Turn a product listing optimization request into a structured review brief. Provide the selling platform, product facts, current listing and the problems you have noticed; this Skill's Python handler organizes keyword suggestions, attribute checks and possible conversion obstacles into Markdown that an operator can use to plan the next edits.
This Skill reviews the information you provide rather than querying marketplace rankings. It produces a Markdown brief that brings search wording, missing or weak product attributes and potential purchasing confusion into one place.
Use the brief as a starting point for a manual listing review. Some issues and weights are built-in heuristics, so check each suggestion against the actual page and your platform's requirements. A suggested problem is not proof that the listing has that defect, and the output is not an official platform score.
The Python handler gives the review a repeatable Markdown structure. Teams can bring the current listing and a clear goal, then review keyword, attribute and conversion suggestions together instead of collecting unconnected comments from several people.
Keep the platform explicit and separate verified page issues from suggested checks. Unknown platforms fall back to Amazon and generic weights may be used, so the useful output is a review checklist—not evidence of ranking factors or a prediction of sales improvement.
Platform, product details, current listing, optimization goal, and observed issues.
Use a Skill-capable agent with file access. Python 3 is required for the bundled Python helpers; inspect their sample schemas before supplying data.
No live rankings or seller APIs are used. The handler adds default traps, defaults unknown platforms to Amazon, and falls back to generic attribute weights; these are not measured defects or official ranking weights.
Send this request to the OpenClaw agent that will use the Skill. It installs the named publisher’s release and checks the workspace configuration.
Install @harrylabsj/search-listing-optimizer version 1.0.0 in my active OpenClaw workspace. Use the original ClawHub release, not a same-named substitute. First check whether it is already installed; do not force-overwrite local changes. Run:
openclaw skills install @harrylabsj/search-listing-optimizer --version 1.0.0
openclaw skills info search-listing-optimizer
Read SKILL.md and all setup references, identify required tools, dependencies and credentials, and report the actual version, path and readiness. Start a new session if needed. Do not describe package installation as a successful business-task test.This is the unchanged source SKILL.md for the reviewed version. Install the complete package to retain referenced scripts, assets, and instructions. Starter prompts below are EcomAgentTools examples.
---
name: search-listing-optimizer
description: Optimize ecommerce product listings for Amazon A9, Taobao/1688 search, JD search, TikTok Shop discovery, Xiaohongshu SEO, and Shopify storefront search using keyword density frameworks, attribute-completeness checklists, and conversion-trap detection. Use when sellers need a listing audit, keyword refresh, or conversion improvement brief without live search analytics or platform API access.
---
# Search Listing Optimizer
## Overview
Use this skill to audit a product listing and generate an optimization brief covering keyword targeting, attribute completeness, image metadata, and conversion-trap detection. It applies built-in search-ranking heuristics and best-practice libraries to surface actionable improvements.
This MVP is heuristic. It does **not** connect to live search analytics, A9 algorithms, or platform seller portals. It relies on the user's provided listing notes and product context.
## Trigger
Use this skill when the user wants to:
- audit an Amazon, Taobao, JD, TikTok Shop, Xiaohongshu, or Shopify listing for search visibility
- identify missing keywords, weak attributes, or conversion traps in existing copy
- rebuild a listing keyword strategy for a new product or category entry
- compare listing quality across multiple marketplaces
- generate a before-and-after optimization brief for a product team
### Example prompts
- "Audit our Amazon skincare listing for A9 visibility"
- "Help me optimize our Taobao product listing for crowd-collapse ranking"
- "What's wrong with our JD listing that gets traffic but no conversions?"
- "Create an optimization brief for our new TikTok Shop product"
## Workflow
1. Capture the platform, product context, and optimization goal.
2. Apply keyword-density and search-intent frameworks for the target platform.
3. Assess attribute completeness across key fields.
4. Detect common conversion traps (pricing, images, reviews, description).
5. Return a markdown optimization brief with prioritized action items.
## Inputs
The user can provide any mix of:
- platform: Amazon, Taobao, 1688, JD, TikTok Shop, Xiaohongshu, Shopify, general
- product category: category name, subcategory, price range
- current listing notes: title, bullet points, description, keywords, images, reviews
- optimization goal: visibility boost, conversion improvement, new listing, relaunch
- competitor context: competitor ASINs, titles, or keyword strategies (optional)
## Outputs
Return a markdown brief with:
- platform search algorithm summary (how ranking works on that platform)
- keyword opportunity map (search volume intent × competition)
- listing attribute scorecard (title, bullets, description, backend keywords, images)
- conversion-trap detection (pricing, trust signals, review depth, description clarity)
- prioritized action list with expected impact
- template for revised title, bullets, or description
## Safety
- No live search analytics, ad console, or seller portal access.
