SkillsAdd

onewave-ai/claude-skills

lookalike-customer-finder

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.

First seen Jan 24, 2026

Installation

$ npx skills add onewave-ai/claude-skills --skill lookalike-customer-finder
View on skills.sh ↗ GitHub repository ↗

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 287
License MIT
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,860 B
  • docs SUMMARY.md 1,834 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 289 installs

SKILL.md

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.