Set up local development environment for OpenEvidence integration. Use when configuring development workflow, setting up testing environment, or creating a rapid iteration loop for clinical AI...
Replace the nonexistent local developer loop with a repeatable browser/app evaluation process. Keep inputs minimal, separate observed facts from assumptions, and leave consequential decisions with the named accountable owner.
Use Read, Glob, and Grep to inspect supplied policies, plans, and evidence. Use WebFetch only for current first-party OpenEvidence documentation. Use Write or Edit only when the user requests a named deliverable with an approved destination. Never expose credentials, PHI, recordings, or unrestricted environment output.
Use only the official OpenEvidence web/mobile sign-in or an institution-approved access path. Do not invent API keys, OAuth clients, SDK credentials, service accounts, or private endpoints. Never ask a user to reveal a password, session token, cookie, or recovery code.
Do not create or share accounts; change access, roles, agreements, consent, retention, or security settings; enter PHI; record a conversation; copy content into another system; contact a patient; make a diagnosis or treatment decision; submit billing; transmit a support packet; run a production pilot; or represent vendor capabilities without explicit approval from the accountable owner. A qualified professional remains responsible for clinical decisions.
Return scope, current first-party evidence and date, data classification, workflow or findings, citations reviewed, assumptions rejected, clinical and governance owners, approval state, unresolved risk, and the exact next action. Redact patient and credential data.
| Condition | Response |
|---|---|
| No sandbox | Use synthetic inputs in an authorized account; do not probe private infrastructure. |
| Output non-deterministic | Score invariant qualities rather than exact wording. |
| Reviewer disagreement | Preserve both rationales and escalate to the clinical owner. |
This compact example shows the minimum reviewable handoff; adapt fields to the approved workflow without adding sensitive data.
Input:
feature=Ask; cases=12 synthetic; reviewer=clinical lead; surface=web
Expected handoff:
runs=12; acceptable=9; revise=2; stop=1; next-change=prompt