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 benchling-integrationgit 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/benchling-integration)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/benchling-integration"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/benchling-integration/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/benchling-integration"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/benchling-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00050 | $0.01967 |
| Opus 5.5 | $0.00020 | $0.00787 |
| Sonnet 5.5 | $0.00010 | $0.00393 |
| Haiku 4.5 | $0.00005 | $0.00197 |
Grade A, and why
benchling-integration 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 14d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- benchling-integration — 100% identical, 264 lines differ
- benchling-integration — 86% identical, 247 lines differ
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchling Integration
When to use
Use this skill for Benchling registry entities, sequence imports, inventory, ELN entries, workflow tasks, apps, event-driven integrations, and warehouse analytics.
Reviewed 2026-09-30: examples target the released benchling-sdk 1.25.0 and its stable v2 API models. The current authentication guide recommends V3 for new development, while the V3 guide still describes endpoint-specific early access. Confirm your tenant's V3 availability and stability before migrating; these v2 SDK examples must not be mechanically rewritten to V3.
SDK imports, model serialization, and request construction were checked locally against 1.25.0. Tenant-dependent examples are illustrative: no authenticated requests, mutations, AWS deployment, or warehouse connection were run.
Workflow
- Identify the tenant, API version, identity, and permissions. Use OAuth app credentials for background integrations; use delegated authorization when acting as an individual user. See authentication.
- Read the relevant schema and resolve actual folder, registry, status, and dropdown IDs. Preserve sequence alphabet/topology and sample units. A valid Python model does not establish biological correctness or satisfaction of a tenant's required fields.
- Read a small filtered page before writing. Use typed SDK methods and check their actual parameter names; not all services share the same CRUD naming convention.
- Construct and serialize a representative payload. For imports, retain external IDs and returned Benchling IDs so a retry can reconcile a partial run without duplicates.
- Perform the requested operation and read back the result. Check terminal async status;
completed polling can still mean
FAILED.
Setup and a read-only query
uv pip install "benchling-sdk==1.25.0"
What ships with it
5 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.
- 14d ago Changed · -53 lines 115b5418f7c4
- 15d ago Changed 40482e57c559
- 1mo ago Changed · +17 lines f009a42b2108
- 1mo ago First seen · 230 lines · 50 tokens per session scan A e6e1915db825
benchling-integration is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (50,453 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 1,967 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 2026-08-30.
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