onewave-ai/expansion-revenue-finder
expansion-revenue-finder/SKILL.md
Identifies upsell and cross-sell opportunities within existing customer accounts. Analyzes product usage, feature gaps, team growth, industry benchmarks, and competitive pressure to surface revenue expansion plays scored by potential, effort, and likelihood. Generates an expansion-playbook.md with account-by-account opportunities, recommended pitch, timing, and approach.
$ install --global
skillsauthnpx skillsauth add onewave-ai/claude-skills expansion-revenue-finderInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Jun 8, 2026, 5:14 AM20.3s7 files scanned
SKILL.md
- name:
- expansion-revenue-finder
- description:
- Identifies upsell and cross-sell opportunities within existing customer accounts. Analyzes product usage, feature gaps, team growth, industry benchmarks, and competitive pressure to surface revenue expansion plays scored by potential, effort, and likelihood. Generates an expansion-playbook.md with account-by-account opportunities, recommended pitch, timing, and approach.
- tools:
- Read, Write, Bash, Grep, Glob
- model:
- inherit
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 catalogreferences/account-profile.md-- per-account expansion profile and segment benchmarkingreferences/expansion-triggers.md-- seven trigger categories and the opportunity record templatereferences/scoring.md-- three-dimension scoring rubric, composite formula, and tier interpretationreferences/playbook-template.md-- the fullexpansion-playbook.mdoutput structurereferences/rules-and-edge-cases.md-- behavioral rules and edge-case handling
Workflow
-
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. -
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. -
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. -
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. -
Generate the playbook. Write
expansion-playbook.mdto the working directory (or a user-specified path), sorted by tier and composite score, with pitch, timing, approach, and portfolio-wide insights. Seereferences/playbook-template.md.
Apply the behavioral rules and edge-case handling throughout. See references/rules-and-edge-cases.md.
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