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 Lord1Egypt/scientific-agent-toolkit --skill ginkgo-cloud-labgit clone --depth 1 https://github.com/Lord1Egypt/scientific-agent-toolkitWrote 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/lord1egypt/scientific-agent-toolkit/ginkgo-cloud-lab)<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/ginkgo-cloud-lab"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/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/lord1egypt/scientific-agent-toolkit/ginkgo-cloud-lab"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/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 | $0.00755 |
| Opus 5 | $0.00044 | $0.00378 |
| Sonnet 5 | $0.00017 | $0.00151 |
| 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 10d 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
100% identical to ginkgo-cloud-lab — 0 lines 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 — 57 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.
- 10d ago First seen · 57 lines · 87 tokens per session scan A cb52652c0d44
ginkgo-cloud-lab is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (3 stars, last pushed 3mo ago), licensed MIT. It adds 87 tokens to every session and 755 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ginkgo-cloud-lab, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
alterlab-pyhealth
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…
alterlab-deep-research
Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology…
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…
alterlab-cobrapy
Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and…
alterlab-deeptools
Process and visualize deep-sequencing coverage with the deepTools CLI — convert BAM to bigWig (bamCoverage), build log2 ratio tracks (bamCompare), run QC (multiBamSummary correlation, PCA, plotFingerprint), apply the ATAC-seq Tn5 shift (alignmentSieve --ATACshift), and make TSS/peak heatmaps and profiles…