ddhq-miss-audit

Auditing unmatched election campaigns against DDHQ results requires identifying gp_api product campaigns with no matching election data within a specific date window. This process classifies unmatched campaigns into definitive reasons like data quality issues or missing races.

Some product campaigns lack matching DDHQ election results due to data quality issues, missing races, or false negatives. An audit classifies these unmatched campaigns using deterministic SQL pre-pass and web-verification to pinpoint the exact discrepancy.

Yes, you need a Databricks and Python environment to execute the campaign matching audit. The Skill utilizes these dependencies to run its deterministic SQL pre-pass and web-verification subagent fan-out for accurate election analysis.

The best way to classify unmatched campaign data is using a deterministic SQL pre-pass followed by web-verification. This approach accurately categorizes unmatched gp_api campaigns into specific reasons like data quality issues, missing races, or false negatives.

Quantifying missing DDHQ election matches generates an external aggregate summary CSV, an internal detail CSV, and a confirmed-winners list. These outputs provide both high-level summaries and granular details of the unmatched campaign audit.

Yes, you can run a campaign audit for a specific election-date window by providing start and end dates, such as 2026-01-01 to 2026-05-31. The audit identifies unmatched gp_api campaigns within that defined timeframe.