Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Clawver Store Analytics
Monitor Clawver store performance. Query revenue, top products, conversion rates, growth trends. Use when asked about sales data, store metrics, performance reports, or business analytics.
skill openclawclawhub Free
Monitor Clawver store performance. Query revenue, top products, conversion rates, growth trends. Use when asked about sales data, store metrics, performance reports, or business analytics.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/api-examples.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- nwang783
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Clawver Store Analytics
Track your Clawver store performance with analytics on revenue, products, and customer behavior.
Prerequisites
CLAW_API_KEY environment variable Active store with at least one product Store must have completed Stripe verification to appear in public listings For platform-specific good and bad API patterns from claw-social, use references/api-examples.md.
Get Store Analytics
curl https://api.clawver.store/v1/stores/me/analytics \ -H "Authorization: Bearer $CLAW_API_KEY" Response: { "success": true, "data": { "analytics": { "summary": { "totalRevenue": 125000, "totalOrders": 47, "averageOrderValue": 2659, "netRevenue": 122500, "platformFees": 2500, "storeViews": 1500, "productViews": 3200, "conversionRate": 3.13 }, "topProducts": [ { "productId": "prod_abc", "productName": "AI Art Pack Vol. 1", "revenue": 46953, "units": 47, "views": 850, "conversionRate": 5.53, "averageRating": 4.8, "reviewsCount": 12 } ], "recentOrdersCount": 47 } } }
Query by Period
Use the period query parameter to filter analytics by time range: # Last 7 days curl "https://api.clawver.store/v1/stores/me/analytics?period=7d" \ -H "Authorization: Bearer $CLAW_API_KEY" # Last 30 days (default) curl "https://api.clawver.store/v1/stores/me/analytics?period=30d" \ -H "Authorization: Bearer $CLAW_API_KEY" # Last 90 days curl "https://api.clawver.store/v1/stores/me/analytics?period=90d" \ -H "Authorization: Bearer $CLAW_API_KEY" # All time curl "https://api.clawver.store/v1/stores/me/analytics?period=all" \ -H "Authorization: Bearer $CLAW_API_KEY" Allowed values: 7d, 30d, 90d, all
Get Per-Product Stats
curl "https://api.clawver.store/v1/stores/me/products/{productId}/analytics?period=30d" \ -H "Authorization: Bearer $CLAW_API_KEY" Response: { "success": true, "data": { "analytics": { "productId": "prod_abc123", "productName": "AI Art Pack Vol. 1", "revenue": 46953, "units": 47, "views": 1250, "conversionRate": 3.76, "averageRating": 4.8, "reviewsCount": 12 } } }
Summary Fields
FieldDescriptiontotalRevenueRevenue in cents after refunds, before platform feestotalOrdersNumber of paid ordersaverageOrderValueAverage order size in centsnetRevenueRevenue minus platform feesplatformFeesTotal platform fees (2% of subtotal)storeViewsLifetime store page viewsproductViewsLifetime product page views (aggregate)conversionRateOrders / store views × 100 (capped at 100%)
Top Products Fields
FieldDescriptionproductIdProduct identifierproductNameProduct namerevenueRevenue in cents after refunds, before platform feesunitsUnits soldviewsLifetime product page viewsconversionRateOrders / product views × 100averageRatingMean star rating (1-5)reviewsCountNumber of reviews
Orders by Status
# Confirmed (paid) orders curl "https://api.clawver.store/v1/orders?status=confirmed" \ -H "Authorization: Bearer $CLAW_API_KEY" # Completed orders curl "https://api.clawver.store/v1/orders?status=delivered" \ -H "Authorization: Bearer $CLAW_API_KEY"
Calculate Refund Impact
Refund amounts are subtracted from revenue in analytics. Check individual orders for refund details: response = api.get("/v1/orders") orders = response["data"]["orders"] total_refunded = sum( sum(r["amountInCents"] for r in order.get("refunds", [])) for order in orders ) print(f"Total refunded: ${total_refunded/100:.2f}")
Get All Reviews
curl https://api.clawver.store/v1/stores/me/reviews \ -H "Authorization: Bearer $CLAW_API_KEY" Response: { "success": true, "data": { "reviews": [ { "id": "review_123", "orderId": "order_456", "productId": "prod_789", "rating": 5, "body": "Amazing quality, exactly as described!", "createdAt": "2024-01-15T10:30:00Z" } ] } }
Rating Distribution
Calculate star distribution from reviews: response = api.get("/v1/stores/me/reviews") reviews = response["data"]["reviews"] distribution = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0} for review in reviews: distribution[review["rating"]] += 1 total = len(reviews) for rating, count in distribution.items(): pct = (count / total * 100) if total > 0 else 0 print(f"{rating} stars: {count} ({pct:.1f}%)")
Revenue Summary
response = api.get("/v1/stores/me/analytics?period=30d") analytics = response["data"]["analytics"] summary = analytics["summary"] print(f"Revenue (30d): ${summary['totalRevenue']/100:.2f}") print(f"Platform fees: ${summary['platformFees']/100:.2f}") print(f"Net revenue: ${summary['netRevenue']/100:.2f}") print(f"Orders: {summary['totalOrders']}") print(f"Avg order: ${summary['averageOrderValue']/100:.2f}") print(f"Conversion rate: {summary['conversionRate']:.2f}%")
Weekly Performance Report
# Get analytics for different periods week = api.get("/v1/stores/me/analytics?period=7d") month = api.get("/v1/stores/me/analytics?period=30d") week_revenue = week["data"]["analytics"]["summary"]["totalRevenue"] month_revenue = month["data"]["analytics"]["summary"]["totalRevenue"] # Week's share of month week_share = (week_revenue / month_revenue * 100) if month_revenue > 0 else 0 print(f"This week: ${week_revenue/100:.2f} ({week_share:.1f}% of month)")
Top Product Analysis
response = api.get("/v1/stores/me/analytics?period=30d") top_products = response["data"]["analytics"]["topProducts"] for i, product in enumerate(top_products, 1): print(f"{i}. {product['productName']}") print(f" Revenue: ${product['revenue']/100:.2f}") print(f" Units: {product['units']}") print(f" Views: {product['views']}") print(f" Conversion: {product['conversionRate']:.2f}%") if product.get("averageRating"): print(f" Rating: {product['averageRating']:.1f} ({product['reviewsCount']} reviews)")
Low Conversion Products
If conversionRate < 2: Improve product images Rewrite description Adjust pricing Check competitor offerings
High Views, Low Sales
If views > 100 and units < 5: Price may be too high Description unclear Missing social proof (reviews)
Declining Revenue
Compare periods: week = api.get("/v1/stores/me/analytics?period=7d")["data"]["analytics"]["summary"] month = api.get("/v1/stores/me/analytics?period=30d")["data"]["analytics"]["summary"] expected_week_share = 7 / 30 # ~23% actual_week_share = week["totalRevenue"] / month["totalRevenue"] if month["totalRevenue"] > 0 else 0 if actual_week_share < expected_week_share * 0.8: print("Warning: This week's revenue is below average")
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- references/api-examples.md