ginkgo-cloud-lab▌
K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026
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### Ginkgo Cloud Lab
- ›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..."
| 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
Overview
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.
Available Protocols
1. Cell Free Protein Expression Validation
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.
- Price: $39/sample | Turnaround: 5-10 days | Status: Certified
- Details: See references/cell-free-protein-expression-validation.md
2. Cell Free Protein Expression Optimization
DoE-based optimization across up to 24 conditions per protein (lysates, temperatures, chaperones, disulfide enhancers, cofactors). Designed for difficult-to-express and membrane proteins.
- Price: $199/sample | Turnaround: 6-11 days | Status: Certified
- Details: See references/cell-free-protein-expression-optimization.md
3. Fluorescent Pixel Art Generation
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.
- Price: $25/plate | Turnaround: 5-7 days | Status: Beta
- Details: See references/fluorescent-pixel-art-generation.md
General Ordering Workflow
- Select a protocol at https://cloud.ginkgo.bio/protocols
- Configure parameters (number of samples/proteins, replicates, plates)
- Upload input files (FASTA for protein protocols, PNG/SVG for pixel art)
- Add any special requirements in the Additional Details field
- Submit and receive a feasibility report and price quote
For protocols not listed above, use the EstiMate chat to describe a custom protocol in plain language and receive compatibility assessment and pricing.
Authentication
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.
Key Infrastructure
- RACs (Reconfigurable Automation Carts): Modular robotic units with high-precision arms and maglev transport
- Catalyst Software: Protocol orchestration, scheduling, parameterization, and real-time monitoring
- 70+ integrated instruments: Sample prep, liquid handling, analytical readouts, storage, incubation
- Nebula: Ginkgo's autonomous lab facility in Boston, MA
How to use ginkgo-cloud-lab on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ginkgo-cloud-lab
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ginkgo-cloud-lab from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ginkgo-cloud-lab. Access the skill through slash commands (e.g., /ginkgo-cloud-lab) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★43 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
Useful defaults in ginkgo-cloud-lab — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aditi Bansal· Dec 24, 2024
ginkgo-cloud-lab has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Dev Diallo· Dec 20, 2024
Useful defaults in ginkgo-cloud-lab — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Aanya Torres· Dec 16, 2024
We added ginkgo-cloud-lab from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aditi Kapoor· Dec 8, 2024
Keeps context tight: ginkgo-cloud-lab is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Rahul Santra· Nov 19, 2024
ginkgo-cloud-lab is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Alexander Thomas· Nov 11, 2024
ginkgo-cloud-lab is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★William Khan· Nov 7, 2024
ginkgo-cloud-lab reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yusuf Mehta· Oct 26, 2024
Registry listing for ginkgo-cloud-lab matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Layla White· Oct 18, 2024
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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