--- name: amazon-serp-analysis version: 1.0.1 description: "Analyze Amazon market tracks/niches for go/no-go investment decisions. Given a seed keyword, scrapes SERP, expands related keywords to map the full track landscape, scores competition intensity, estimates demand, and delivers a structured recommendation with entry strategy. Trigger on: 赛道分析, 赛道研究, 值不值得做, 市场分析, 竞争分析, niche analysis, track analysis, 亚马逊赛道, 切入点, 要不要做这个品, 这个类目怎么样." --- # amazon-serp-analysis Comprehensive Amazon niche analysis pipeline. Input: a seed keyword. Output: full track landscape map, competition scoring, demand assessment, and go/no-go recommendation with specific entry angles. ## Requirements - `omni-scraper` skill installed and configured (required — provides SERP + PDP data) - `amazon-listing-judge` skill installed (optional — adds listing quality depth) - SellerSprite MCP tools (optional — adds keyword volume and search trends) - ClickHouse MCP (optional — adds historical brand analytics data) --- ## Workflow ### Step 1 — Seed SERP Scrape Scrape Amazon search results for the seed keyword: ```bash curl -s -X POST "$CLAW_API_BASE/api/scrape" \ -H "Content-Type: application/json" \ -d '{ "claw_key": "'"$CLAW_KEY"'", "url": "https://www.amazon.com/s?k=KEYWORD_URL_ENCODED", "mode": "scraper" }' ``` From the response `parsed.results`, extract for each position: - `position`, `asin`, `title`, `price`, `rating`, `review_count`, `bought_past_month`, `sponsored`, `ac_badge` **Stop if**: `parsed` is null and `body` is also null — report error and stop. --- ### Step 2 — Keyword Expansion (build track landscape) Goal: identify 5–10 related search terms that form the full track. **If SellerSprite MCP is available**, use it to expand keywords: ``` mcp__sellersprite-mcp__keyword_miner { keyword: "", marketplace: "US" } mcp__sellersprite-mcp__traffic_extend { keyword: "", marketplace: "US" } ``` Take the top 5–8 related keywords by search volume. **Fallback (no SellerSprite)**: Infer related keywords from SERP titles by: 1. Extracting noun phrases and modifiers from the top 10 product titles 2. Identifying variant terms (material, size, use-case, audience) 3. Generating 5–8 plausible search terms that a buyer might use 4. Label these as "inferred" in the output **If ClickHouse MCP is available**, check brand analytics trend data: ```sql SELECT search_term, SUM(clicks) as total_clicks FROM amazon.brand_analytics WHERE search_term ILIKE '%%' AND date >= today() - INTERVAL 90 DAY GROUP BY search_term ORDER BY total_clicks DESC LIMIT 10 ``` This validates which related terms have real search volume. --- ### Step 3 — Scrape 1–2 Key Related Keywords Pick the 2 highest-volume related keywords from Step 2 and scrape their SERPs (same curl as Step 1). This reveals: - Whether the same products dominate multiple related SERPs (brand lock) - Price and review patterns across the broader track **Token budget**: If context is getting long, skip this step and note it was skipped. --- ### Step 4 — Deep-dive Top 5 Organic Products For the top 5 non-sponsored products from the seed keyword SERP: **If `amazon-listing-judge` is available**: ```bash uv run /scripts/grade.py ``` Record the total score and key weaknesses. **Fallback**: Use the SERP data already collected. Estimate listing quality as: - High (>80): has AC/BS badge + ≥4.5 stars + ≥1K reviews + bought_past_month - Medium (55-80): has some but not all signals - Low (<55): no badges, low reviews, no sales velocity signal --- ### Step 5 — Competition Scoring Calculate the following metrics from collected data: #### 5a. Review Barrier ``` avg_reviews = average review count of top 10 organic results ``` - < 200 reviews avg → **Low barrier** (🟢 +2) - 200–1,000 → **Medium barrier** (🟡 +1) - 1,000–3,000 → **High barrier** (🔴 0) - > 3,000 → **Very high barrier** (🔴 -1) #### 5b. Brand Concentration ``` count unique brands in top 10 