amazon-market-entry-analyzer

One-click market viability assessment for Amazon sellers. Analyzes market size, competition intensity, brand landscape, pricing structure, and consumer pain points to deliver a GO/CAUTION/AVOID recommendation. Uses all 11 ZooData API endpoints with cross-validation for data-backed decisions. Use when user asks about: market entry, can I sell, should I enter, market viability, is this niche worth it, category analysis, market opportunity, market assessment, niche evaluation, product category research. Pick this to EVALUATE a specific niche/category the user already named (one GO/CAUTION/AVOID verdict). To discover what to sell with no target in mind, use amazon-opportunity-discoverer; to track how categories shift over time, use amazon-market-trend-scanner. Requires ZOODATA_API_KEY.

Amazon Market Entry Analyzer — GO / CAUTION / AVOID

One input (keyword/category). Full market viability assessment with sub-market discovery.

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

Capabilities & Data Flow

• Network: only https://api.zoodata.ai (Bearer ZOODATA_API_KEY). Setting ZOODATA_BASE_URL to an untrusted host (anything other than api.zoodata.ai / *.zoodata.ai / localhost) makes the CLI refuse the request and withhold the key — the Bearer token is never sent to an untrusted host.
• Execution: bundled shared ZooData CLI {skill_base_dir}/scripts/zoodata.py (Python 3, stdlib-only). This skill allows only the composite, granular, diagnostic, and review-fallback commands explicitly routed below.
• Local files: a temporary /tmp/review_<ASIN>_<timestamp>/ working dir during the review fallback; reads the optional credential store ~/.zoodata/config.json.
• Sent to the API: keywords, category paths, ASINs, marketplace/date and numeric filter values only. Never sent: budget, experience level, risk tolerance, or any other user-profile text — profile inputs map client-side to numeric filters.
• Credits: every API call consumes account credits. For broad or ambiguous requests, state the estimated credit cost and confirm with the user before running multi-call scans. The composite market-entry command executes ~17+ API calls (~15-25 credits) in ONE invocation and has NO skip/trim flags — under a credit cap, use the granular commands instead.

CLI Route Selection

• Use market-entry for the full assessment.
• Use granular commands only when the workflow selected a granular route before the composite call, or when an explicit non-terminal fallback in this skill requires evidence not already returned.
• Map granular evidence requests only through this table:

API endpoint	CLI subcommand
categories	categories
markets/search	market
products/search	products
products/competitors	competitors
realtime/product	product
reviews/analysis	analyze
products/price-band-overview	price-band-overview
products/price-band-detail	price-band-detail
products/brand-overview	brand-overview
products/brand-detail	brand-detail
products/history	history

Use check only for credential diagnostics. Use reviews-raw, review-tag-prompt, review-reduce-prompt, and review-aggregate only for the documented review fallback.

Shared CLI Contract

Before selecting or invoking the first command, read and apply the local references/cli-contract.md. Reapply it after every granular or composite result and before any fallback, additional call, state write, interpretation, or user-facing report. Use this skill's fallback logic only when the shared contract classifies the result as non-terminal.

Local Interface Failure Output

For a terminal interface failure, respond in the user's language that the market-entry assessment could not be completed, followed by the succeeded and failed endpoint identifiers. Do not issue GO/CAUTION/AVOID, a viability score, risk-gate result, or entry strategy. Keep control tokens, parameters, and retry logs internal unless diagnostics are requested.

Input

• Required: keyword or categoryPath

• Optional: marketplace (default US)

• Optional (seller-side — drives the Small-Seller Entry Risk Gates section below):

  • budget — first-6-month capital available (e.g. "$10K", "$50K", "$200K+")
  • risk_tolerance — low / medium / high
  • ip_concern — known compliance, patent, trademark, or restricted-category concerns; or "none"

  If the user hasn't supplied these, ask once at the start of the workflow (a single batched question is fine). If the user declines or skips, omit the Risk Gates section from the final verdict and add a line under Data Provenance: "Risk Gates: skipped — seller-side inputs not provided." Do not guess thresholds — silent gate evaluation with invented inputs produces inconsistent verdicts across runs.

API Pitfalls (CRITICAL)

• Keyword search is broad → categoryPath is auto-resolved via categories endpoint, with fallback to top search result. If category_source is inferred_from_search, confirm with user
• Brand/price-band queries MUST include --category to avoid cross-category contamination
• Revenue = sampleAvgMonthlyRevenue (NEVER calculate avgPrice × totalSales — overestimates 30-70%)
• Sales = monthlySalesFloor (lower bound). Fallback: 300,000 / BSR^0.65, tag 🔍
• Use sampleOpportunityIndex, sampleTop10BrandSalesRate directly — never reinvent
• 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 (GO/CAUTION/AVOID verdict, market size, brand/price analysis) remain valid — do not re-run them.
• Aggregation endpoints without categoryPath produce severely distorted data

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), stop before any evidence call. Tell the user that a ZooData API key is required, link to https://zoodata.ai/en/api-keys, and explain that the key may be set in the environment or local config. Do not substitute public knowledge or a "for reference only" analysis.

