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Company data normalization

Skill spiralcrew-ou/profilespider-agent-skills/company-data-normalization

24 free SKILL.md skills for Claude Code & compatible agents: qualify leads, research companies, clean lists.

Install
npx -y skills add spiralcrew-ou/profilespider-agent-skills --skill company-data-normalization

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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.

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SKILL.md

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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

  1. Produce canonical values; do not invent unknown fields.
  2. Map industries only to the supplied taxonomy when given.
  3. Report a confidence level per record.
  4. List unresolved fields explicitly.
  5. Preserve originals alongside normalized values.

Process

  1. Parse each company record.
  2. Normalize name and domain.
  3. Map industry and standardize location.
  4. Assign confidence.
  5. 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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