search-shoes▌
zappos.com/search-shoes-j77j97 · updated May 21, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Search Zappos for shoes (and apparel, bags, accessories) matching a query plus filter set (size, width, brand, color, price, sort, etc.) and return structured per-product JSON — price, brand, ratings, colorways, image URLs, badges, canonical URL, plus the page total and active filter chips. Read-only.
| name | search-shoes |
| title | Zappos Search Shoes |
| description | >- Search Zappos for shoes (and apparel, bags, accessories) matching a query plus filter set (size, width, brand, color, price, sort, etc.) and return structured per-product JSON — price, brand, ratings, colorways, image URLs, badges, canonical URL, plus the page total and active filter chips. Read-only. |
| website | zappos.com |
| category | shopping |
| tags | - shopping - shoes - apparel - search - zappos - amazon |
| source | 'browserbase: agent-runtime 2026-05-19' |
| updated | '2026-05-19' |
| recommended_method | browser |
| alternative_methods | - method: browser rationale: >- Zappos has no public JSON API. Both the search/category surface and the PDP hydrate from JSON state objects embedded directly in the HTML (window.__INITIAL_STATE__ on search, window.__next_f on the PDP). Parsing those state objects in a stealth + residential-proxy Browserbase session is dramatically more reliable than DOM-scraping the listing cards and avoids the silent-degradation failure mode of bare sessions. - method: api rationale: >- Confirmed unavailable. Zappos is Amazon-owned but exposes no public Zappos-specific API. The internal calypso.zappos.com / cloudCatalog endpoints referenced in the embedded page config require Amazon SSO. |
| verified | false |
| proxies | true |
Zappos Search Shoes
Purpose
Search Zappos for shoes (and apparel, bags, accessories) matching a query plus an optional filter set, and return the matching results as structured JSON: per-product price, brand, ratings, colorways, image URLs, badges, canonical URL, plus the page-wide total result count and the active filter chip list. Optionally drill into each product's PDP for the full size × width × stock matrix. Read-only — never click Add to Cart, Add to Favorites, Sign In, or any purchase-flow control.
When to Use
- A shopping/comparison agent collecting structured product listings for a query like "running shoes men size 11 wide" or "women's black leather boots under $200".
- Bulk extraction of Zappos's full filter surface (gender × department × size × width × color × brand × material × occasion × discount × sort), which is materially richer than Amazon's general-purpose search.
- Tasks that need the per-variant size × width × stock matrix (only available on the PDP, not the listing page).
- Resolving a Zappos product-ID list to canonical product URLs and pricing.
Workflow
Zappos has no public JSON API. Both surfaces hydrate from a JSON state object embedded directly in the HTML — parsing the state object is dramatically more reliable than DOM-scraping the rendered listing cards (avoids lazy-loaded image placeholders, ref invalidation on scroll, and srcset ambiguity). Zappos inherits some Amazon anti-bot patterns but is lighter-touch than amazon.com proper; lead with a Browserbase stealth + residential-proxy session. A bare local Chromium will sometimes get a low-quality "robot-friendly" variant of the page without colorFacet/txAttrFacet_* populated — pay the stealth-proxy tax up front. Direct egress from non-Browserbase hosts is also DNS-blocked from this sandbox, so the browser is mandatory either way.
1. Create the session
export BROWSERBASE_API_KEY="$BB_API_KEY"
browse env remote
# Subsequent verbs auto-use this remote, stealth+proxies session.
browse open "<URL>" --advanced-stealth --proxies --keep-alive --session-timeout 600
--advanced-stealth --proxies --keep-alive should be passed on the first browse open of a session; the daemon persists the session for follow-up verbs.
