Amazon Market Entry Analyzer — GO / CAUTION / AVOID

• 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. apiclaw.py reviews-raw --asin X → fetch up to 100 raw reviews (10 credits, ~60s) b. For each review: render Map prompt via apiclaw.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 apiclaw.py review-reduce-prompt --label-type X --candidates '[...]' and have your LLM produce semantic clusters d. apiclaw.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

Unique Logic

Sub-Market Discovery

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

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)

Go/No-Go Decision

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.

Composite Command

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

Runs all 11 endpoints (~20 calls). Output JSON is large — use targeted extraction, not full read.

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 APIClaw 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:

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)

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

API Budget: ~20 calls