Finds specimens that match what you want to do. Tells you whether they'll work for your specific assay at your specific provider. Shows you exactly which claims are verified and which need one phone call. Every number traceable to a source.
Ten seconds later:
Then a table showing every sourcing path with per-link evidence states. Green = grounded. Open = unknown, here's who to contact. No fake numbers.
Brokers say "WGBS costs $150-300/sample." We say "IMR charges $875/sample for 22Gb depth [verified: imr.bio/pricing.html]. Cornell charges $250/sample standard depth [verified: epicore.med.cornell.edu]." The buyer clicks the link and sees the same number.
Ask about a domain with zero wiki data. The system doesn't say "nothing found." It searches PubMed, ClinicalTrials.gov, the web. Visits provider pages. Extracts stated requirements. Delivers a sourcing chain from what it found. Then compiles into the wiki so the next question gets an instant answer.
Not 25 pages of prose. A chain:
The buyer sees which links are solid and which need one phone call.
"Will these specimens work for my assay?" depends on who runs it. Psomagen needs >200ng and DIN>7.0. Cornell accepts standard input. EM-seq works with 10ng. The system pairs your specimen source with a specific provider and evaluates fitness against that provider's stated requirements.
When the system can't ground a claim:
[open_question — searched psomagen.com, no DIN threshold found for stool matrix].
A gap with provenance is infinitely more useful than a confident-sounding number from training data.
First query in a new domain: search, score thin leads, deliver immediately, compile in background. Second query: wiki has entities, discover finds them instantly, full scoring with provenance depth. The product gets faster with every query. But the first buyer never waits.
Real query: 500 AD CSF samples for Olink targeted proteomics.
The cost picture. Academic core rate cards show Olink Target 96 at $113/sample, not the $40-60 that appears in training data. For 500 samples: $61K, not $25K. SomaScan 7K: $720/sample. For 500 samples: $365K, not $75K.
The blocking gate. ADNI specimens are "not for use in technology development." A commercial buyer who doesn't know this wastes 6-10 weeks on the RARC application before hitting a policy wall. The chain flags it on link 1.
The provider landscape. 9 academic cores found, 4 with published CSF-validated pricing. IU Indianapolis at $113. UCSD at $165. UH Houston at $100. The buyer picks based on real numbers, not a single vendor quote.
The feasibility evidence. 4 published studies ran Olink PEA on AD CSF at scale (797 samples in Nature Aging 2023, 463 DIAN CSF in Cell 2024). Prior art is the strongest signal that the path works.
The aha is not more features. It's fewer lies.
No composite score. Three axes, buyer picks the weight.
No training-data fills. [open_question] over a plausible guess.
No compile gate. Search results are immediately usable.
No prose where a table works. The chain table is the recommendation.
No wiki-link to a nonexistent entity. Source URL or nothing.
Every piece of information the buyer sees is either grounded — they can verify it — or honestly flagged — they know it's unverified. The system never smooth-talks. It shows its work.
viCRO is an open-source CLI that runs on Claude Code. It builds a knowledge graph of biological specimens — who has what, where, at what quality, under what consent, at what cost.
Four phases in a loop:
The wiki starts empty and grows with every question. 20 skills. 4 agents. MIT licensed. Your data stays on your machine.
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