Design sales compensation plans aligned with business goals and customer retention.
Works with
Starts with the standard 50/50 base-to-variable split, then adjusts based on sales cycle length, customer churn patterns, and business model to avoid misaligned incentives
Emphasizes tying compensation to customer outcomes and net dollar retention, not just closed bookings, to reward sticky deals over churny ones
Guides ramp structures for new hires (3–6 months for SMB, 6–12 months for enterprise) wit
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsales-compensationExecute the skills CLI command in your project's root directory to begin installation:
Fetches sales-compensation from refoundai/lenny-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate sales-compensation. Access via /sales-compensation in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Help the user design effective sales compensation plans using frameworks from 2 product leaders.
When the user asks for help with sales compensation:
Jason M Lemkin: "It's usually 50/50, right? 50% base, 50% bonus for a sales rep." The standard OTE structure is 50% base salary and 50% variable commission. This is a common baseline for quota-carrying roles.
Sahil Mansuri: "Sales comp plans are stuck in the stone ages... What we haven't done is built a modern technical sales compensation plan that actually aligns the needs and incentives of the business, the customer and the rep." Consider designing comp that rewards long-term retention and net dollar retention, not just closed deals.
If your business depends on customer retention, comp plans should include components tied to customer outcomes, not just initial bookings. Reps who close churny deals should earn less than those who close sticky customers.
New sales hires need ramp periods with guaranteed draws or reduced quotas while they learn the product and market. Typical ramps are 3-6 months for SMB and 6-12 months for enterprise.
Complex comp plans with many variables lead to confusion and gaming. Simple plans where reps understand exactly what actions increase their pay are more effective.
For all 2 insights from 2 guests, see references/guest-insights.md
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
We added sales-compensation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: sales-compensation is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in sales-compensation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
sales-compensation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for sales-compensation matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: sales-compensation is the kind of skill you can hand to a new teammate without a long onboarding doc.
sales-compensation has been reliable in day-to-day use. Documentation quality is above average for community skills.
sales-compensation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: sales-compensation is focused, and the summary matches what you get after install.
We added sales-compensation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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