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Expansion Revenue Finder

  • 157 installs
  • 237 repo stars
  • Updated July 15, 2026
  • onewave-ai/claude-skills

Identify upsell, cross-sell, and seat-expansion opportunities inside existing accounts using usage patterns, plan limits, and feature adoption gaps.

About

Expansion revenue finder skill audits active customers for upgrade signals—hitting plan caps, adopting premium features, or showing team growth—to surface ranked upsell paths and quantified ARR expansion opportunities.

  • Usage-to-limit mapping
  • Plan upgrade triggers
  • Add-on fit scoring
  • Account whitespace map
  • Outreach talk tracks

Expansion Revenue Finder by the numbers

  • 157 all-time installs (skills.sh)
  • +5 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #384 of 853 Sales & Marketing skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/onewave-ai/claude-skills --skill expansion-revenue-finder

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Listed on Skillselion
Installs157
repo stars237
Last updatedJuly 15, 2026
Repositoryonewave-ai/claude-skills

What it does

Identify upsell, cross-sell, and seat-expansion opportunities inside existing accounts using usage patterns, plan limits, and feature adoption gaps.

Files

SKILL.mdMarkdownGitHub ↗

Expansion Revenue Finder

Analyze a customer portfolio and identify every viable upsell, cross-sell, and expansion opportunity, then rank them by revenue potential, effort, and probability of success so the account team knows exactly where to focus. Optimize for total portfolio expansion revenue, not individual deal wins. Ground every recommendation in data signals, not wishful thinking.

Contents

  • references/data-inputs.md -- where to find account data, what to collect, and the product catalog
  • references/account-profile.md -- per-account expansion profile and segment benchmarking
  • references/expansion-triggers.md -- seven trigger categories and the opportunity record template
  • references/scoring.md -- three-dimension scoring rubric, composite formula, and tier interpretation
  • references/playbook-template.md -- the full expansion-playbook.md output structure
  • references/rules-and-edge-cases.md -- behavioral rules and edge-case handling

Workflow

1. Collect data. Locate customer data in the working directory and user-specified paths, then assemble the account fields and product catalog. See references/data-inputs.md. If no structured data exists, ask the user to describe their accounts and note reduced scoring confidence.

2. Profile and benchmark each account. Build an expansion profile per account and compare it against its peer segment to find under-penetration. See references/account-profile.md. Without external benchmarks, use the portfolio's top quartile as the benchmark.

3. Scan for triggers. Check every trigger category for each account. Record an opportunity only when a trigger fires AND a matching product/feature is available to sell. Capture each as a structured opportunity record. See references/expansion-triggers.md. Flag underutilization as a separate "activation opportunity," not an upsell.

4. Score and tier. Rate each opportunity on revenue potential, effort, and likelihood (1-10 each), compute the composite, and assign a tier. See references/scoring.md.

5. Generate the playbook. Write expansion-playbook.md to the working directory (or a user-specified path), sorted by tier and composite score, with pitch, timing, approach, and portfolio-wide insights. See references/playbook-template.md.

Apply the behavioral rules and edge-case handling throughout. See references/rules-and-edge-cases.md.

Related skills

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