- Optimization recommendations are directional; actual ranking depends on platform algorithms.
- Do not claim guaranteed ranking improvements or sales lift.
- Pricing and promotional decisions remain with the operator.
## Best-fit Scenarios
- sellers preparing new listings or refreshing existing ones for better visibility
- teams managing multiple marketplace accounts needing a consistent audit framework
- brands expanding to new platforms and needing a listing best-practice brief
## Not Ideal For
- real-time rank tracking, automated listing updates, or live ad campaign management
- products in highly regulated categories with strict claim restrictions
- very early-stage products with no sales history or review data
## Acceptance Criteria
- Return markdown text.
- Include attribute scorecard, keyword opportunity map, and prioritized action list.
- Make ranking assumptions explicit.
- Keep the brief practical for marketplace sellers and listing specialists.
Starter prompts for the main use cases—copy and use them directly.
Draft a listing audit from supplied Amazon notes Use for: Draft a listing audit from supplied Amazon notes. Prepare: Platform, product details, current listing, optimization goal, and observed issues.
Use the installed search-listing-optimizer Skill. Task: Draft a listing audit from supplied Amazon notes. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. No live rankings or seller APIs are used. The handler adds default traps, defaults unknown platforms to Amazon, and falls back to generic attribute weights; these are not measured defects or official ranking weights.[INPUT][CONSTRAINTS]Build a keyword and attribute review checklist Use for: Build a keyword and attribute review checklist. Prepare: Platform, product details, current listing, optimization goal, and observed issues.
Use the installed search-listing-optimizer Skill. Task: Build a keyword and attribute review checklist. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. No live rankings or seller APIs are used. The handler adds default traps, defaults unknown platforms to Amazon, and falls back to generic attribute weights; these are not measured defects or official ranking weights.[INPUT][CONSTRAINTS]Separate observed conversion issues from default suggestions Use for: Separate observed conversion issues from default suggestions. Prepare: Platform, product details, current listing, optimization goal, and observed issues.
Use the installed search-listing-optimizer Skill. Task: Separate observed conversion issues from default suggestions. Inputs: [INPUT]. Constraints: [CONSTRAINTS]. Check missing information first. Produce a reviewable result with evidence, assumptions, and next steps. No live rankings or seller APIs are used. The handler adds default traps, defaults unknown platforms to Amazon, and falls back to generic attribute weights; these are not measured defects or official ranking weights.[INPUT][CONSTRAINTS]A $24 mug master and existing listing lack capacity, material, care and clear shipping information. Search-related fields and purchase copy need to use consistent product facts.
Case input files: business-inputs.zip
Use the search-listing-optimizer Skill.
Rewrite our Shopify listing so shoppers can judge capacity, care and delivered price before buying. Review the existing title, attributes and copy, identify relevant keyword themes, then deliver a new title, bullets and description with an explanation of each change. Do not invent search volume, ranking weights, reviews or product claims.
The attached business materials are:
- inputs/brief.json
- inputs/brief.txt
Deliver a complete result.md report that the operations team can read, plus any finished files needed for this task. Keep supporting calculations and evidence with the deliverables. Write the report in English; use the requested target language for customer-facing copy. The business datasets are fictional examples unless the brief explicitly names a live public website.The primary file produced by this prompt is shown below in its original language.
Make capacity, care and the shipping-inclusive price visible beside the purchase controls. Keep the $24 product price, but show $29 for one mug including US shipping, before any applicable tax. Two mugs do not qualify for free shipping.
The supplied business data is synthetic; this report contains draft recommendations, not measured market evidence or achieved results. Customer-facing copy is in English, matching the supplied listing and US market.
HarborMug 500 mL Insulated Stainless Steel Mug — Blue
Choose the HarborMug in blue for a 500 mL insulated stainless steel mug. Check the capacity and measurements against the mug you want: dimensions are 9 × 9 × 19 cm and weight is 0.35 kg. No tested insulation retention duration is available.
Care Hand-wash only. Do not microwave.
Price and US shipping The mug costs $24. Shipping is $5 on orders below $50 and free on orders of $50 or more. One mug costs $29 including shipping, before any applicable tax. Two mugs have a $48 merchandise subtotal and cost $53 including shipping, before any applicable tax.
Orders ship within the US only. Dispatch is in 2 business days, with delivery in 3–5 business days. Allow dispatch time in addition to delivery time.
Returns Unused items may be returned within 30 days. Final refund requires merchant approval.
