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Google Merchant Center Framework

作者 Luis Calderon · GitHub ↗ · v1.0.0 · MIT-0
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在 OpenClaw 中安装
/install google-merchant-center-framework
功能描述
Google Merchant Center analysis and optimization framework. Use when the user asks about Shopping feed health, product disapprovals, title optimization, pric...
使用说明 (SKILL.md)

Google Merchant Center Framework

Diagnose feed health, optimize product listings, resolve disapprovals, analyze price competitiveness, and assess Shopping campaign readiness. A decision-framework for agents working with Google Merchant Center accounts of any size.

How This Skill Works

Step 1: Collect context from the user's message — merchant category, product count, known issues, goals (fix disapprovals, improve performance, launch Shopping ads, etc.).

Step 2: Ask one follow-up with all remaining questions using multiple-choice format. Key questions:

  • a) Account size: (1) \x3C500 SKUs, (2) 500-10K, (3) 10K-100K, (4) 100K+
  • b) Primary goal: (1) Fix disapprovals, (2) Improve feed quality, (3) Boost Shopping performance, (4) Launch new campaign, (5) General audit
  • c) Feed method: (1) Direct API, (2) Scheduled fetch (XML/CSV), (3) Shopify/WooCommerce plugin, (4) Supplemental feeds, (5) Not sure
  • d) Current disapproval rate: (1) \x3C2%, (2) 2-10%, (3) 10-25%, (4) >25%, (5) Unknown

Allow shorthand answers (e.g., "1a 2c 3b 4e").

Step 3: Analyze using the frameworks below. Prioritize by impact: disapprovals first (products not showing), then data quality (products showing poorly), then performance (products showing but underperforming).

Step 4: Deliver structured output with specific fixes, benchmarks, and priority order.

Integration Note

This skill works best when paired with a Google Merchant Center MCP for live data access. Without live data, the agent applies frameworks to data the user provides (exports, screenshots, descriptions of issues). With live data, the agent can pull product statuses, diagnostics, and price insights directly.


1. Feed Health Analysis

Health Score Framework

Calculate a feed health score from these weighted components:

Component Weight How to Score
Approval rate 30% 100% approved = 100 pts. Deduct 2 pts per % disapproved
Required attribute completeness 25% % of products with all required attrs filled
Recommended attribute completeness 15% % of products with 8+ of 12 recommended attrs
Data freshness 15% Updated \x3C24h = 100, 24-48h = 70, 48-72h = 40, >72h = 0
Image quality 15% % of products with compliant images (no watermarks, no placeholders, >100x100px)

Score interpretation:

  • 90-100: Excellent. Focus on performance optimization.
  • 75-89: Good. Fix remaining disapprovals and fill attribute gaps.
  • 50-74: Needs work. Systematic issues present. Prioritize disapprovals, then required attributes.
  • \x3C50: Critical. Feed is substantially broken. Triage disapprovals by volume.

Required vs Recommended Attributes

Always required (all products):

  • id, title, description, link, image_link, availability, price

Required for most categories:

  • brand (required except for movies, books, musical recordings)
  • gtin (required for all products with a manufacturer-assigned GTIN)
  • condition (required if used or refurbished)

Conditionally required:

  • color, size, gender, age_group — required for Apparel & Accessories
  • item_group_id — required for product variants
  • shipping — required if not set at account level
  • tax — required in the US if not set at account level

High-impact recommended attributes:

  • additional_image_link (up to 10 images; products with 3+ images get ~15% higher CTR)
  • product_type (your own categorization; helps with campaign structure)
  • sale_price + sale_price_effective_date (triggers sale badge in Shopping)
  • custom_label_0 through custom_label_4 (essential for campaign segmentation)
  • product_highlight (up to 10 bullet points; shown in free listings)
  • product_detail (section_name + attribute_name + attribute_value tuples)

Diagnostic Priority Order

  1. Account-level suspensions — nothing shows until resolved
  2. Item-level disapprovals — products removed from serving
  3. Item-level warnings — products serve but with reduced visibility
  4. Missing required attributes — will become disapprovals
  5. Missing recommended attributes — reduces competitiveness
  6. Data quality issues — poor titles, descriptions, categorization

2. Product Optimization

Title Optimization

Titles are the single highest-impact attribute for Shopping performance. Google uses titles for query matching more heavily than descriptions.

Structure formula by category:

Category Title Formula Example
Apparel Brand + Gender + Product Type + Attributes (Color, Size, Material) "Nike Women's Air Max 270 Running Shoes - Black/White, Size 8"
Electronics Brand + Product Line + Model + Key Specs "Samsung Galaxy S24 Ultra 256GB Titanium Black Unlocked"
Home & Garden Brand + Product Type + Material + Key Dimensions + Color "KitchenAid Classic 4.5-Quart Tilt-Head Stand Mixer Onyx Black"
Beauty Brand + Product Line + Product Type + Size/Count + Variant "CeraVe Moisturizing Cream Face and Body 19oz Fragrance-Free"
Grocery Brand + Product Name + Flavor/Variant + Size/Count + Pack "KIND Bars Dark Chocolate Nuts & Sea Salt 12-Count Box"

Title rules:

  • Max 150 characters; first 70 characters are most critical (truncation in mobile)
  • Front-load the most important keywords in the first 70 characters
  • Never use ALL CAPS or promotional text ("Free Shipping", "Best Price")
  • Include color, size, material when relevant — these are matching signals
  • Use the actual brand name, not abbreviations
  • Separate attributes with hyphens or commas, not pipes

Title quality scoring:

Signal Points
Brand name present and correct +20
Product type keyword included +20
Key differentiating attribute (color/size/material) +15 each, max +30
Within 70-character sweet spot +15
No promotional language +15
Total possible 100

Description Optimization

  • 1,000-5,000 characters optimal (minimum 500)
  • First 150-200 characters most critical (may be shown in free listings)
  • Include keywords naturally — Google does use descriptions for matching, but less than titles
  • Include specifications, materials, dimensions, use cases
  • No HTML tags (stripped by Google), no promotional language, no links

Image Requirements

Requirement Standard Notes
Minimum resolution 100x100px (250x250 for apparel) 800x800+ recommended for zoom
Max file size 16MB
Format JPEG, PNG, GIF, BMP, TIFF, WebP
Background White or transparent preferred Non-white OK but clean background required
Watermarks Not allowed Automatic disapproval
Text overlay Not allowed on main image OK on additional images
Promotional overlay Not allowed "Sale", "Free shipping" overlays = disapproval
Product visibility Product must fill 75-90% of frame No tiny product in large frame

Image optimization checklist:

  • Main image: product only, white background, high resolution
  • 3+ additional images (lifestyle, different angles, scale/size reference)
  • Apparel: show product on a person or flat-lay (not hanger)
  • Consistent image style across product line

GTIN/MPN Completeness

  • GTIN is critical. Products with valid GTINs get ~20% more impressions than those without.
  • Google cross-references GTINs against the GS1 database. Invalid GTINs cause disapprovals.
  • If a product genuinely has no GTIN (custom, handmade, vintage, parts), set identifier_exists to no.
  • Never fabricate GTINs. Never reuse GTINs across different products.
  • MPN is required when GTIN is not available and the product has a manufacturer part number.

3. Price Competitiveness

Interpreting Price Benchmarks

Google provides price benchmarks in the Price Competitiveness report. Key metrics:

Metric What It Means Action Threshold
benchmark_price Median price of the same product from other merchants If your price >15% above: expect significantly lower CTR
price_difference_percentage Your price vs benchmark >10% above: review pricing. >20% above: likely suppressed
country_code Market the benchmark applies to Compare only within same market

Competitive Visibility Score Framework

Position Your Price vs Benchmark Expected Impact
Strong >10% below benchmark High impressions, high CTR. Verify margin is acceptable.
Competitive Within +/-10% of benchmark Normal serving. Optimize other signals.
Weak 10-20% above benchmark Reduced impressions. Consider sale_price or promotions.
Uncompetitive >20% above benchmark Severely reduced serving. Re-evaluate pricing or exclude from Shopping.

Price Strategy Decisions

When price is too high vs benchmark:

  1. Can you lower the price? Do it.
  2. Can you add a promotion or sale_price? Triggers sale badge, improves CTR even if final price is still above benchmark.
  3. Can you differentiate on value (bundle, warranty, fast shipping)? Add to title and description.
  4. Is the product a loss leader for competitors? Consider excluding from Shopping.
  5. Use custom_label to segment high-priced items into separate campaigns with different ROAS targets.

Price-Landing Page Match — #1 cause of avoidable disapprovals:

  • Price shown on page must match price attribute exactly (including currency)
  • sale_price must match the visible sale price on the landing page
  • If microdata/structured data on the page differs from the feed, the feed value may be overridden or flagged

4. Performance Analysis

Shopping Performance Benchmarks by Category

Category Median CTR Good CTR Median CPC (Shopping Ads) Median Conv. Rate
Apparel & Accessories 1.2% >2.0% $0.40-0.70 1.5-2.5%
Electronics 0.8% >1.5% $0.50-1.00 1.0-2.0%
Home & Garden 1.0% >1.8% $0.35-0.65 1.5-3.0%
Health & Beauty 1.5% >2.5% $0.30-0.55 2.0-3.5%
Grocery & Food 1.8% >3.0% $0.20-0.40 2.5-4.0%
Toys & Games 1.3% >2.2% $0.25-0.50 2.0-3.5%
Sports & Outdoors 1.0% >1.7% $0.35-0.65 1.5-2.5%
Auto Parts 0.7% >1.2% $0.40-0.80 1.0-2.0%

Performance Diagnostic Tree

Low impressions:

  1. Check disapprovals — products not serving at all?
  2. Check price competitiveness — priced out of auctions?
  3. Check title relevance — titles match search queries?
  4. Check bid/budget (paid) — budget exhausting before end of day?
  5. Check product ratings — competitors with ratings outranking you?

Low CTR (impressions OK):

  1. Image quality — compelling vs generic?
  2. Title quality — informative vs vague?
  3. Price position — higher than competitors in same carousel?
  4. Sale badge — competitors showing sales, you are not?
  5. Shipping speed/cost — competitors showing "Free delivery" and you are not?

Low conversion rate (clicks OK):

  1. Landing page match — does the page show what the listing promised?
  2. Landing page speed — >3 seconds load time loses ~50% of mobile users
  3. Price on landing page — any surprise fees or shipping costs?
  4. Mobile experience — is the landing page mobile-optimized?
  5. Availability — "Add to cart" immediately visible?

Key Metrics to Track Weekly

Metric Red Flag
Disapproval rate >5% or increasing trend
Impression share (paid) \x3C50% in target categories
CTR Below category median
Click-to-conversion rate \x3C1% for most categories
Price competitiveness % >30% of products uncompetitive

5. Disapproval Workflows

Top 10 Disapproval Reasons and Fix Paths

# Reason Cause Fix Timeline
1 Misrepresentation Misleading claims, missing business info Remove superlatives, add return policy, add About Us page 3-7 days, may need manual review
2 Price Mismatch Feed price ≠ landing page price Sync feed price to page exactly; check currency 24-72h re-crawl
3 Availability Mismatch Feed says in stock, page says out of stock Sync inventory feed; increase update frequency Next crawl
4 Missing GTIN Has manufacturer GTIN but not in feed Add GTIN or set identifier_exists: no Immediate on next processing
5 Image Overlay Watermarks, promo text on main image Replace with clean product image 24-72h re-crawl
6 Landing Page Error 404/500 errors, geo-blocking Googlebot Fix URLs; unblock Googlebot Next crawl
7 Insufficient Product Data Title/description too short or generic Title >30 chars with brand+type; description >500 chars Immediate
8 Missing Shipping Weight Carrier-calculated rates but no weight in feed Add shipping_weight or switch to flat-rate Immediate
9 Restricted Product Regulated category without verification Verify compliance; apply for merchant verification if required Varies
10 Duplicate Item ID Same ID reused for different products Assign unique, stable IDs per variant Immediate

Disapproval Triage Matrix

Volume Same Reason? Action
>100 products Yes Systematic root cause — fix feed template or feed rule, not individual items
>100 products No Group by reason, fix highest-volume first
10-100 products Fix in bulk via supplemental feed or feed rules
\x3C10 products Fix individually; watch for pattern

Appeal Process

  1. Fix the issue first. Never appeal without fixing.
  2. Wait 24-72 hours for automatic re-crawl.
  3. If not resolved: Request manual review (Diagnostics > Item Issues > Request Review).
  4. One appeal at a time. 7-day cooldown applies for repeated policy violations.

6. Feed Management

Feed Method Decision Tree

Platform has native GMC integration (Shopify, WooCommerce)?
  YES → Use platform plugin as primary feed
        Need to override attributes the plugin doesn't support?
          YES → Add supplemental feed for overrides
          NO  → Plugin-only is sufficient
  NO  → Have developer resources?
        YES → Content API (real-time; best for >10K SKUs or fast inventory)
        NO  → Scheduled fetch (XML/CSV hosted on your server; update at least daily)

Feed Types

Feed Type Use Case Update Frequency
Primary feed All products, all required attributes Daily minimum; every 6h for fast-moving inventory
Supplemental feed Override/add attributes without touching primary As needed
Feed rules Transform data at processing time (regex, lookups) Applied every processing run
Content API Real-time individual product updates Real-time; 100K requests/day limit
Automated feeds Google crawls your site Google's schedule (not your control)

Supplemental Feed Strategy

Use supplemental feeds for:

  • Custom labels — campaign segmentation (margin tiers, seasonality, priority)
  • Title A/B testing — optimized titles without touching your CMS
  • Missing GTINs — lookup table mapped to product IDs
  • Promotion IDs — linking products to active promotions
  • Seasonal overrides — holiday titles, temporary attribute changes

Supplemental feeds match on id — must exactly match the primary feed id.


7. Shopping Campaign Readiness

Pre-Launch Checklist

Check Requirement
Feed health score >80 (Section 1 framework)
Disapproval rate \x3C5% of active products
Price competitiveness >60% of products within +10% of benchmark
Landing page speed \x3C3 seconds, mobile-friendly
Conversion tracking Google Ads tag firing, revenue tracking verified
Shipping info Accurate in feed or account settings

Standard Shopping vs Performance Max

Factor Standard Shopping Performance Max
Query control High (negatives, search term reports) Low (limited insights)
Bidding Manual CPC or Target ROAS Automated only
Best for Tight ROAS targets, query-level strategy Broad reach, new advertisers, >30 conv/month
Min daily budget $5-10/day viable $50-100/day recommended
Ramp-up Immediate 2-4 week learning phase

Custom Label Strategy

Slot Use Example Values
custom_label_0 Margin tier high_margin, medium_margin, low_margin
custom_label_1 Product priority hero, standard, long_tail
custom_label_2 Seasonality evergreen, summer, holiday, clearance
custom_label_3 Price range under_25, 25_to_100, over_100
custom_label_4 Performance tier top_seller, average, underperformer, new

ROAS Targets by Category (Starting Points)

Category Conservative ROAS Aggressive ROAS
Apparel 400-600% 200-300%
Electronics 600-800% 300-500%
Home & Garden 400-600% 200-400%
Health & Beauty 300-500% 150-300%
Grocery 200-400% 100-200%

Output Format

  • Start with a Feed Health Score (0-100) and top-line summary
  • Group findings into: Critical (disapprovals, policy) → High (data quality, price) → Medium (optimization) → Low (nice-to-have)
  • Include specific product counts and percentages per issue
  • Provide exact fix instructions, not general advice
  • Mark estimates with ⚠️ when based on incomplete data
  • End with a prioritized action plan (numbered, with effort: 5min / 30min / 2hrs / half-day)
  • Include a 7-day and 30-day check-in plan
安全使用建议
This skill is an offline decision framework for Google Merchant Center and appears coherent. Before using it with live data, connect to your Merchant Center only via the platform's official OAuth/connector (do not paste long account credentials into chat). Prefer least-privilege access scopes, and avoid pasting full feeds or customer PII in chat; instead provide redacted exports or allow the platform connector to fetch data. If you intend the agent to act on your GMC account, verify which scopes/actions it will be granted and revoke access when finished.
功能分析
Type: OpenClaw Skill Name: google-merchant-center-framework Version: 1.0.0 The google-merchant-center-framework skill bundle is a comprehensive diagnostic and optimization guide for AI agents managing Google Merchant Center accounts. The SKILL.md file contains detailed frameworks for calculating feed health scores, optimizing product titles by category, and triaging disapprovals based on industry standards. No malicious code, data exfiltration instructions, or harmful prompt injections were identified; the content is entirely aligned with its stated purpose of e-commerce feed management.
能力评估
Purpose & Capability
The name/description (GMC analysis & optimization) matches the SKILL.md content: questions to collect context, diagnostic frameworks, scoring, and optimization guidance. There are no unrelated requested binaries, env vars, or config paths.
Instruction Scope
Runtime instructions are limited to collecting user-provided context, asking follow-ups, applying diagnostic frameworks, and recommending fixes. The doc notes optional live access via a Google Merchant Center connection but does not instruct the agent to read local files, environment variables, or send data to unexpected endpoints.
Install Mechanism
No install spec and no code files — this is instruction-only, so nothing is downloaded, written to disk, or executed by the skill itself.
Credentials
The skill declares no required environment variables, no primary credential, and no config paths. The Integration Note reasonably references using a Merchant Center connection for live data; that implies credentials would be supplied by whatever MCP integration the platform provides, but the skill itself does not request unrelated secrets.
Persistence & Privilege
always is false and there is no install behavior that modifies agent config or requests persistent presence. Normal autonomous invocation is allowed by platform defaults but the skill does not escalate privileges.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install google-merchant-center-framework
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /google-merchant-center-framework 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release: feed health scoring, product optimization, price competitiveness, disapproval workflows, campaign readiness frameworks
元数据
Slug google-merchant-center-framework
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Google Merchant Center Framework 是什么?

Google Merchant Center analysis and optimization framework. Use when the user asks about Shopping feed health, product disapprovals, title optimization, pric... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 111 次。

如何安装 Google Merchant Center Framework?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install google-merchant-center-framework」即可一键安装,无需额外配置。

Google Merchant Center Framework 是免费的吗?

是的,Google Merchant Center Framework 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Google Merchant Center Framework 支持哪些平台?

Google Merchant Center Framework 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Google Merchant Center Framework?

由 Luis Calderon(@lac5q)开发并维护,当前版本 v1.0.0。

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