2. Build the search URL
Accept any of these input shapes and normalize to a Zappos URL:
| Input | URL |
|---|---|
| Free-form query | https://www.zappos.com/search?term=<urlenc-query> |
| Query + department | https://www.zappos.com/search?term=<q> (Zappos's intent parser usually classifies department automatically — e.g. running shoes men auto-applies txAttrFacet_Gender=Men and zc1=Shoes; verify via filters.breadcrumbs) |
| Full Zappos URL passed by caller | Use as-is |
| Brand-browse URL | https://www.zappos.com/brand/<brandId> |
| Product-ID list | For each id, open https://www.zappos.com/product/<productId> (Zappos redirects to the canonical /p/<slug>/product/<id>/color/<colorId>) |
| Apply a UI-discovered filter | Use the facetZsoUrl value from __INITIAL_STATE__.facets.navigation[*].values[*].facetZsoUrl — these are pre-encoded /filters/<slug>/<base64>.zso?t=<term> URLs. They chain when you keep clicking, but the base64 token is not human-readable; the easiest way to build a multi-filter URL is to apply filters one at a time and follow facetZsoUrl each step. |
Pagination: append &p=<N> (0-indexed; 100 results per page). __INITIAL_STATE__.filters.pageCount tells you the total number of pages.
Sort: append &s=<key>/<dir>/<key2>/<dir2>/. Observed values: goLiveDate/desc/recommended/desc/ (Newest), recommended/desc/ (Best for You — default), customerRating/desc/, bestSellers/desc/, productPrice/asc/, productPrice/desc/, brandNameFacet/asc/ (Brand A–Z), reviewCount/desc/ (Most Reviews).
3. Load the page and pull the state object
browse open "$URL"
# Wait briefly for client-side hydration (search page is React + SSR, but
# facets.navigation populates a beat after initial paint).
sleep 3
RESULT=$(browse eval 'JSON.stringify({
total: window.__INITIAL_STATE__.products.totalProductCount,
page: window.__INITIAL_STATE__.filters.page,
pageCount: window.__INITIAL_STATE__.filters.pageCount,
term: window.__INITIAL_STATE__.filters.term,
breadcrumbs: window.__INITIAL_STATE__.filters.breadcrumbs.map(b => ({
name: b.name, removeUrl: b.removeUrl, autoFaceted: b.autoFaceted
})),
products: window.__INITIAL_STATE__.products.list.map(p => ({
productId: p.productId,
styleId: p.styleId,
colorId: p.colorId,
productName: p.productName,
brandName: p.brandName,
productType: p.productType,
gender: p.txAttrFacet_Gender,
color: p.color,
styleColor: p.styleColor,
price: p.price,
originalPrice: p.originalPrice,
percentOff: p.percentOff,
onSale: p.onSale === "true" || p.onSale === true,
isNew: p.isNew === "true" || p.isNew === true,
rating: p.reviewRating,
reviewCount: p.reviewCount,
badges: (p.badges || []).map(b => b.bid),
promoBadges: p.promoBadges || [],
image: p.msaImageId ? `https://m.media-amazon.com/images/I/${p.msaImageId}._AC_SR768,1024_.jpg` : null,
imageAngles: p.imageMap,
swatchUrl: p.swatchUrl,
colorwayCount: (p.relatedStyles || []).length + 1,
onHand: p.onHand,
isLowStock: p.isLowStock,
productUrl: "https://www.zappos.com" + p.productUrl,
})),
facets: window.__INITIAL_STATE__.facets.navigation.map(g => ({
field: g.facetField,
displayName: g.facetFieldDisplayName,
values: g.values.map(v => ({
name: v.name, count: v.count, selected: v.selected, facetZsoUrl: v.facetZsoUrl
}))
}))
})')
4. Paginate (if pageCount > 1 and caller wants > 100 results)
Loop p=1, p=2, ... up to pageCount - 1, re-running step 3 each time and concatenating products.
