3.2 KB
- references/scaling.md 6.5 KB
- references/scheduled-jobs.md 4.3 KB
- references/secrets.md 3.9 KB
- references/volumes.md 6.8 KB
- references/web-endpoints.md 8.2 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.
- 25d ago Changed · +6 lines 5da5d0a39db4
- 26d ago Changed · +5 lines f802994b58fe
- 1mo 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 (54,777 stars, last pushed yesterday), licensed MIT. It adds 65 tokens to every session and 4,387 once invoked, about $0.0003 per session on Opus 5.5. 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
Use when building or operating a machine learning pipeline. Covers feature engineering, training reproducibility, train/serve skew, deployment, monitoring for drift, and retraining. Trigger on "model worked offline but fails in production", "training runs aren't reproducible", "detect data drift", "set up retraining".…
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.
laravel-vector-search
Use when implementing semantic/vector search in Laravel 13 with PostgreSQL + pgvector.
prompt-library
Use when looking for a ready-made agent or task template (code review, support, data analysis, translation, extraction) instead of writing one from scratch.
prompt-creation
Use when writing a new system prompt from scratch, structuring it with Anthropic's 9-element format, or choosing emphasis levels and few-shot examples.