niche-scout
/niche-scout
Ocenia opłacalność niszy na Amazon KDP przez analizę BSR, wolumenu słów kluczowych i scoring konkurencji przy wyborze tematów książek.
- Autor
- cdeistopened ↗
- Repozytorium
- skill-stack ↗
- Licencja
- MIT
- Ostatnia zmiana repozytorium
- 07.07.2026
Ocena i bezpieczeństwo
- Pliki w paczce
- 1
- Skrypty
- 0
- Komentarze HTML
- 0
- Adresy URL w SKILL.md
- 0
Brak sygnałów. Repozytorium zmienione 07.07.2026, bez odwołań do wycofanych modeli i API.
MIT, otwarta: można używać i zmieniać, z zachowaniem informacji o autorze.
Co robi i kiedy
Ocenia opłacalność niszy na Amazon KDP przez analizę BSR, wolumenu słów kluczowych i scoring konkurencji przy wyborze tematów książek.
Argumenty
/niche-scout [keyword phrase]
Oryginalny opis (EN)
Evaluate Amazon KDP niche profitability using BSR analysis, keyword volume, and competition scoring. Use when researching book niches, validating publishing ideas, or comparing market opportunities.
Jak działa
- Usage
- Workflow
- Niche Selection Checklist (from Ch 2)
- Reference Files
- Tool Dependencies
- Related Skills
Podgląd SKILL.md
--- name: niche-scout description: Evaluate Amazon KDP niche profitability using BSR analysis, keyword volume, and competition scoring. Use when researching book niches, validating publishing ideas, or comparing market opportunities. argument-hint: "[keyword phrase]" --- # Niche Scout Evaluate whether a keyword/topic is a profitable KDP publishing niche. Based on the 3-step validation framework from Sean Dollwet, Dale Roberts, and Dave Chesson (175 episodes distilled in Kings of Kindle). ## Usage ``` /niche-scout beekeeping for beginners /niche-scout "anxiety management" /niche-scout claude code AI coding assistant ``` ## Workflow ### Step 1: Amazon Keyword Volume (DataForSEO) Run the keyword volume script to get Amazon search volume and related keywords: ```bash python3 ~/.claude/skills/niche-scout/scripts/keyword-volume.py "$ARGUMENTS" ``` This returns: - Amazon search volume for the seed keyword + 9 variations - Related keywords with volume (up to 20) - Use these to assess **demand** and identify series potential (10-keyword strategy) **Demand threshold:** Seed keyword should have 200+ monthly Amazon searches. Related keywords reveal series opportunities. ### Step 2: BSR Analysis (Apify Amazon Scraper) Run the BSR scraper to get Best Seller Rank for top books: ```bash python3 ~/.claude/skills/niche-scout/scripts/bsr-scraper.py "$ARGUMENTS" ``` This runs two Apify calls: 1. **Search results** — gets titles, prices, ratings, ASINs for top 20 books 2. **Product details** — gets BSR, publisher, categories for top 10 Wait for both to complete (typically 30-90 seconds each). ### Step 3: Score the Niche Apply Sean Dollwet's BSR benchmarks to the scraped data: | Metric | Target | Scoring | |--------|--------|---------| | Books under 5,000 BSR | No more than 5 of top 20 | >5 = too competitive | | Books under 30,000 BSR | Most or all of top 20 | Sweet spot for new publishers | | Books under 100,000 BSR | At least 7 of 10 | Minimum viable demand | | Self-published on page 1 | At least 2-3 | Proves indie can compete | | Search results count | Under 3,000 | Lower = easier to rank | **Self-published detection signals:** - Publisher = "Independently published" or "Independently Published" - Price $2.99-$4.99 range - Keyword-heavy title (exact match of search term) - Stock/template cover design ### Step 4: Check All Three Platforms A keyword that's weak on Kindle might be strong on paperback or audiobook: | Platform | Good BSR | Notes | |----------|----------|-------| | Kindle eBooks | Under 50,000 | Most competitive; most publishers start here | | Paperback | Under 100,000 | Higher royalties; reference/cookbooks do better | | Audiobook (Audible) | Under 100 search results | Least competitive; under-served in most niches | ### Step 5: Generate the Report Output a structured niche report with: 1. **Keyword volume table** — seed + related keywords with Amazon search volume 2. **BSR table** — top 10 books sorted by BSR with publisher, self-pub flag 3. **Benchmark scorecard** — pass/fail on each Dollwet criterion 4. **Estimated daily sales** — using BSR-to-sales conversion (see reference/bsr-sales-table.md) 5. **Competition signals** — weak covers, bad titles, low reviews = beatable 6. **Series potential** — can you find 5-10 related keywords for a book series? 7. **Verdict** — VIABLE / BORDERLINE / TOO COMPETITIVE / DEAD ### Step 6: Hot vs Evergreen Assessment Classify the niche: - **Hot topic**: Trending, short window, fast cash if you move quickly - **Evergreen**: Steady demand year-round, compounds over time - **Seasonal**: Predictable spikes (gardening in spring, etc.) The ideal portfolio mixes both. Check Google Trends for seasonality patterns. --- ## Niche Selection Checklist (from Ch 2) Before committing to a niche, ALL boxes must check: - [ ] **Demand confirmed:** Multiple books in top results have BSR under 100,000 - [ ] **BSR sweet spot:** Most of the top 20 books fall between 10,000 and 30,000 - [ ] **Competition beatable:** No more than 5 books under 5,000 BSR in top 20 - [ ] **Self-published proof:** At least 2-3 self-published books selling well on page 1 - [ ] **Weak spots visible:** You can identify covers, titles, or descriptions you can beat - [ ] **Multi-platform potential:** Checked Kindle, paperback, and audiobook - [ ] **Search results count:** Under 3,000 (lower = better) - [ ] **Series potential:** Can identify 5-10 related keyword variations - [ ] **No trademark issues:** Keywords don't include trademarked terms --- ## Reference Files - For BSR-to-sales conversion: see [reference/bsr-sales-table.md](reference/bsr-sales-table.md) - For tool comparison: see [reference/tools.md](reference/tools.md) - For the full keyword workflow: use `/keyword-fill` after niche is validated ## Tool Dependencies - **DataForSEO** — Amazon keyword volume + related keywords (credentials in ~/.zshrc or hardcoded) - **Apify** — Amazon product scraping for BSR, publisher, categories (APIFY_TOKEN in env) - **Python 3** with `requests` library ## Related Skills - **keyword-fill** — Fill the 7 Amazon keyword boxes (run AFTER niche is validated) - **book-architect** — Generate outline + title/subtitle (run AFTER niche + keywords) - **listing-optimizer** — Write description + select categories - **deep-research** — For deeper market analysis on borderline niches - **seomachine** — For Google keyword data (complements Amazon data)
Wersje
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