5. (Optional) Drill into the PDP for size × width × stock matrix
The search listing's sizing field is empty ({}) — the size matrix is only on the PDP. PDPs use Next.js streaming RSC, not __INITIAL_STATE__. Concatenate window.__next_f and parse for allStockItems:
browse open "$productUrl" # https://www.zappos.com/p/<slug>/product/<id>/color/<colorId>
sleep 3
browse eval "(() => {
const all = (window.__next_f || []).map(p => Array.isArray(p) ? p[1] : '').join('');
const matches = [...all.matchAll(/\"size\":(\\d+(?:\\.\\d+)?),\"sizeDimensionValueId\":\"\\d+\",\"sizeDisplayText\":\"([^\"]+)\"[^}]*?\"stockId\":\"([^\"]+)\"[^}]*?\"width\":\"([^\"]+)\"[^}]*?\"isOutOfStock\":(true|false)[^}]*?\"onHand\":\"(\\d+)\"/g)];
return JSON.stringify(matches.map(m => ({
size: m[2],
width: m[4], // e.g. 'D - Medium', '2E - Wide', '4E - Extra Wide'
stockId: m[3],
inStock: m[5] === 'false',
onHand: parseInt(m[6], 10)
})));
})()"
The width labels on the PDP use the letter+name form (D - Medium, 2E - Wide, 4E - Extra Wide, B - Medium, 2A - Narrow) — these are the canonical Zappos width strings. The listing-page facet (hc_men_width, hc_women_width) uses descriptive names only (Extra Narrow / Narrow / Medium / Wide / Extra Wide / Extra-Extra Wide); see the gotcha below.
6. Release
browse stop
Site-Specific Gotchas
window.__INITIAL_STATE__exists on search/category/filter pages, but NOT on the PDP. PDPs are a separate Next.js app that streams its state throughwindow.__next_f(an array of[type, chunk]tuples). Concatenate__next_f[*][1]and string-search for keys like"widths","allStockItems","sizing". The listing-page parser will silently return empty arrays on the PDP if you don't switch parsers.onSale,isNew, andisFabricSwatchare STRING booleans ("true"/"false"), not real booleans. Coerce with=== "true".percentOffis also a string ("15%","0%"), not a number — strip%and parseInt if you need a number.productRating(integer 0–5) andreviewRating(decimal float like 4.2) are both present and different fields. UsereviewRatingfor the precise stars;productRatingis the rounded integer used in the star-icon UI.- Listing-page
sizingis always{}— Zappos does not expose the per-size stock matrix on the listing. You must drill into the PDP per product. Plan for this cost (one extra page load per product when callers need the size matrix). - Listing width facet is descriptive-only; PDP uses letter codes. The
hc_men_width/hc_women_widthfacet on the listing emitsExtra Narrow / Narrow / Medium / Wide / Extra Wide / Extra-Extra Wide. The PDP'sallStockItems[*].widthemits the canonical letter form (D - Medium,2E - Wide,4E - Extra Wide,B - Medium,2A - Narrow,4A - Super Narrow,5E,6E). When the caller asks for "wide" or "2E", do the mapping client-side; don't try to pass a letter code to the listing-facet URL. - The intent parser auto-applies department + gender filters from the query text.
term=running+shoes+menauto-faceted tozc1=Shoes+txAttrFacet_Gender=Men. Verify what got applied via__INITIAL_STATE__.filters.breadcrumbs[*]— each chip carriesautoFaceted: true|false. If the caller's intent didn't include that filter and the auto-facet is wrong, follow theremoveUrlon the chip to drop it. /search?term=...redirects to a SEO slug path (/{slug}/.zso?t=...) once Zappos's intent parser classifies the query. The final URL is the canonical surface; either form works for follow-up&p=Npagination.- Filter URLs from
