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review-data-pr

Perform an intelligent review of geographic data PRs beyond schema validation. Use this skill whenever reviewing a data PR, performing code review on contributions, checking if data makes geographical sense, or assessing data quality. Trigger on: PR review requests for data changes, "review this PR", "does this look right", "is this data correct", quality assessment requests, geographic plausibility checks, suspicious pattern detection, or any pull request touching files in the contributions/ directory. This goes beyond schema validation to check real-world geographic accuracy.

Datos de origen

Repositorio
dr5hn/countries-states-cities-database
Última actividad en el origen
27 de marzo de 2026 a las 10:41
Idioma detectado de SKILL.md
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9864
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3030

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SKILL.md
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name
review-data-pr
description
Perform an intelligent review of geographic data PRs beyond schema validation. Use this skill whenever reviewing a data PR, performing code review on contributions, checking if data makes geographical sense, or assessing data quality. Trigger on: PR review requests for data changes, "review this PR", "does this look right", "is this data correct", quality assessment requests, geographic plausibility checks, suspicious pattern detection, or any pull request touching files in the contributions/ directory. This goes beyond schema validation to check real-world geographic accuracy.
## Review Process This skill goes beyond schema validation to check data quality and plausibility. ### Step 1: Read the PR Context - Read the PR description for intent and data source - Check which files are changed and what entity types are involved - Note the number of records being added/modified/deleted ### Step 2: Check Geographical Plausibility For each record, consider: - Does the city name make sense for this country/state? (e.g., a city named "Springfield" in India would be unusual) - Are the coordinates in the right general area? (a city in Maharashtra should be in western India) - Is the state assignment correct? (verify the city actually belongs to that state) - For states: does the type make sense? (e.g., "province" vs "state" vs "region") ### Step 3: Look for Suspicious Patterns Flag these patterns for manual review: - All records have identical or sequential coordinates (copy-paste error) - All records have the same latitude or longitude (likely placeholder data) - Records have unusually low coordinate precision (e.g., whole numbers like 34.0, -118.0) - Bulk additions with no clear geographic grouping - Names that appear machine-generated or contain unusual characters - Coordinates that resolve to ocean/water bodies rather than land ### Step 4: Verify Data Source - Is the data source documented in the PR description? - Is it a Tier 1 (government, ISO, UN) or Tier 2 (GeoNames, academic) source? - Is the source URL accessible? - Does the source actually contain the claimed data? ### Step 5: Check for Completeness For city additions: - Are nearby or related cities included? (not required, but useful context) - Is the timezone field included? (optional but valuable) - Is the wikiDataId included? (optional but aids cross-referencing) ### Step 6: Assess Overall Quality Rate the contribution on: - **Accuracy:** Do the values appear correct? - **Completeness:** Are optional but valuable fields included? - **Source quality:** Is the data from a reliable source? - **Impact:** How many users would benefit from this data? ## Review Output Structure your review as: ``` ### Data Quality Review **Plausibility:** [Assessment] **Source Quality:** [Assessment] **Completeness:** [Assessment] **Findings:** - [List specific observations] **Recommendation:** [Approve / Request Changes / Needs Maintainer Review] ``` ## Escalation Criteria Escalate to the maintainer (@dr5hn) when: - Contribution involves deletions of states or countries - Data source is questionable or unverifiable - Coordinates appear systematically incorrect - Contribution modifies more than 100 records - You are uncertain about the geographical accuracy ## Tone Guidelines - Be welcoming and constructive, especially for first-time contributors - Acknowledge the effort involved in data contributions - Provide specific, actionable feedback - Link to the contribution guidelines when relevant - Thank contributors for their work
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