Frequently asked questions How much does it hold? 500 mL. Can I put it in the dishwasher or microwave? Hand-wash only; it is not microwave safe. How long does it keep drinks hot or cold? The mug is insulated, but no tested retention duration is available. Do two mugs qualify for free shipping? No. Two mugs total $48 before shipping; free shipping starts at $50.
| Element | Existing content or gap | Change and purpose |
|---|---|---|
| Title | “HarborMug Blue Mug” | Retains brand and color; adds 500 mL, insulated and stainless steel so shoppers can identify the product and capacity at a glance. |
| Capacity bullet | Only “Insulated stainless steel mug” | Adds the exact 500 mL capacity without inventing serving counts. |
| Size and weight bullet | Absent | Adds the supplied dimensions and weight for fit comparisons. Dimension axes are unspecified, so no diameter or height labels are invented. |
| Care bullet | Absent | Makes hand-washing and microwave restrictions visible before purchase. |
| Price bullet | Description says only “$24” | Separates item price from customer shipping and gives the one-mug total before tax. The merchant’s $4 shipping cost is not the shopper’s $5 shipping charge. |
| Shipping bullet | Absent | States US-only shipping and separates dispatch from delivery; avoids presenting 3–5 days as an order-to-door guarantee. |
| Returns bullet | No visible policy | Adds the unused condition, 30-day window and merchant approval requirement; does not promise an automatic refund. |
| Description opening | “Your everyday mug. $24.” | Replaces generic language with capacity, material, color, measurements and weight. Keeps insulation wording within the supplied claim. |
| Description care and policy sections | Missing | Gives shoppers scannable answers and reinforces the same terms as the bullets. |
| FAQ | Absent | Answers capacity, care, insulation-duration and two-mug shipping questions directly. No retention time, leakproof claim or health/environmental guarantee is added. |
These are product-relevant search themes, not measured demand. Search volume and competition are unknown for every theme; priority reflects match to the product and the shopper’s decision. No ranking weights or target keyword percentages are assumed.
| Theme and example queries | Intent | Volume / competition | Priority and placement |
|---|---|---|---|
| insulated stainless steel mug; stainless steel mug | Find product by material and insulation | Unknown / unknown | High: title and opening description |
| 500 mL mug; 500ml mug | Find a specific capacity | Unknown / unknown | High: canonical “500 mL” in title, bullet and description; test “500ml” in storefront search before adding variants |
| blue mug; HarborMug | Color or brand lookup | Unknown / unknown | High: title; blue as a color attribute |
| mug dimensions; mug weight | Compare physical fit | Unknown / unknown | Medium: specifications and second bullet |
| hand-wash mug; microwave safe mug | Check care compatibility | Unknown / unknown | High for decision clarity: care and FAQ; answer microwave queries truthfully with “not microwave safe” |
| mug shipping cost; mug returns | Resolve purchase objections | Unknown / unknown | High for decision clarity: purchase-area shipping text and policy sections |
Do not target “leakproof,” “spillproof,” “dishwasher safe,” “BPA-free,” “eco-friendly,” “travel mug,” or retention-hour phrases without supporting product evidence. Do not add irrelevant synonyms simply to repeat keywords. Natural coverage of the main product phrase and its attributes is the density framework here; no optimal percentage is established.
Shopify documents product titles, descriptions and product types as places to use shopper terms; its search behavior depends on the storefront implementation. This draft assumes Shopify’s built-in storefront search. Check the actual theme and any third-party search app before relying on those fields. See Shopify search behavior.
Search & Discovery can use product information including category and tags for semantic understanding. Proposed tags are organizational suggestions, not an Amazon-style backend keyword field or a promise of visibility. No fixed field weights or ranking lift are claimed. See Shopify Search & Discovery.
Qualitative assessment of the supplied listing, not an algorithm score. “Draft complete” means the copy is supplied here, not that a store was changed.