facetZsoUrlchain irreversibly via an opaque base64 token./filters/running-shoes-men/CK_XAeICAQE.zso?t=...is the result of applying one filter; click another and the path becomes/filters/running-shoes-men/CK_XAeICAQE+egLYBIIBAQTiAgEP.zso?t=.... The token is not human-decodable — you cannot construct multi-filter URLs offline. Apply filters by re-issuingbrowse openagainst thefacetZsoUrlof the next desired value, read the newfacets.navigationfrom the response, and repeat. Budget ~1 page load per filter applied. - Image URL template: from
msaImageId(e.g.71xtWRJ+iDL), the full image URL ishttps://m.media-amazon.com/images/I/<msaImageId>._AC_SR<W>,<H>_.jpg. Common sizes:SR768,1024(large),SR256,256(thumb). TheimageMapfield has 8 angle codes (MAIN,PAIR,FRNT,BACK,LEFT,RGHT,TOPP,BOTT) → each is its own msaImageId; expand the same way.thumbnailImageUrlis frequentlynull— fall back tomsaImageIdfor the cover image. - Canonical product URL form:
https://www.zappos.com/p/<slug>/product/<productId>/color/<colorId>. TheproductUrlandproductSeoUrlfields on each listing entry are paths, not absolute URLs — prependhttps://www.zappos.com. - Colorways: each entry in
products.listrepresents one colorway of a product. Sibling colorways are inrelatedStyles[](sameproductId, differentstyleId+colorId). Total colorway count =relatedStyles.length + 1. The "different colors" indicator in the UI is computed from this array. - Badge codes (
badges[*].bid): observed values includeNEW(new arrival),NWC(new with color refresh / new colorway),EXC(Zappos Exclusive),BST(best seller),CFP(Customer Favorite / "Customer Pick").promoBadgesis a separate array for site-wide promotions (e.g. discount codes). Zappos does not show "Amazon's Choice" labels here — that is Amazon-proper-only. - Free shipping & free 365-day returns are universal site policies, not per-product flags. No per-listing field exists; emit them as constants in the output schema (
free_shipping: true,free_returns: "365 days"). Zappos has carried free 365-day returns since 2009. - Stealth + residential proxy on the first session create is mandatory. Without
--advanced-stealth --proxiesthe page will frequently load but with hydration shapes that suggest a "robot-friendly" variant —facets.navigationpopulated butproducts.listtruncated, orfacetZsoUrlvalues returning 200 with zero results. We did not observe an explicit Akamai 403 in one full iteration of search + filter + sort + PDP, so anti-bot is lighter-touch than amazon.com — but the failure mode is silent degradation rather than a hard block. Lead with stealth. - The Vercel sandbox cannot do direct
curltowww.zappos.com— DNS resolution is blocked from this egress. All page fetches must route through the Browserbase session. Don't waste a turn trying. - Anchor
/p/<slug>/product/<id>(no/color/) works too and redirects to the default colorway. Useful when you only have a product-ID list and don't know the colorId. - Price string in the PDP RSC payload has a doubled dollar sign (
"$$320.00") — a JSON-encoding artifact of Next.js's RSC$prefix for reference markers. Strip one$when parsing PDP prices. The search listing'spricefield is clean ("$139.95"). - No size/width per-letter facet on the listing for kids. Zappos splits size facets by gender (
hc_men_size,hc_women_size,hc_kids_size) — pick the right one based on the activetxAttrFacet_Genderbreadcrumb. Mixing them returns 0 results. - Pagination cap:
pageCountis capped at ~50 (5000 results); searches that would return more are silently truncated. Use additional facets to narrow if the caller needs deeper drill.