organic results ``` - 8–10 unique brands → **Fragmented** (🟢 +2) - 5–7 unique brands → **Moderate** (🟡 +1) - 3–4 unique brands → **Concentrated** (🔴 0) - 1–2 brands dominating 5+ spots → **Locked** (🔴 -1) #### 5c. Demand Signals Count how many of the top 10 organic results have "bought in past month" badge: - 6–10 products → **Hot track** (🟢 +2) - 3–5 products → **Active** (🟡 +1) - 0–2 products → **Low demand or seasonal** (🔴 0) #### 5d. Listing Quality Gap Average listing quality score of top 5 organic products: - Average score < 60 → **Big quality gap** (🟢 +2, easy to outcompete) - 60–75 → **Some gap** (🟡 +1) - > 75 → **Well-optimized** (🔴 0) #### 5e. Price Range Health ``` min_price = lowest price among top 10 max_price = highest price among top 10 range_ratio = max_price / min_price ``` - ratio > 3 → **Wide range, room to position** (🟢 +1) - ratio 1.5–3 → **Moderate** (🟡 0) - ratio < 1.5 → **Commodity, price war risk** (🔴 -1) #### Competition Score Total (max 9) ``` competition_score = sum of all sub-scores above ``` - 7–9 → 🟢 **Low competition — favorable** - 4–6 → 🟡 **Moderate — conditional entry** - 0–3 → 🔴 **High competition — difficult** --- ### Step 6 — Track Demand Score Estimate overall demand level: **If SellerSprite available**: use `traffic_keyword_stat` for monthly search volume. **From SERP signals**: - Count products with "bought in past month" → quantify total estimated monthly units - Estimate: if avg = "1K+" per product × 10 products = ~10K monthly units in track - Note: this is a rough lower bound estimate Seasonal flags: - If keyword contains holiday/event terms, flag as seasonal - If ClickHouse shows <3 months of search history, flag as trend/fad --- ### Step 7 — Synthesis & Recommendation #### Entry Assessment Matrix | Factor | Your Score | Implication | |--------|-----------|-------------| | Competition score | X/9 | | | Demand level | High/Med/Low | | | Price positioning room | Yes/No | | | Listing quality gap | Yes/No | | #### Verdict 🟢 **GO** — competition_score ≥ 7 AND demand ≥ Medium 🟡 **CONDITIONAL** — competition_score 4–6 OR strong demand with one differentiator 🔴 **NO-GO** — competition_score ≤ 3 OR demand Low with high barrier #### Entry Angle Candidates (pick the best fit) 1. **Price disruption**: If top products cluster at $20+, enter at $12–15 with comparable quality 2. **Listing quality gap**: If top listings score < 65, win on listing optimization alone 3. **Underserved variant**: If SERP titles share one attribute, find the missing variant buyers search for 4. **Bundle/differentiation**: If all products are standalone, add accessories or improved feature 5. **Niche long-tail**: If main keyword is crowded, target the highest-volume related term with lower competition --- ## Output Format Present the full analysis in this structure: ``` ## 🔍 赛道分析：{seed_keyword} ### 赛道全貌 - **关键词组**: [seed] + [related1], [related2], ... (数据来源: SellerSprite/推断) - **价格带**: $X–$Y，主力区间 $A–$B - **市场容量估算**: 约 X 万月销量（基于月销标信号） ### 📊 SERP 竞品概览（前10有机结果） | 排名 | ASIN | 标题(简) | 价格 | 评分 | 评论数 | 月销 | 徽章 | |------|------|---------|------|------|--------|------|------| ... ### ⚔️ 竞争强度评分：X/9 — 🟢/🟡/🔴 | 维度 | 得分 | 说明 | |------|------|------| | 评论壁垒 | | 平均 X 条 | | 品牌集中度 | | X 个独立品牌 | | 需求热度 | | X/10 产品有月销标 | | Listing 质量差距 | | 头部均分 X/100 | | 价格带健康度 | | 区间比 X | ### 🔥 赛道热度 - 需求水平: High/Med/Low - 季节性: 是/否 - 趋势信号: ... ### 🎯 结论 **裁定**: 🟢 GO / 🟡 有条件进入 / 🔴 NO-GO **最佳切入点**: ... **建议策略**: 1. ... 2. ... **风险提示**: ... ``` --- ## Data Source Labels Always label where each data point comes from: - `[omni-scraper]` — from SERP/PDP scrape - `[SellerSprite]` — from SellerSprite MCP - `[ClickHouse]` — from brand analytics DB - `[inferred]` — LLM reasoning from available data If a tool was unavailable, explicitly state: "SellerSprite not available — keyword volume data omitted."

Amazon Serp Analysis

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