kdp-niche-finder

name

kdp-niche-finder

description

Find PROFITABLE, low-competition KDP coloring-book niches using REAL Amazon data (Apify top-10 BSR/reviews/price) and a deterministic Opportunity Score — not vibes. Sweeps many candidate keywords, computes Opp = demand/competition for each, corrects for the common traps (single-winner inflation, off-season seasonal reads, low-demand floors), and returns a ranked, evidence-backed shortlist of money niches plus a 5-10 book series for each. USE THIS whenever the user wants to find/research/validate KDP or coloring-book niches, asks "what book should I make", "find a niche", "is this niche worth it", "tim ngach", "ngach kiem tien", "research niche", "blue ocean niche", wants to AVOID saturated markets, or wants to verify a niche idea with hard Amazon numbers. Prefer this over guessing from WebSearch — Amazon blocks direct page fetches, so Apify is the only reliable hard-data source here.

KDP Niche Finder — hard-data niche research

Find coloring-book (and low-content) niches that actually make money: real demand, beatable competition, margin headroom. Every verdict traces to an Apify top-10 pull + a deterministic score — never a guess. WebSearch can suggest candidates, but it routinely overrates niches (e.g. "fishing for men" looks hot but real BSR shows modest demand). Only Apify numbers decide.

The one metric that matters: Opportunity Score

Opp = avg monthly sales (top-10)  /  avg review count (top-10)

• Numerator = DEMAND — are people actually buying? (from BSR → sales table)
• Denominator = COMPETITION BARRIER — have rivals locked it with reviews?
• High Opp = real demand AND nobody has cemented dominance = a lane you can break into.

Opp	Meaning
≥ 5	🌊 BLUE_OCEAN — prioritize
2–5	🌤 MODERATE
0.5–2	COMPETITIVE
< 0.5	SATURATED — avoid

Opp never stands alone. Three traps make the raw number lie — always sanity-check (see Reading the numbers below): single mega-seller inflation, off-season seasonal collapse, and a low-demand floor where Opp is high only because reviews are ~0 (nobody competing or buying).

Workflow

Step 0 — Confirm Apify is live (token pool)

The hard data comes from scripts/apify_research.py (Apify junglee/Amazon-crawler). It reads APIFY_API_TOKEN from .env, which may be a comma-separated pool — the script auto-rotates to the next token on HTTP 401/402/403 ("Monthly usage hard limit exceeded"). Quick check:

python3 scripts/apify_research.py top10 "frog coloring book for adults" | head -5

If it prints JSON → live. If all tokens 403 → tell the user Apify credit is exhausted; they must top up or add another token to the comma list in .env. Do NOT silently fall back to WebSearch guesses and present them as hard data — say clearly the result is LOW_CONFIDENCE if you must.

Step 1 — Build the candidate keyword list

You need a list of key,keyword pairs to sweep. Sources, in order of value:

1. The user's ideas — whatever niches they want validated.
2. Category brainstorm — for a theme (e.g. "male hobbies", "cottagecore", "mental health"), enumerate 8-15 concrete long-tail keywords + audience. Favor SPECIFIC long-tail ("bass fishing coloring book") over generic heads ("coloring book") — heads are always saturated; the money is in the specific intersection a brand hasn't filled.
3. Sub-angles of a winner — once a niche scores well, sweep its species/style/occasion variants to build a 5-10 book SERIES (e.g. fishing → bass, deep-sea, trout, kayak, ice…).

Keep keys snake_case; they become the saved filenames.

Step 2 — Sweep the keywords (pull real top-10)

Use the bundled helper — it pulls each keyword (skipping ones already cached), saving each to data/niches/apify/<key>.json:

python3 .Codex/skills/kdp-niche-finder/scripts/niche_sweep.py \
  "bass_fishing=bass fishing coloring book" \
  "deep_sea_fishing=deep sea fishing coloring book" \
  "frog_adults=frog coloring book for adults"

Each pull takes ~30-60s. For a large list (10+), run it in the background (run_in_background) and continue once it completes — don't block. Re-running is cheap (cached pulls are skipped), so it's safe to resume after an interruption. Conserve Apify credits: only pull what you'll use, and reuse the saved JSONs (they persist in data/niches/apify/).

