Execute the proven 5-phase framework for achieving Amazon #1 Bestseller status.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionamazon-bestseller-launchExecute the skills CLI command in your project's root directory to begin installation:
Fetches amazon-bestseller-launch from breverdbidder/life-os 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 amazon-bestseller-launch. Access via /amazon-bestseller-launch 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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Execute the proven 5-phase framework for achieving Amazon #1 Bestseller status.
Amazon's A10 algorithm ranks books based on:
| Metric | Weight | Target for #1 |
|---|---|---|
| Sales Velocity | 40% | 50-200+ sales in 24-48 hours |
| Conversion Rate | 25% | >15% page visitors → buyers |
| Reviews | 20% | 25+ reviews in first 30 days |
| Read-Through | 10% | >70% Kindle Unlimited pages read |
| Keywords/Categories | 5% | Rank top 3 in 3+ categories |
Select 3 categories using the "Low Competition, High Demand" formula:
CATEGORY_SCORE = (Monthly_Sales / #Books_in_Category) × Avg_Review_Count
Target: CATEGORY_SCORE > 500
Winning Category Criteria:
Amazon allows 7 backend keywords (50 chars each). Optimize using:
PRIMARY: [main topic] + [audience] + [benefit]
SECONDARY: [problem] + [solution] + [format]
LONG-TAIL: [specific niche] + [unique angle]
Keyword Research Tools:
□ Title: Primary keyword + benefit (≤200 chars)
□ Subtitle: Secondary keywords + specific outcome
□ Description: 4,000 chars, HTML formatting, 3 CTAs
□ Author Bio: Credibility + related books + social proof
□ A+ Content: 5 modules minimum (if Brand Registered)
□ Editorial Reviews: 3-5 pre-launch endorsements
Target: 50 ARC readers → 25+ reviews by launch day
ARC Recruitment Sources:
Email 1 (T-60): Announce book, recruit reviewers
Email 2 (T-45): Send ARC via BookFunnel
Email 3 (T-30): Check-in, ask for feedback
Email 4 (T-14): Reminder to prepare review
Email 5 (T-1): "Review goes live tomorrow!"
Email 6 (Launch): Direct link to leave review
| Day | Cumulative Reviews | BSR Impact |
|---|---|---|
| 1 | 5-10 | Enter top 10,000 |
| 7 | 15-20 | Enter top 1,000 |
| 14 | 20-25 | Stabilize ranking |
| 30 | 25-50 | Long-term visibility |
| Phase | eBook Price | Goal |
|---|---|---|
| Pre-order | $0.99 | Maximize pre-orders |
| Launch (Day 1-3) | $0.99 | Sales velocity |
| Post-launch (Day 4-7) | $2.99 | Revenue + ranking |
| Steady state | $4.99-9.99 | Profit margin |
Pre-orders count as Day 1 sales. Strategy:
Minimum viable launch team:
- 50 email subscribers committed to buy Day 1
- 25 ARC reviewers ready to post reviews
- 10 social media amplifiers (shares/posts)
- 5 podcast/blog appearances scheduled
6:00 AM EST - Verify listing is live, price correct
7:00 AM - Email blast #1 to full list
8:00 AM - Social media announcement (all platforms)
10:00 AM - Notify ARC team: "POST REVIEWS NOW"
12:00 PM - Email blast #2 (non-openers)
2:00 PM - Check BSR, adjust if needed
4:00 PM - Social media push #2
6:00 PM - Email blast #3 (last chance $0.99)
9:00 PM - Track final Day 1 metrics
| Category Competitiveness | Day 1 Sales Needed |
|---|---|
| Low (<1,000 books) | 25-50 |
| Medium (1,000-10,000) | 50-100 |
| High (10,000+) | 100-200+ |
Track every 2 hours on launch day:
# Key metrics to monitor
metrics = {
"bsr": "Best Seller Rank (lower = better)",
"category_rank": "Position in chosen categories",
"review_count": "Total reviews posted",
"review_avg": "Average star rating",
"also_bought": "Appearing in 'also bought' carousels"
}
□ Day 2-3: Continue $0.99 pricing
□ Day 3: Raise to $2.99 if BSR stable
□ Day 4-7: Amazon Ads campaign (ACoS target <50%)
□ Daily: Monitor reviews, respond to questions
Sponsored Products Campaign Setup:
Campaign Type: Manual targeting
Daily Budget: $20-50
Bid Strategy: Dynamic bids (down only)
Keywords: 50-100 from research
Match Types: Exact (60%), Phrase (30%), Broad (10%)
Target ACoS by Phase:
| Phase | Target ACoS | Goal |
|---|---|---|
| Launch (Week 1) | 100%+ OK | Visibility |
| Growth (Week 2-4) | 50-70% | Ranking |
| Profit (Month 2+) | 30-50% | Sustainable |
Enroll in KDP Select for 90-day exclusivity benefits:
Countdown Deal Timing:
PRE-LAUNCH (T-90 to T-0)
□ Category research: 3 low-competition categories selected
□ Keywords: 7 backend keywords optimized
□ Listing: Title, description, A+ content complete
□ ARC campaign: 50 readers recruited, ARCs distributed
□ Launch team: 50+ committed Day 1 buyers
□ Pre-orders: Open and promoted
□ Price: Set to $0.99 for launch
LAUNCH DAY (T-0)
□ Email sequence: 3 blasts scheduled
□ Social media: Posts scheduled all platforms
□ ARC team: Notified to post reviews
□ Monitoring: BSR tracked every 2 hours
POST-LAUNCH (T+1 to T+30)
□ Price increase: $0.99 → $2.99 → $4.99
□ Amazon Ads: Campaigns live
□ Reviews: 25+ posted
□ Countdown deal: Scheduled for T+21
references/category-research.mdreferences/email-templates.mdreferences/amazon-ads.mdscripts/launch-tracker.pyMake 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
amazon-bestseller-launch has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: amazon-bestseller-launch is focused, and the summary matches what you get after install.
I recommend amazon-bestseller-launch for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
amazon-bestseller-launch fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: amazon-bestseller-launch is focused, and the summary matches what you get after install.
amazon-bestseller-launch has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added amazon-bestseller-launch from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: amazon-bestseller-launch is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for amazon-bestseller-launch matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: amazon-bestseller-launch is focused, and the summary matches what you get after install.
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