📦 Amazon Competitor Intelligence Monitor — 技能工具

Know your enemy. Two modes: Full Scan + Quick Check. Respond in user's language.

Files

File	Purpose
{skill_base_dir}/scripts/apiclaw.py	Execute for all API calls (run --help for params)
{skill_base_dir}/references/reference.md	Load for exact field names or response structure
{skill_base_dir}/monitor-data/	Runtime storage (auto-created): config.json, baseline.json, history/, alerts.json

Credential

Required: APICLAW_API_KEY. Get free key at apiclaw.io/api-keys.

Input

Required: keyword or ASIN(s). Optional: my_asin, competitor_asins, brand. If only ASIN given → derive keyword via product --asin then ask user to confirm. Brand queries MUST also include confirmed --category.

API Pitfalls (CRITICAL)

• Category auto-detection: categoryPath is auto-detected from keyword, ASIN, or top search result. If category_source in output is inferred_from_search, MUST confirm with user before trusting results
• All keyword-based endpoints MUST include --category; ASIN-specific endpoints do NOT need it
• Brand + category: a brand sells across categories — only analyze within locked subcategory
• Use API fields directly: revenue=sampleAvgMonthlyRevenue (NEVER price×sales), sales=monthlySalesFloor, concentration=sampleTop10BrandSalesRate
• reviews/analysis: needs 50+ reviews; fallback to ratingBreakdown from realtime/product

Mode Selection

• Full Scan (~28-35 credits): First run, no baseline.json, explicit request, or weekly refresh
• Quick Check (~5-10 credits): Cron trigger, baseline exists, "check competitors"

Full Scan Flow

• competitor-analysis --keyword X [--category Y] [--my-asin Z] (composite, auto-detects category)
• If category_source is inferred_from_search, confirm with user before presenting results
• Analyze & score → save baseline to {skill_base_dir}/monitor-data/ → offer Auto-Monitor

Quick Check Flow

• Load config.json + baseline.json from {skill_base_dir}/monitor-data/ (missing → fall back to Full Scan)
• Poll product --asin {asin} for each tracked ASIN
• Diff against baseline with tiered alerts → update baseline → offer Auto-Monitor

Alert Tiers

🔴 Critical	🟡 Watch	🟢 Opportunity
Price change > threshold	FBA↔FBM switch	Competitor stock-out
BSR crash > threshold	Rating change	Bullet/image changes
Buy Box owner changed	Abnormal review growth	Variant added/removed
Title modified

Competitive Score (per competitor, 1-100)

Dimension	Weight	80-100 (Strong)	50-79 (Moderate)	0-49 (Weak)
Sales Dominance	25%	Top 3 in category, >5K units/mo 📊	Top 20, 1K-5K units/mo 📊	Below Top 20, <1K units/mo 📊
Brand Strength	20%	Brand in CR10, 5+ SKUs, wide price range 📊	Known brand, 2-4 SKUs 📊	Unknown brand, single SKU 📊
Listing Quality	20%	7+ images, 5 bullets, A+, optimized title 📊	5-6 images, basic bullets 📊	<5 images, weak bullets, no A+ 📊
Customer Satisfaction	20%	Rating ≥4.5, <3% 1-star, positive sentiment 📊	4.0-4.4, 3-8% 1-star 📊	<4.0 or >8% 1-star 📊
Trend Momentum	15%	BSR improving 30d, sales growth >10% 🔍	BSR stable, flat sales 🔍	BSR declining, sales drop 🔍

Competitive Threat Level

Total Score	Threat	Interpretation
80-100	🔴 Dominant	Hard to compete head-on; find differentiation or avoid price band 💡
50-79	🟡 Competitive	Beatable with better listing, pricing, or reviews 💡
0-49	🟢 Vulnerable	Weak competitor; opportunity to capture share 💡

Market Structure Analysis

• CR10 > 70%: Concentrated market — new entrants need strong differentiation or niche positioning 🔍
• CR10 40-70%: Moderately competitive — room for well-positioned products 🔍
• CR10 < 40%: Fragmented — opportunity for brand building 🔍
• Top brand share > 25%: Category leader dominance — avoid direct competition in their price band 💡
• New SKU rate > 15%: Active market with frequent new entrants 📊
• New SKU rate < 5%: Mature/stagnant market, high barriers 🔍

Auto-Monitor Prompt

After EVERY run, offer: "Set up automatic monitoring? I can generate a scheduled Quick Check." Provide platform-specific setup (OpenClaw /cron, ChatGPT Scheduled Tasks, Claude Projects).

Output Spec

Full Scan sections: Battlefield Overview → Competitor Matrix → Brand Power Ranking → Price Map → 30-Day Trends → Review Battle → Listing Audit → Competitive Scores → Battle Strategy → Data Provenance → API Usage.

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.

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

Full Scan: ~28-35 credits (all 11 endpoints via composite). Quick Check: ~5-10 credits (realtime/product × N ASINs).