Step 3 — Rank by real Opportunity Score

python3 scripts/rank_niches.py

This reads every data/niches/apify/*.json, computes avg/min BSR, est. monthly sales (BSR→sales table baked in), avg reviews, avg price, and Opp, then prints a ranked table with a verdict per niche. It also flags mo<12 ⇒ WEAK/low-demand regardless of Opp (the low-demand floor).

Step 4 — Read the numbers (apply judgment, don't trust raw Opp)

For each high-Opp niche, open its data/niches/apify/<key>.json and check top10_titles + top10_bsr + top10_reviews to catch the three traps:

• Single-winner trap. If min_bsr is tiny (e.g. 523) but avg_bsr is in the millions, ONE dominant book is carrying the whole average → not a real opening. (Saw this on fly_fishing.)
• Seasonal off-season collapse. Halloween/Christmas niches pulled in June read near-dead (BSR in the millions) because it's off-peak — you CANNOT judge a seasonal niche off-season. Re-pull ~2-3 months before the season, or judge on supply/competition alone. Conversely, a seasonal niche that still ranks live off-season is a STRONG signal.
• Low-demand-floor / empty Opp. Very high Opp driven by avg reviews ≈ 0 can mean "nobody has cemented it" (good) OR "nobody's buying" (bad). Cross-check the numerator: is anything actually selling (a book under BSR ~100k)? If not, it's low-demand, not blue ocean. Also watch the inverse: a keyword that returns GENERAL results (e.g. "halloween bookstore" returns generic halloween) means no dedicated competitor exists yet — the intersection is genuinely OPEN.

Also weight demand vs competition together: a niche with 173 sales/mo at 24 reviews beats one with 8 reviews but 6 sales/mo — both are "low competition" but only the first has real money.

Step 5 — Deliver + save

Write a ranked shortlist to data/niches/ (markdown). For the top picks, recommend a 5-10 book series (each volume a distinct sub-angle/keyword so they don't cannibalize). Save raw pulls stay in data/niches/apify/. Optionally scaffold per-book briefs under data/niches/production/<NN>-<niche>/ (README + one brief per volume) so work resumes later.

Output format

🎯 NICHE FINDER — <topic/keywords swept>  (N niches, real Apify data)

RANKED (by Opp, judgment-corrected)
| # | niche | demand/mo | avg reviews | Opp | verdict | note (trap?) |
...

✅ POTENTIAL NICHES (survive the traps)
 1. <niche> — <why: demand × low competition, one-line>
 ...
❌ AVOID — saturated: <list (huge reviews)> | low-demand: <list> | single-winner: <list>

SERIES for the top pick (5-10 books, distinct sub-angles): <list of volumes + keywords>

NEXT: re-pull the exact keyword right before producing (sub-angles shift weekly); seasonal
niches need an in-season re-pull. Then /kdp-create-book "<flagship concept>".

Rules

• NEVER fabricate BSR/reviews/price. Only report what the Apify pull returns; if Apify is down, say so and mark anything from WebSearch as LOW_CONFIDENCE.
• ALWAYS sanity-check raw Opp against the three traps before calling something a winner — a pure-number BLUE_OCEAN that's actually a single-winner or off-season read is worse than useless.
• Prefer SPECIFIC long-tail keywords; generic head terms ("coloring book for adults") are always saturated and waste a pull.
• For a SERIES, each volume must be a genuinely different sub-angle (species/style/occasion) — KDP forbids duplicate content and near-identical books cannibalize each other.
• Validate the FLAGSHIP (vol 1) with real sales before batch-producing the rest of a series.
• Conserve Apify credits: reuse cached data/niches/apify/*.json; only pull keywords you'll act on.
• Re-pull a niche's exact keyword right before producing it — niches shift week to week.

Reference

• references/opportunity-score.md — the BSR→sales table, full math, and worked trap examples.
• Core scripts (in the repo, not the skill): scripts/apify_research.py (token-pool puller), scripts/rank_niches.py (ranker). The skill's scripts/niche_sweep.py wraps the puller for batch lists.