菜单
Monitor recent market changes and anomalies: SOV shifts, sales movement, review velocity, competitor changes. Triggers: market alert, recent change, SOV change, sales shift. Use for alerts/changes, not full market snapshots; use market-overview for broad analysis.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | ecommerce.market-alert |
| version | 3.0.0 |
| description | Monitor recent market changes and anomalies: SOV shifts, sales movement, review velocity, competitor changes. Triggers: market alert, recent change, SOV change, sales shift. Use for alerts/changes, not full market snapshots; use market-overview for broad analysis. |
| allowed-tools | ["Bash","Read","Write"] |
| metadata | {"requires":{"apis":["nexscope"]}} |
IMPORTANT — Working Directory: All scripts in this skill MUST be executed with the working directory set to the absolute path of the directory containing this SKILL.md file (the skill root directory). Always
cdinto this directory before running any command. Do NOT use relative paths or shell shortcuts like~in paths, as they may not be resolved by the execution environment.
What changed recently in this market, and does it require action?
Before running this skill, if the user provides a broad product category, niche, keyword, market idea, or trend topic, first identify:
Use this market understanding to choose keywords, filters, regions, comparison scope, and analysis dimensions before executing the script.
Are there market changes?
Monitor market changes using Jungle Scout data (International Standard).
# Basic keyword analysis
python3 scripts/market_alert.py '{"keyword": "bluetooth earbuds"}'
# Specific market
python3 scripts/market_alert.py '{"keyword": "yoga mat", "market": "uk"}'
# With charts
python3 scripts/market_alert.py '{"keyword": "bluetooth earbuds"}' --chart /tmp/charts
--chart with an output directory unless the user asks for a quick text-only result or chart data is unavailable.ecommerce_chart_helpers.py which reads from chart_style.json (derived from references/display-rules.md). Do NOT hardcode colors in scripts.| Parameter | Type | Required | Default |
|---|---|---|---|
keyword | string | Yes | - |
market | string | No | |
new_threshold_days | int | No | 90 |
The output will be a structured markdown report, following this format:
Market Monitoring Alert Report: [Keyword]
1. Executive Summary
2. SOV (Share of Voice) Analysis
| Rank | Brand | Revenue SOV% |
|---|---|---|
| 1 | [sov_analysis.brand_ranking[0].brand] | [sov_analysis.brand_ranking[0].revenue_sov_pct]% |
| 2 | [sov_analysis.brand_ranking[1].brand] | [sov_analysis.brand_ranking[1].revenue_sov_pct]% |
| 3 | [sov_analysis.brand_ranking[2].brand] | [sov_analysis.brand_ranking[2].revenue_sov_pct]% |
| (Listing more brands if available) |
3. Market Alerts
| Alert Type | Severity | Trigger Condition |
|---|---|---|
| [alert.type] | [alert.severity] | [alert.trigger] |
| ... | ... | ... |
| (Listing all identified alerts) |
4. Attached Visualizations
| Source | Endpoint | Data |
|---|---|---|
| Amazon Search (SerpApi) | /api/v1/tools/research/amazon/search | Product list ranked by relevance, monthly sales |
| Jungle Scout | /api/v1/tools/jungle-scout/keywords/by-keyword | Search volume, keyword trends |
| Keepa | /api/v1/tools/research/keepa/productRequest | availableDate for new competitor detection |
| Jungle Scout (fallback) | /api/v1/tools/jungle-scout/product-database/query | Fallback if Amazon Search fails |
Note: v3.0 uses Amazon Search as primary product source (real search results) instead of
Jungle Scout product-database (which returns random keyword-matched products, not ranked results).
Keepa enriches with availableDate (batch, single API call for up to 50 ASINs) to enable new competitor detection.
v3.0 uses real revenue for SOV:
Brand SOV = Brand's 30-day Revenue / Total Market Revenue × 100%
This is more accurate than exposure-based SOV because:
| Alert | Severity | Trigger |
|---|---|---|
| ⚡ Fast Riser | HIGH | New product with $1k+/mo revenue |
| 🆕 New Competitors | HIGH | ≥20% new products |
| 📢 SOV Shift | HIGH | New brands ≥15% revenue share |
| 🎯 Tier Concentration | MEDIUM | ≥40% new in one tier |
| 👑 Market Concentration | INFO | Top 3 brands ≥70% |
| 💡 Price Vacuum | INFO | Empty price range |
| Chart | Description | File |
|---|---|---|
| SOV | Revenue-based market share | 1_sov.png |
| Price Tiers | Distribution by price | 2_price_tiers.png |
| Composition | New vs Established | 3_composition.png |
| Alerts | Severity breakdown | 4_alerts.png |
NexScope proxy API key is provided via environment variable NEXSCOPE_API_KEY.
Auth header format: Authorization: Bearer {NEXSCOPE_API_KEY}
| Benefit | Description |
|---|---|
| International Recognition | Trusted by sellers worldwide |
| Data Compliance | Official Amazon data partner |
| Real Sales Data | 30-day actual revenue & units |
| Brand Value | Enhances skill credibility |
| Variable | Required | Description |
|---|---|---|
NEXSCOPE_PROXY_BASE | Yes | NexScope proxy base URL |
NEXSCOPE_API_KEY | Yes | NexScope proxy API key |
Compare Amazon competitor business metrics: price, reviews, BSR, sales, seller count, traffic. Triggers: competitor metrics, who performs best, price/BSR/review comparison. Use for business performance, not listing copy; use competitor-listing-analyzer for content structure.
Analyze Amazon listing content and structure: title, bullets, images, A+ content, and listing quality. Triggers: listing breakdown, reverse-engineer listings, title/bullet/image analysis. Use for content audits, not business metrics; use competitor-analyzer for price/BSR/sales.
Validate whether demand is real by comparing search volume with sales and buying intent. Triggers: is demand real, fake demand, inflated volume, are people buying, conversion gap. Use for demand proof, not broad market overview or niche go/no-go scoring.
Check visual/design patent similarity from product images. Triggers: design infringement, look-alike patent, visual similarity, design clearance. Use for appearance/design risk, not utility claims or legal-status checks.
Suggest product differentiation and USP using competitor/review evidence. Triggers: how to stand out, USP, competitive advantage, product improvement. Use for positioning and feature strategy, not raw competitor metrics or review monitoring.
Find visually similar Amazon products from an image. Triggers: reverse image search, find lookalikes, who else sells this, source by image. Use for marketplace lookalikes, not patent similarity or design infringement checks.