company-data-normalization
Skill9 starsChanged 5 months agoMITView on GitHub
Normalizes company names, industries, domains, locations, and descriptions. Use when the user provides company records and wants consistent canonical fields with confidence and unresolved items flagged.
In this repository23 other files it gives its agents.
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The file
Files
Company Data Normalization
Purpose
Normalize company names, domains, industries, and locations into consistent values.
When to use this skill
- Standardizing company fields before a join or merge
- Cleaning a multi-source company list
- Preparing data for segmentation or analysis
- Resolving inconsistent industry labels
When not to use this skill
- You need to detect duplicates (use Duplicate Record Review)
- The records contain no company identifiers
- You need verified, authoritative registry data
Required inputs
- Company records
Optional inputs
- A canonical industry taxonomy
- A location format standard
- Known aliases to map
Rules
- Produce canonical values; do not invent unknown fields.
- Map industries only to the supplied taxonomy when given.
- Report a confidence level per record.
- List unresolved fields explicitly.
- Preserve originals alongside normalized values.
Process
- Parse each company record.
- Normalize name and domain.
- Map industry and standardize location.
- Assign confidence.
- List unresolved fields.
Output format
Return one record per company with the following fields:
- normalized_company_name
- normalized_domain
- standardized_industry
- standardized_location
- normalization_confidence
- unresolved_fields
Validation
- Confirm domains are root domains without protocol.
- Confirm industries match the taxonomy when provided.
- Confirm low-confidence rows are flagged.
Limitations
- Normalization is heuristic without an authoritative registry.
- Ambiguous names may need manual disambiguation.
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