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lookalike-customer-finder

Business
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Input your best customers and find 100+ companies that match the profile. Uses firmographic data, tech stack, growth signals, and similarity scoring to identify ideal prospects. Use when building target account lists or expanding to new markets.

QUICK START

How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/OneWave-AI/claude-skills/blob/HEAD/lookalike-customer-finder/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/lookalike-customer-finder/. Do not write files or run scripts until I approve.

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

Lookalike Customer Finder

Analyze a company's best customers and find similar companies that match the same profile, producing a high-quality, ranked target account list.

Contents

  • references/scoring-model.md - Profile dimensions, weighted scoring model, and score bands.
  • references/output-template.md - Full Markdown report structure (ICP, ranked lookalikes, market insights, targeting strategy, action plan).
  • references/data-sources.md - Recommended enrichment tools and data points to gather.
  • references/examples.md - Best practices, trigger phrases, and an example request.

Workflow

  1. Collect the best customers provided. If none are given, ask for the top 5-10 accounts.
  2. Analyze common characteristics across them. See references/scoring-model.md for the five profile dimensions.
  3. Build the Ideal Customer Profile (ICP) from those shared traits.
  4. Search the market for companies matching the ICP. Pull firmographics, tech stack, growth signals, and contacts from the tools in references/data-sources.md.
  5. Score each candidate 0-100 using the weighted scoring model in references/scoring-model.md.
  6. Rank and tier the companies by score (Tier 1: top 10, Tier 2: next 40, Tier 3: next 50).
  7. Produce the report following references/output-template.md, including market insights, a tiered targeting strategy, and a quick-start action plan.
  8. Apply the best practices in references/examples.md throughout: favor quality over quantity, weight growth signals, and enrich contacts before recommending outreach.