agentleFS
spiralcrew-ouspiralcrew-ou/profilespider-agent-skills/company-data-normalization/SKILL.md

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.

The file

Files
SKILL.md

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.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.