### Ginkgo Cloud Lab
Works with
name: "ginkgo-cloud-lab"
description: "Submit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs). Use when the user wants to ru..."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionginkgo-cloud-labExecute the skills CLI command in your project's root directory to begin installation:
Fetches ginkgo-cloud-lab from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ginkgo-cloud-lab. Access via /ginkgo-cloud-lab in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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| name | ginkgo-cloud-lab |
| description | Submit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs). Use when the user wants to run cell-free protein expression (validation or optimization), generate fluorescent pixel art, or interact with Ginkgo Cloud Lab services. Covers protocol selection, input preparation, pricing, and ordering workflows. |
| metadata | version: "1.0" |
Ginkgo Cloud Lab (https://cloud.ginkgo.bio) provides remote access to Ginkgo Bioworks' autonomous lab infrastructure. Protocols are executed on Reconfigurable Automation Carts (RACs) -- modular units with robotic arms, maglev sample transport, and industrial-grade software spanning 70+ instruments.
The platform also includes EstiMate, an AI agent that accepts human-language protocol descriptions and returns feasibility assessments and pricing for custom workflows beyond the listed protocols.
Rapid go/no-go expression screening using reconstituted E. coli CFPS. Submit a FASTA sequence (up to 1800 bp) and receive expression confirmation, baseline titer (mg/L), and initial purity with virtual gel images.
DoE-based optimization across up to 24 conditions per protein (lysates, temperatures, chaperones, disulfide enhancers, cofactors). Designed for difficult-to-express and membrane proteins.
Transform a pixel art image (48x48 to 96x96 px, PNG/SVG) into fluorescent bacterial artwork using up to 11 E. coli strains via acoustic dispensing. Delivered as high-res UV photographs.
For protocols not listed above, use the EstiMate chat to describe a custom protocol in plain language and receive compatibility assessment and pricing.
Access Ginkgo Cloud Lab at https://cloud.ginkgo.bio. Account creation or institutional access may be required. Contact Ginkgo at [email protected] for access questions.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
google-deepmind/science-skills
google-deepmind/science-skills
Useful defaults in ginkgo-cloud-lab — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
ginkgo-cloud-lab has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in ginkgo-cloud-lab — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added ginkgo-cloud-lab from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: ginkgo-cloud-lab is the kind of skill you can hand to a new teammate without a long onboarding doc.
ginkgo-cloud-lab is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ginkgo-cloud-lab is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ginkgo-cloud-lab reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for ginkgo-cloud-lab matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in ginkgo-cloud-lab — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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