amazon-opportunity-discoverer

Tell me your budget and experience. I find opportunities, score them, and rank.

Files

• Script: {skill_base_dir}/scripts/zoodata.py — run --help for params
• Reference: {skill_base_dir}/references/reference.md (field names & response structure)

Credential

Required: ZOODATA_API_KEY. Get free key at zoodata.ai/api-keys

Input

• Required: keyword or category + budget (Low/Med/High) + experience (Beginner/Intermediate/Advanced)
• Recommended: risk tolerance (Conservative/Moderate/Aggressive)
• Optional: fulfillment preference (FBA/FBM), specific filter criteria

API Pitfalls (see zoodata skill for full list)

• categoryPath is auto-resolved via categories, with fallback to top search result. If category_source is inferred_from_search, confirm with user — keyword-only queries contaminate results
• All keyword-based endpoints MUST include --category when locked
• Revenue = sampleAvgMonthlyRevenue directly. Sales = monthlySalesFloor (lower bound)
• reviews/analysis needs 50+ reviews. Fallback chain when sample is insufficient:
  1. Lightweight: realtime/product ratingBreakdown — only star distribution, no themes
  2. Full 11-dim insights — bypass /reviews/analysis entirely: a. zoodata.py reviews-raw --asin X → fetch up to 100 raw reviews (10 credits, ~60s) b. For each review: render Map prompt via zoodata.py review-tag-prompt --review '<json>' and have your own LLM produce JSON tags (sentiment + 11 dimensions) c. Collect candidate phrases per dimension; for each dimension render Reduce prompt via zoodata.py review-reduce-prompt --label-type X --candidates '[...]' and have your LLM produce semantic clusters d. zoodata.py review-aggregate --reviews R --tagged T --clusters C → consumerInsights output compatible with /reviews/analysis
  3. Fallback caveats (apply to the 4-step chain above — lessons from end-to-end validation):
     • Working dir: WORK=/tmp/review_<ASIN>_$(date +%s) && mkdir -p $WORK
     • Step b CLI behavior: review-tag-prompt RENDERS the prompt only; YOUR LLM produces the JSON. Render once to learn the schema, then produce tags for all N reviews in one in-context pass (don't call the CLI N times).
     • Step c candidate extraction (Python one-liner): candidates = {d: sorted({el.strip().lower() for t in tagged for el in (t.get(d) or [])}) for d in DIMS}
     • Small-sample rule (reviewCount<50): demote single-mention items 📊→🔍; NEVER attach table-level or section-header 📊 when any row inside is 🔍; suppress "🔴 Critical" verdicts on count=1
     • Scope: fallback replaces ONLY the /reviews/analysis aggregation. This skill's primary workflow outputs (opportunity scoring, mode-based selection, ranked candidate list) remain valid — do not re-run them.
• Deduplicate ASINs across modes — same product appears in multiple scans
• Each mode has built-in filters that STACK with user filters (e.g. beginner: $15-60, sales≥300)

On Missing Key

When ZOODATA_API_KEY is not set (verify via python {skill_base_dir}/scripts/zoodata.py check — exits 2 if no key in env or ~/.zoodata/config.json): follow the "On Missing Key" protocol in zoodata/SKILL.md — STOP before any call, link the user to https://zoodata.ai/en/api-keys, and DO NOT produce a "partial analysis from public knowledge" / "for reference only" fallback as a substitute.

On 401 Invalid Key

When zoodata.py returns code 401: follow the "On 401 Invalid Key" protocol in zoodata/SKILL.md — STOP further calls, tell the user the key was rejected and direct them to api-keys, do not fabricate missing data.

On 402 Credit Exhausted

When zoodata.py returns code 402: follow the "On 402 Credit Exhausted" protocol in zoodata/SKILL.md — STOP further calls, report partial findings already gathered, do not fabricate missing data.

Unique Logic

Profile → Strategy Mapping

Profile	Primary Modes	Price	Max Reviews
Beginner + Conservative	beginner, long-tail, fbm-friendly	$15-60	<50
Beginner + Moderate	beginner, emerging, low-price	$10-50	<100
Intermediate + Moderate	fast-movers, underserved, single-variant	$15-80	<200
Intermediate + Aggressive	high-demand-low-barrier, speculative	$10-100	<500
Advanced + Aggressive	fast-movers, speculative, top-bsr	any	any

User Criteria → Filter Params

Always translate: "300+ monthly sales" → --sales-min 300, "reviews <100" → --ratings-max 100, "$15-35" → --price-min 15 --price-max 35. If user has specific criteria, use custom filters (Approach B/C), NOT default modes.

Data-Driven Category Selection (no specific category given)

Scan with market --keyword "{broad}" --topn 10, rank subcategories by: newSkuRate>10%, topBrandSalesRate<60%, fbaRate>50%, avgPrice $10-50, avgMonthlySales>200. Pick top 3-5.

