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 yanjumlinnb-boop/scientific-agent-skills --skill ginkgo-cloud-labgit clone --depth 1 https://github.com/yanjumlinnb-boop/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/yanjumlinnb-boop/scientific-agent-skills/ginkgo-cloud-lab)<a href="https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/ginkgo-cloud-lab"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/ginkgo-cloud-lab/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/yanjumlinnb-boop/scientific-agent-skills/ginkgo-cloud-lab"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/ginkgo-cloud-lab.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00087 | $0.00765 |
| Opus 5 | $0.00044 | $0.00382 |
| Sonnet 5 | $0.00017 | $0.00153 |
| Haiku 4.5 | $0.00009 | $0.00076 |
Grade A, and why
ginkgo-cloud-lab 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 12d 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.
This is a copy
98% identical to ginkgo-cloud-lab — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
3 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.
- 12d ago First seen · 58 lines · 87 tokens per session scan A 86976bf5cc75
ginkgo-cloud-lab is a skill published in the GitHub repository yanjumlinnb-boop/scientific-agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 87 tokens to every session and 765 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to ginkgo-cloud-lab, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…