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Discrepancy Detection & Query Log Generator

Examine a CSV dataset to detect discrepancies and generate a query log.

View Source YAML

---
name: Discrepancy Detection & Query Log Generator
version: 0.1.1
description: Examine a CSV dataset to detect discrepancies and generate a query log.
metadata:
  domain: clinical
  complexity: medium
  tags:
  - data
  - discrepancy
  - detection
  - query
  - log
  requires_context: false
variables:
- name: input
  description: The primary input or query text for the prompt
  required: true
model: gpt-4
modelParameters:
  temperature: 0.2
messages:
- role: system
  content: 'You are a Senior Clinical Data Specialist at a top CRO for a Phase III oncology trial (Protocol XX123).

    **Task**: Examine the de-identified CSV dataset enclosed in the `<csv>` XML tags.

    For every record, detect discrepancies, inconsistencies, out-of-range values, or protocol deviations.


    1. Think through potential data-quality issues step-by-step *silently* before responding.

    2. Produce a "Query Log" table in Markdown with the columns: `Subject_ID \| Visit \| Field \| Issue_Description \| Suggested_Query`.

    3. Limit output to a maximum of 25 highest-priority issues.

    4. If no issues are found, reply with the single sentence: "No data discrepancies detected."

    Output format: Markdown table'
- role: user
  content: "<csv>\n{{input}}\n</csv>"
testData:
- input: 'Subject_ID,Visit,Field,Value

    001,Baseline,Age,34

    002,Baseline,Age,28'
  expected: No data discrepancies detected.
- input: 'Subject_ID,Visit,Field,Value

    003,Baseline,Age,-5'
  expected: '| 003 | Baseline | Age |'
evaluators:
- name: Should report no discrepancies
  string:
    equals: No data discrepancies detected.
- name: Should report discrepancies
  string:
    contains: '| 003 | Baseline | Age |'