Opportunity Score (per candidate, 1-100)

Dimension	Weight	Good	Medium	Warning
Demand Signal	20%	sales>300, rev>$5K	100-300	<100
Competition Gap	20%	reviews<200, CR10<40%	200-1K, 40-60%	>1K, >60%
Price Opportunity	15%	in best opp band, opp>1.0	0.5-1.0	<0.5
Trend Momentum	15%	BSR rising	stable	declining
Profit Margin	15%	>30%	15-30%	<15%
Differentiation	10%	clear pain points	some gaps	none
Profile Fit	5%	matches user profile	partial	mismatch

Tiers

Score	Tier	Label
80-100	S	🔥 Hot — act fast
60-79	A	✅ Strong — worth pursuing
40-59	B	⚠️ Moderate — needs differentiation
0-39	C	❌ Weak — skip

Quick-Scan Mode (~10 credits): 2 modes × 1 page, skip realtime/trend. Label as "directional only."

Composite Command

python3 {skill_base_dir}/scripts/zoodata.py opportunity-scan --keyword "{kw}" --category "{path}" --modes "beginner,emerging,underserved"

Or with custom filters: --sales-min 300 --ratings-max 100 --price-min 15 --price-max 35

Output

Respond in user's language.

Sections: Scan Summary → Top 10 Opportunities Table → Detailed Analysis (Top 3) → Category Heatmap → Risk Alerts → Next Steps (S: buy sample, A: deep-dive, B: watch) → Data Provenance → API Usage

If user provides COGS, calculate profit. User criteria override: ANY fail → CAUTION/AVOID.

Language (required)

Output language MUST match the user's input language. If the user asks in Chinese, the entire report is in Chinese. If in English, output in English. Exception: API field names (e.g. monthlySalesFloor, categoryPath), endpoint names, technical terms (e.g. ASIN, BSR, CR10, FBA, credits) remain in English.

Disclaimer (required, at the top of every report)

Data is based on ZooData API sampling as of [date]. Monthly sales (monthlySalesFloor) are lower-bound estimates. This analysis is for reference only and should not be the sole basis for business decisions. Validate with additional sources before acting.

Confidence Labels (required, tag EVERY conclusion)

• 📊 Data-backed — direct API data (e.g. "CR10 = 54.8% 📊")
• 🔍 Inferred — logical reasoning from data (e.g. "brand concentration is moderate 🔍")
• 💡 Directional — suggestions, predictions, strategy (e.g. "consider entering $10-15 band 💡")

Rules: Strategy recommendations are NEVER 📊. Anomalies (>200% growth) are always 💡. User criteria override AI judgment.

Aggregate-label rule (applies to ALL report output, not just fallback): NEVER attach 📊 to ANY element that aggregates or groups underlying content when ANY piece of that content is 🔍 or 💡. "Aggregate/grouping elements" include:

• Section headers at EVERY level (#, ##, ###, ####) — including top-level summary sections like "Overall Score", "Verdict", "Executive Summary"
• Summary/score lines anywhere in the report (e.g. ## Overall Score — 27/100 · Grade F 📊 is WRONG if any Basis row inside is 🔍)
• Table column headers in comparison tables (e.g. **Target ASIN** 📊 as a column label is WRONG if any cell in that column contains 🔍)
• Table row headers or row-aggregation labels (when the row aggregates multiple cells of mixed confidence)
• Any other visual grouping label — bullet-list group titles, callout box titles, etc.

A group-level 📊 implies the whole block/column/row is data-backed, which smuggles inferred/directional content into the 📊 tier via visual grouping. Either (a) omit the group-level label entirely (preferred when content mixes tiers), or (b) use the LOWEST confidence present inside (🔍 if any underlying content is 🔍; 💡 if any is 💡). This is a universal output-quality rule — it applies regardless of which fallback path (if any) was triggered.

Emoji reservation rule (closely related): The three confidence symbols 📊 🔍 💡 are RESERVED for confidence labeling. NEVER use them as decorative prefixes on section headers, table headers, or any aggregate element — even when you also include a correct confidence suffix on the same line. Example:

• ❌ WRONG: ## 📊 Overall Score — 27/100 · Grade F 🔍 (the leading 📊 reads as a data-backed claim even though the trailing 🔍 is correct)
• ✅ RIGHT: ## Overall Score — 27/100 · Grade F 🔍 (no decorative emoji, just the proper confidence suffix)
• ✅ RIGHT: ## 🎯 Overall Score — 27/100 · Grade F 🔍 (use non-reserved decorative icons like 🎯 🧭 📋 📝 📂 🏁 🚨 🏆 🔔 when a visual prefix is desired)

Decorative emoji ≠ confidence label — but from a reader's perspective, a leading 📊/🔍/💡 is indistinguishable from a confidence claim. Reserve these three symbols EXCLUSIVELY for confidence annotation to avoid ambiguity.

Data Provenance (required)

Include a table at the end of every report:

Data	Endpoint	Key Params	Notes
(e.g. Market Overview)	markets/search	categoryPath, topN=10	📊 Top N sampling, sales are lower-bound
...	...	...	...

Extract endpoint and params from _query in JSON output. Add notes: sampling method, T+1 delay, realtime vs DB, minimum review threshold, etc.

API Usage (required)

Endpoint	Calls	Credits
(each endpoint used)	N	N
Total	N	N

Extract from meta.creditsConsumed per response. End with Credits remaining: N.

API Budget: ~50-60 credits