Amazon Competitor Intelligence Monitor

SKILL.md

---
name: Amazon Competitor Intelligence Monitor
version: 1.1.1
description: >
  Deep competitor intelligence for Amazon sellers with continuous monitoring.
  Two modes: Full Scan (complete analysis, 28-35 credits) and Quick Check (lightweight monitoring, 5-10 credits).
  Full Scan: 11 endpoints, competitor matrix, brand ranking, pricing, reviews, battle strategy.
  Quick Check: realtime/product polling, baseline diff, tiered alerts.
  Use when user asks about: competitor analysis, competitive landscape, competitor tracking,
  competitor monitoring, competitive intelligence, competitor comparison, benchmark, track competitor,
  spy on competitors, competitor analysis, competitor monitoring, competitor tracking.
  Requires APICLAW_API_KEY.
author: SerendipityOneInc
homepage: https://github.com/SerendipityOneInc/APIClaw-Skills
metadata: {"openclaw": {"requires": {"env": ["APICLAW_API_KEY"]}, "primaryEnv": "APICLAW_API_KEY"}}
---

# APIClaw — Competitor Intelligence Monitor

> 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](https://apiclaw.io/en/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)

1. **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
2. **All keyword-based endpoints MUST include `--category`**; ASIN-specific endpoints do NOT need it
3. **Brand + category**: a brand sells across categories — only analyze within locked subcategory
4. **Use API fields directly**: revenue=`sampleAvgMonthlyRevenue` (NEVER price×sales), sales=`monthlySalesFloor`, concentration=`sampleTop10BrandSalesRate`
5. **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

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

## Quick Check Flow

1. Load config.json + baseline.json from `{skill_base_dir}/monitor-data/` (missing → fall back to Full Scan)
2. Poll `product --asin {asin}` for each tracked ASIN
3. 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).