# Team Jack - **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences) - **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT) - **Where:** Online - **Team:** [JACK NG](https://cerebralvalley.ai/u/jacksen) - **GitHub:** https://github.com/jacksen-ng/living-protocol-copilot - **Demo video:** https://youtu.be/bYIiml9bOeo - **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery - **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/49 Living Protocol Copilot A grounded, safety-first copilot for wet-lab protocols What I built Living Protocol Copilot is a web application that transforms static laboratory SOPs into structured, risk-aware “living protocols.” It does two main things. First, it analyzes a protocol step by step, highlights high-risk procedures, and identifies every important detail that the SOP leaves unspecified. Rather than guessing or completing missing information, the system explicitly marks those gaps for the user. Second, it provides evidence-grounded troubleshooting for failed experiments. For a symptom such as “bands are weak and smeared,” the system returns a ranked list of possible causes. Each cause is linked to both a specific step in the user’s own SOP and a published source from a curated whitelist of 42 trusted references, including Thermo Fisher Scientific, Bio-Rad, Abcam, and New England Biolabs. Users can paste a protocol, upload a PDF, or photograph a printed protocol using their phone. The application also includes a grounded conversational agent for asking follow-up questions about the protocol. The system is built with Next.js and FastAPI. All Claude calls are handled server-side, using Sonnet 5 as the primary model, Opus 4.8 as a fallback, and Haiku for scope gating. Responses are grounded through the Claude Citations API. What I found The hardest part was not getting Claude to answer. It was getting Claude to refuse when the evidence was insufficient. A general-purpose assistant may confidently invent a missing reagent concentration or suggest a plausible-sounding experimental cause. In a wet-lab environment, however, a fabricated number can become a safety risk and undermine experimental reproducibility. For that reason, the entire system is built around four red lines: 1. Never invent biological or procedural information. 2. Never declare a procedure “safe.” 3. Remain grounded in evidence or remain silent. 4. Express uncertainty clearly, treating “insufficient information” as a valid first-class answer. In a representative live run, the protocol parser identified 15 unspecified details and filled in none of them. The troubleshooting system generated 10 candidate causes, all supported by citations. Eight were linked directly to a specific SOP step, and every external source was verified against the curated whitelist. Why it matters Failed experiments are expensive, time-consuming, and often difficult to diagnose. The explanation may be buried in scattered vendor manuals, troubleshooting PDFs, or the undocumented knowledge of an experienced researcher. There is strong evidence that this problem is real: manufacturers routinely publish symptom-indexed troubleshooting tables. Eight of the 42 sources in this project are dedicated troubleshooting guides. These documents exist because experimental procedures do not always work as expected. Living Protocol Copilot explores a more responsible way to apply large language models in high-stakes scientific workflows: cite real evidence, expose missing information, communicate uncertainty, and openly admit when the answer is unknown. This principle extends beyond wet-lab research. In any high-stakes domain, a confident but incorrect answer may be far more costly than saying, “I don’t know.” --- Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/49. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt