Product Title Booster

v1.0.0

Optimizes e-commerce product titles for Taobao, JD, Pinduoduo, Amazon, and Shopify using platform-specific rules to improve search ranking and conversion.

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byhaidong@harrylabsj

Install

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Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Product Title Booster" (harrylabsj/product-title-booster) from ClawHub.
Skill page: https://clawhub.ai/harrylabsj/product-title-booster
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

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openclaw skills install product-title-booster

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npx clawhub@latest install product-title-booster
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Purpose & Capability
The name, description, skill.json, README, and SKILL.md all describe e-commerce title optimization for the same five platforms and the templates/outputs align with that purpose. The skill does not request unrelated credentials, binaries, or system access.
Instruction Scope
Runtime instructions stay focused on extracting keywords from provided product/competitor titles, applying platform constraints, generating titles/A-B variants, and scoring. One minor mismatch: SKILL.md repeatedly says to "verify proposed titles against platform-specific restricted term lists" but the skill bundle provides no restricted-term data and skill.json claims no_network — so the agent cannot automatically fetch up-to-date platform lists unless it already has that knowledge. This is an operational gap rather than an obvious malicious behavior.
Install Mechanism
Instruction-only skill with no install spec and no code files. Nothing is downloaded or written to disk by the skill package itself.
Credentials
No environment variables, credentials, or config paths are requested. The inputs described (product details, competitor titles) are consistent with the stated functionality.
Persistence & Privilege
The skill is not always-on and does not request elevated or persistent privileges. Default autonomous invocation is enabled (platform default) but that is expected for an agent skill.
Assessment
This skill appears coherent and limited in scope: it asks for product and (optionally) competitor titles and returns optimized titles/variants and scores. Before installing or using it, consider: (1) The SKILL.md asks the agent to "verify against platform-specific restricted term lists" but the package supplies no lists and claims no network access — if you need legally accurate or up-to-date policy checks, perform a manual verification against each platform's current rules before publishing. (2) When providing competitor titles, avoid sending sensitive internal data or full competitor documentation; the extractor only needs public listing titles. (3) Treat generated titles as suggestions — validate claims, certifications, and trademark usage yourself to avoid policy or legal issues. If the skill later included network calls, downloads, or requested API keys, re-evaluate as that would materially change the risk profile.

Like a lobster shell, security has layers — review code before you run it.

latestvk973hw1hm9fx9jnty22pqkt2b185n39j
34downloads
0stars
1versions
Updated 18h ago
v1.0.0
MIT-0

Product Title & Selling-Point Booster

Purpose

This skill optimizes e-commerce product titles for search visibility and conversion across five major platforms: Taobao (淘宝), JD (京东), Pinduoduo (拼多多), Amazon, and Shopify/independent stores. It applies platform-specific constraints — character limits, keyword positioning rules, and formatting conventions — to extract high-intent keywords and craft titles that rank better and convert more clicks. "Booster" signals immediate, measurable listing improvement.

Triggers

  • "优化商品标题"
  • "生成淘宝标题"
  • "Amazon title optimizer"
  • "product title booster"
  • "标题优化"
  • "listing title"
  • "电商标题"
  • "title A/B test"
  • "多平台标题"
  • "标题评分"

Workflow

  1. Receive product details from user: product name, brand, category, key attributes (material, size, color, function), and target platform(s).
  2. Mine relevant keywords from product attributes: core product term, modifier keywords (material, style, season), scenario keywords, and audience keywords.
  3. Apply platform-specific constraints:
    • Taobao: 60 characters max, keyword-stacking style, core term early
    • JD: Brand first, spec-dense, model numbers prominent
    • PDD: Value/price keywords prominent, benefit language
    • Amazon: 200 characters max, no promotional language, backend search terms separate
    • Shopify: SEO-optimized, H1-friendly, conversion-focused
  4. Generate optimized title(s) that pack maximum search value within constraints.
  5. Create A/B variant suggestions with rationale explaining why each variant may perform differently.
  6. Score the original/optimized title and explain each optimization choice.

Prompt Templates

1. Title from Product Info (title_from_product_info)

Purpose: Generate an optimized title from raw product details. Input:

  • ${brand} — Brand name
  • ${product_type} — Core product term
  • ${key_attributes} — Material, size, color, function, style
  • ${target_platform} — Platform name
  • ${current_title} — (Optional) Existing title to improve

Output: Optimized title + character count + keyword analysis table showing which keywords were included and why.

2. Multi-Platform Title Pack (multi_platform_title_pack)

Purpose: Generate titles for 5 platforms from one product. Input:

  • ${product_details} — Same as above
  • ${platforms} — List of target platforms

Output: Title per platform, each with character count and platform-specific optimization notes.

3. Title A/B Variants (title_ab_variants)

Purpose: Generate 3 alternative titles with rationale. Input:

  • ${current_title} — Current title
  • ${hypothesis} — What to test (keyword order, emotional appeal, specificity)

Output: 3 variant titles, each with: variant title, character count, hypothesis tested, expected click/ranking impact.

4. Keyword Extractor (keyword_extractor)

Purpose: Mine keywords from competitor titles for strategy. Input:

  • ${competitor_titles} — 3–5 competitor listing titles
  • ${target_platform} — Platform context

Output: Keyword frequency table, gap analysis (what competitors use that you don't), and suggested keyword additions.

5. Title Grader (title_grader)

Purpose: Score a title and suggest improvements. Input:

  • ${title} — Title to evaluate
  • ${platform} — Platform rules apply

Output: Score out of 100 + breakdown by dimension (keyword coverage, readability, platform compliance, conversion appeal) and specific improvement suggestions.

Output Format

Titles are delivered with:

  • Optimized title (bolded)
  • Character count (with platform limit noted)
  • Keyword analysis table: Keyword | Search Intent | Position | Reason
  • A/B variants (when requested): Variant | Hypothesis | Expected Impact

Safety Rules

  • NEVER stuff keywords in a way that violates specific platform listing policies
  • NEVER include trademarked competitor brand names in titles
  • NEVER make misleading claims about product attributes, materials, or certifications
  • ALWAYS verify proposed titles against platform-specific restricted term lists
  • ALWAYS remind user to check platform's latest title guidelines (policies change)

Examples

Example 1: Taobao Title Optimization

Input: Brand="XX", Type="真丝连衣裙", Attributes="中长款、修身、2024新款、桑蚕丝", Platform="Taobao" Output: "XX2024新款桑蚕丝真丝连衣裙女中长款修身显瘦高级感气质" (38 chars / 60 limit) with keyword analysis.

Example 2: Multi-Platform Pack

Input: Same product, Platforms=[Taobao, Amazon, Shopify] Output: Three titles with different structural approaches: keyword-stacked (Taobao), brand-spec (Amazon), SEO-optimized (Shopify).

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