On 401 Invalid Key

When _transport.status=401, stop further calls, tell the user that the configured key was rejected, direct them to https://zoodata.ai/en/api-keys, and do not fabricate missing data.

On 402 Credit Exhausted

When _transport.status=402, stop further calls. Report where the workflow stopped, any compatible partial findings already gathered, and returned credit metadata when present; direct the user to https://zoodata.ai/en/pricing and do not fabricate missing data.

Unique Logic

Sub-Market Discovery

Run market --category "{path}" --topn 10 --page-size 20, paginate all pages. Score each sub-market (1-100):

Dimension	Weight	Field	Good→100	Bad→0
Demand	25%	sampleAvgMonthlySales	≥1500	<200
Profit	25%	sampleAPlusRate	≥0.35	<0.15
New Entrant	20%	sampleNewSkuRate	≥0.20	<0.05
Brand Openness	20%	topBrandSalesRate	≤0.50	≥0.90 (inverted)
Capacity	10%	totalSkuCount	300-8000	extreme

Fallback (grossMargin=0 for all): redistribute to Demand 30%, New Entrant 25%, Brand 25%, Capacity 20%.

Present TOP 10 sub-markets. Ask user which to deep-dive (default: top 3). If ≤3 sub-markets, deep-dive all.

Market Viability Score (1-100)

Dimension	Weight	Good	Medium	Warning
Market Size	15%	>$10M/mo	$5-10M	<$5M
Market Trend	10%	Rising	Stable	Declining
Competition	25%	CR10<40%	40-60%	>60%
Price Opportunity	15%	oppIndex>1.0	0.5-1.0	<0.5
New Entrant Space	10%	>15%	5-15%	<5%
Consumer Pain Points	15%	Clear gaps	Some	None
Profit Potential	10%	>30%	15-30%	<15%

Go/No-Go Decision

Score	Signal	Action
70-100	✅ GO	Proceed with product development
40-69	⚠️ CAUTION	Possible but needs differentiation
0-39	🔴 AVOID	Too competitive or too small

CR10 dual-level check: Category CR10 PASS + sub-market CR10 FAIL → ⚠️ CAUTION. Both FAIL → AVOID. User criteria override: If user sets thresholds, ANY fail → CAUTION/AVOID. Never override.

Small-Seller Entry Risk Gates

Before upgrading a market to GO, run these gates against the user's budget, operating constraints, and risk tolerance:

Gate	Pass Signal	Hard-Block Signal
Capital fit	First order, launch PPC, storage, and cash cycle fit available runway	MOQ, inventory, or ad spend requires more cash than the seller can safely hold
Review barrier	Top competitors' review counts, ratings, and review velocity are reachable with a realistic launch plan	Conversion depends on matching an entrenched review moat
Compliance/IP risk	Certifications, restricted claims, safety rules, and trademark/design risks are known and manageable	Unresolved compliance, patent, trademark, or restricted-product exposure
Differentiation evidence	Clear pain point, feature, bundle, content, or price-band wedge	Only commodity resale, copycat design, or no defendable reason to buy
Validation speed	Demand and positioning can be tested in 7-30 days with samples or lightweight listings	Proof requires tooling, a full PO, or a large irreversible launch

Decision adjustment (precedence is pinned — apply in order):

1. Final verdict formula: final = MIN(score_tier, lowest_gate_tier) where the tier ordering is GO > CAUTION > AVOID. A score-90 GO with one gate at AVOID → final AVOID; a score-90 GO with one gate at CAUTION → final CAUTION.
2. GO requires BOTH a passing viability score AND every gate in PASS (or with a documented mitigation plan in the same workflow turn).
3. CAUTION fits markets with 1–2 uncertain gates that can plausibly be validated inside the Validation Speed window (7–30 days, sampled or lightweight listing).
4. AVOID is mandatory when any of {Compliance/IP, Capital fit, Review barrier} is a hard-block — regardless of viability score. These three categories are non-negotiable for small sellers.
5. Relationship to "User criteria override" above: that rule remains authoritative — if the user sets explicit thresholds (e.g. "min monthly sales 500", "max CR10 50%"), those override even gates. The Gates fire as the default backstop when no user-set thresholds cover the same dimension.
6. Tag each gate conclusion with the required confidence label (📊, 🔍, or 💡) — never treat the gate table itself as data-backed.

Composite Command

bash

python3 {skill_base_dir}/scripts/zoodata.py market-entry --keyword "{kw}" --category "{path}"

Runs all 11 endpoints (~20 calls). Apply references/cli-contract.md to its invocation and returned composite bundle.

Output

Respond in user's language.

Sections: Sub-Market Landscape → Executive Summary → Market Overview → Trend → Brand Landscape → Price Structure → Top 5 Competitors → Consumer Insights → Scoring Breakdown (with "Basis" column) → Entry Strategy → Data Provenance → API Usage → Cross-Market Comparison

If user provides COGS, calculate break-even and profit. If not, prompt for it.

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: ~20 calls