Expected Output
{
"query": "running shoes men",
"url": "https://www.zappos.com/running-shoes-men/.zso?t=running+shoes+men",
"total_results": 738,
"page": 0,
"page_count": 8,
"active_filters": [
{ "field": "zc1", "name": "Shoes", "auto_faceted": true },
{ "field": "txAttrFacet_Gender", "name": "Men", "auto_faceted": true }
],
"available_facets": [
{
"field": "hc_men_width",
"display_name": "Men's Shoe Width",
"values": [
{ "name": "Medium", "count": 724, "selected": false },
{ "name": "Wide", "count": 180, "selected": false },
{ "name": "Extra Wide", "count": 6, "selected": false },
{ "name": "Extra-Extra Wide", "count": 59, "selected": false }
]
}
],
"products": [
{
"product_id": "10016301",
"style_id": "6586960",
"color_id": 742,
"product_name": "Velocity Nitro Running Shoes",
"brand": "PUMA",
"product_type": "Shoes",
"gender": ["Men"],
"color": "White",
"style_color": "White/Black",
"price": { "formatted": "$139.95", "raw": 139.95, "currency": "USD" },
"original_price": { "formatted": "$139.95", "raw": 139.95, "currency": "USD" },
"percent_off": 0,
"on_sale": false,
"is_new": false,
"rating": 4.2,
"review_count": 6,
"badges": ["NWC"],
"promo_badges": [],
"colorway_count": 4,
"image": "https://m.media-amazon.com/images/I/71xtWRJ+iDL._AC_SR768,1024_.jpg",
"image_angles": {
"MAIN": "https://m.media-amazon.com/images/I/71xtWRJ+iDL._AC_SR768,1024_.jpg",
"PAIR": "https://m.media-amazon.com/images/I/71xtWRJ+iDL._AC_SR768,1024_.jpg",
"FRNT": "https://m.media-amazon.com/images/I/61NLcuacfhL._AC_SR768,1024_.jpg",
"BACK": "https://m.media-amazon.com/images/I/61cnsdix22L._AC_SR768,1024_.jpg",
"LEFT": "https://m.media-amazon.com/images/I/61M8uJd0QuL._AC_SR768,1024_.jpg",
"RGHT": "https://m.media-amazon.com/images/I/71c0Tde3tGL._AC_SR768,1024_.jpg",
"TOPP": "https://m.media-amazon.com/images/I/71ATXAEyjHL._AC_SR768,1024_.jpg",
"BOTT": "https://m.media-amazon.com/images/I/61v1EiyFv8L._AC_SR768,1024_.jpg"
},
"swatch_url": "https://swch-cl2.olympus.zappos.com/fabric/27567/27580/10016301/6586960.jpg",
"product_url": "https://www.zappos.com/p/puma-velocity-nitro-running-shoes-white-black/product/10016301/color/742",
"in_stock": true,
"on_hand_estimate": 17,
"is_low_stock": false,
"free_shipping": true,
"free_returns": "365 days",
"size_matrix": null
}
]
}
When the caller requests the size × width matrix (one extra PDP load per product):
"size_matrix": [
{ "size": "8", "width": "D - Medium", "stock_id": "61282569", "in_stock": true, "on_hand": 1 },
{ "size": "8.5", "width": "D - Medium", "stock_id": "61282724", "in_stock": true, "on_hand": 1 },
{ "size": "9", "width": "D - Medium", "stock_id": "out_of_stock_1141186_61808_2831", "in_stock": false, "on_hand": 0 },
{ "size": "9.5", "width": "D - Medium", "stock_id": "61282860", "in_stock": true, "on_hand": 3 },
{ "size": "10", "width": "2E - Wide", "stock_id": "61283401", "in_stock": true, "on_hand": 2 }
]
When the query returns zero results (rare — Zappos's typo-correction and intent-parser usually rescue queries; observed only when forcing impossible filter combinations like hc_men_size=22 on a women-only category):
{
"query": "<original query>",
"total_results": 0,
"products": [],
"active_filters": [...],
"autocorrect": "running shoes men",
"no_results_reason": "filter_intersection_empty"
}
How to use search-shoes on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add search-shoes
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches search-shoes from GitHub repository zappos.com/search-shoes-j77j97 and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate search-shoes. Access the skill through slash commands (e.g., /search-shoes) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★70 reviews- ★★★★★Amelia Thomas· Dec 20, 2024
I recommend search-shoes for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Daniel Jain· Dec 12, 2024
Solid pick for teams standardizing on skills: search-shoes is focused, and the summary matches what you get after install.
- ★★★★★Dhruvi Jain· Dec 4, 2024
Registry listing for search-shoes matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Xiao Kim· Dec 4, 2024
Useful defaults in search-shoes — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Amina Yang· Dec 4, 2024
search-shoes is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★James Verma· Nov 27, 2024
We added search-shoes from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Oshnikdeep· Nov 23, 2024
search-shoes reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★James Anderson· Nov 23, 2024
search-shoes has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kofi Bansal· Nov 23, 2024
Keeps context tight: search-shoes is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★James Thomas· Nov 11, 2024
search-shoes fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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