OP

openevidence-local-dev-loop

Provides a local development workflow using mock servers to iterate on OpenEvidence integrations without API quotas.

Install

mkdir -p .claude/skills/openevidence-local-dev-loop && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6320" && unzip -o skill.zip -d .claude/skills/openevidence-local-dev-loop && rm skill.zip

Installs to .claude/skills/openevidence-local-dev-loop

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Local Dev Loop for OpenEvidence.
32 charsno explicit “when” trigger
Beginner

Key capabilities

  • →Simulate clinical evidence queries with mock data
  • →Toggle between mock and live API modes
  • →Validate query categorization using topics endpoint
  • →Run integration tests against real API endpoints

How it works

The development server uses a proxy middleware to route requests to the live API or a mock handler based on the MOCK_MODE environment variable.

Inputs & outputs

You give it
Clinical question string
You get back
Mock evidence response with citations

When to use openevidence-local-dev-loop

  • →Configuring development workflows
  • →Setting up testing environments
  • →Creating rapid iteration loops
  • →Simulating API responses

About this skill

OpenEvidence Synthetic Workflow Evaluation Loop

Overview

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.

Prerequisites

  • A clearly bounded workflow, accountable clinical owner, and organizational policy
  • Current first-party OpenEvidence documentation and applicable institution agreements
  • Synthetic or properly authorized minimum-necessary data

Tool Discipline

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.

Current Contract

  • OpenEvidence does not publish a local runtime, sandbox SDK, or public test API in the audited documentation.
  • Synthetic scenarios are the default evaluation input; real PHI requires the full approved data boundary.
  • Evaluation tests workflow fitness and evidence review, not medical-device validation.

Authentication

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.

Instructions

  1. Define feature, user role, expected workflow outcome, unacceptable failure, and clinical reviewer.
  2. Create synthetic scenarios spanning routine, ambiguous, conflicting-evidence, and failure-path cases.
  3. Read the current guide and record the model, feature, and surface used for each run.
  4. Execute manually through the supported product, preserving only de-identified prompts, citations, and observations.
  5. Have a qualified reviewer score traceability, applicability, uncertainty, and workflow burden.
  6. Iterate one variable at a time and publish a go, revise, or stop recommendation.

Approval Boundaries

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.

Output

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.

Error Handling

ConditionResponse
No sandboxUse synthetic inputs in an authorized account; do not probe private infrastructure.
Output non-deterministicScore invariant qualities rather than exact wording.
Reviewer disagreementPreserve both rationales and escalate to the clinical owner.

Examples

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

Resources

When not to use it

  • →Using real patient data in development environments
  • →Relying on mock data for production validation

Prerequisites

Node.jsOpenEvidence API key

Limitations

  • →Mock data must be de-identified
  • →Live API queries may have 2-5s latency

How it compares

This approach allows for rapid iteration without consuming live API quotas by providing realistic, de-identified clinical responses.

Compared to similar skills

openevidence-local-dev-loop side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
openevidence-local-dev-loop (this skill)12moCautionBeginner
nestjs-expert378moReviewAdvanced
test-nodebridge-handler18moReviewIntermediate
ideogram-local-dev-loop02moReviewIntermediate

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