| Field | Current assessment | Finished draft / operational action |
|---|---|---|
| Title | Partial: brand and color only | Draft complete: material, capacity and insulation added |
| Bullets | Partial: one general feature | Draft complete: specifications, care, price, shipping and returns |
| Description | Incomplete: generic wording and item price | Draft complete: product details, policy sections and FAQ |
| Product attributes | Facts supplied but not shown in listing | Set vendor HarborMug, product type Mug, color Blue, material stainless steel, capacity 500 mL, weight 0.35 kg, dimensions 9 × 9 × 19 cm; retain supplied SKU internally |
| Category | Not supplied | Select the applicable mug category from the store’s current taxonomy; no category ID is invented |
| Tags / backend keywords | Empty keyword list | Suggested tags: insulated mug; stainless steel mug; 500 mL mug; blue mug. No separate backend-keyword field assumed |
| Images and alt text | One clear photo reported; no alt text | Suggested primary alt: “HarborMug blue stainless steel mug.” Confirm it describes the actual photo before use |
| Capacity/care image | Missing infographic | Add a factual 500 mL / dimensions / care graphic; keep the same information in text for accessibility |
| Lifestyle image | Absent | Lower priority: use a real product image in context, with no implication of unsupported lid, fit or leak resistance |
| Pricing and shipping | Only $24 visible | Draft complete; mirror threshold and charge in shipping settings and cart |
| Returns and FAQ | Missing | Draft complete, subject to policy detail below |
| Reviews | Supplied summary only | No review badge or quotations drafted; synthetic 12-review, 4.5/5 figures must not be published as real reviews |
Images were described in the brief but not supplied for visual inspection. No image asset or lifestyle scene has been fabricated.
All amounts are USD; totals exclude any applicable tax. Customer shipping uses $5 below a $50 merchandise subtotal and $0 at or above $50. No discounts were supplied.
| Quantity | Merchandise subtotal | Customer shipping | Total before tax | Free shipping? |
|---|---|---|---|---|
| 1 | $24 | $5 | $29 | No |
| 2 | $48 | $5 | $53 | No |
| 3 | $72 | $0 | $72 | Yes |
Supporting arithmetic is in price-calculations.csv. No weekly sales comparison is used as evidence of copy effectiveness: these figures precede this unimplemented draft.
| Priority | Action | Expected practical effect, not a measured lift |
|---|---|---|
| 1 | Place capacity, care and the one-mug price before tax near purchase controls; apply the revised title and bullets | Helps shoppers judge size, upkeep and cost before ordering |
| 1 | Confirm return-window start, return-shipping terms and dispatch/delivery interpretation; align full policies with product text | Reduces avoidable policy and timing misunderstandings |
| 1 | Verify cart shipping at $24, $48, below $50, exactly $50 and above $50; confirm US-only eligibility and tax display | Keeps the advertised amount consistent with checkout |
| 2 | Apply description, product attributes and relevant tags; confirm category | Gives shoppers and storefront search clearer product information |
| 2 | Add accurate alt text and a factual specifications/care image | Makes supplied facts easier to find and improves image accessibility |
| 3 | Test storefront queries “HarborMug,” “blue mug,” “500 mL mug,” “500ml mug” and “stainless steel mug,” including mobile predictive search | Checks actual discovery behavior without assuming indexing or rank |
| 3 | After implementation, compare product add-to-cart and purchase rates over comparable traffic; track care, capacity and shipping questions | Provides evidence for later revisions; no effect is yet measured |
No store URL, store configuration or search analytics was supplied, so this is a completed copy and attribute brief, with calculated shipping totals, rather than a live storefront validation. No store changes were made.
result.md: full report and finished customer-facing copy.listing-copy.json: reusable title, bullets, description and proposed metadata; not an automatic Shopify import.price-calculations.csv: quantity examples using the supplied customer shipping rules.evidence.md: source facts, claim boundaries and calculation rules.Merchant business data is synthetic. Website observations, when present, come from the public URLs listed in the report.
No live rankings or seller APIs are used. The handler adds default traps, defaults unknown platforms to Amazon, and falls back to generic attribute weights; these are not measured defects or official ranking weights.
Source and package files reviewed: ClawHub 1.0.0. This is a static review, not an installation or business-outcome test.
Use authorized inputs and keep secrets out of prompts. Review customer-facing claims, calculations, code changes, and any proposed external action before applying them.
Generate a heuristic listing-review brief from product notes and marketplace context. A Python handler structures keyword, attribute, and conversion-trap suggestions into Markdown.
Choose the method for your agent and install the complete package, not just the displayed SKILL.md. Platform, product details, current listing, optimization goal, and observed issues. Start a new agent session and explicitly ask to use search-listing-optimizer. Try one use case below with a small sample and review the output.
Draft a listing audit from supplied Amazon notes; Build a keyword and attribute review checklist; Separate observed conversion issues from default suggestions
No live rankings or seller APIs are used. The handler adds default traps, defaults unknown platforms to Amazon, and falls back to generic attribute weights; these are not measured defects or official ranking weights.
Source and package files reviewed: ClawHub 1.0.0. This is a static review, not an installation or business-outcome test.
Source and package files reviewed: ClawHub 1.0.0. This is a static review, not an installation or business-outcome test.
Source and package files reviewed: ClawHub 1.0.0. This is a static review, not an installation or business-outcome test.
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