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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsearch-shoesExecute the skills CLI command in your project's root directory to begin installation:
Fetches search-shoes from zappos.com/search-shoes-j77j97 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate search-shoes. Access via /search-shoes in your agent's command palette.
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.
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| 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 |
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.
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.
export BROWSERBASE_API_KEY="$BB_API_KEY"
browse env remote
# Subsequent verbs auto-use this remote, stealth+proxies session.
browse open "<URL>" --advanced-stealth --proxies --keep-alive --session-timeout 600
--advanced-stealth --proxies --keep-alive should be passed on the first browse open of a session; the daemon persists the session for follow-up verbs.
Accept any of these input shapes and normalize to a Zappos URL:
| Input | URL |
|---|---|
| Free-form query | https://www.zappos.com/search?term=<urlenc-query> |
| Query + department | https://www.zappos.com/search?term=<q> (Zappos's intent parser usually classifies department automatically — e.g. running shoes men auto-applies txAttrFacet_Gender=Men and zc1=Shoes; verify via filters.breadcrumbs) |
| Full Zappos URL passed by caller | Use as-is |
| Brand-browse URL | https://www.zappos.com/brand/<brandId> |
| Product-ID list | For each id, open https://www.zappos.com/product/<productId> (Zappos redirects to the canonical /p/<slug>/product/<id>/color/<colorId>) |
| Apply a UI-discovered filter | Use the facetZsoUrl value from __INITIAL_STATE__.facets.navigation[*].values[*].facetZsoUrl — these are pre-encoded /filters/<slug>/<base64>.zso?t=<term> URLs. They chain when you keep clicking, but the base64 token is not human-readable; the easiest way to build a multi-filter URL is to apply filters one at a time and follow facetZsoUrl each step. |
Pagination: append &p=<N> (0-indexed; 100 results per page). __INITIAL_STATE__.filters.pageCount tells you the total number of pages.
Sort: append &s=<key>/<dir>/<key2>/<dir2>/. Observed values: goLiveDate/desc/recommended/desc/ (Newest), recommended/desc/ (Best for You — default), customerRating/desc/, bestSellers/desc/, productPrice/asc/, productPrice/desc/, brandNameFacet/asc/ (Brand A–Z), reviewCount/desc/ (Most Reviews).
browse open "$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
}))
}))
})')
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.
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.
browse stop
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 through window.__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, and isFabricSwatch are STRING booleans ("true" / "false"), not real booleans. Coerce with === "true". percentOff is also a string ("15%", "0%"), not a number — strip % and parseInt if you need a number.productRating (integer 0–5) and reviewRating (decimal float like 4.2) are both present and different fields. Use reviewRating for the precise stars; productRating is the rounded integer used in the star-icon UI.sizing is 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).hc_men_width / hc_women_width facet on the listing emits Extra Narrow / Narrow / Medium / Wide / Extra Wide / Extra-Extra Wide. The PDP's allStockItems[*].width emits 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.term=running+shoes+men auto-faceted to zc1=Shoes + txAttrFacet_Gender=Men. Verify what got applied via __INITIAL_STATE__.filters.breadcrumbs[*] — each chip carries autoFaceted: true|false. If the caller's intent didn't include that filter and the auto-facet is wrong, follow the removeUrl on 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=N pagination.facetZsoUrl chain 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-issuing browse open against the facetZsoUrl of the next desired value, read the new facets.navigation from the response, and repeat. Budget ~1 page load per filter applied.msaImageId (e.g. 71xtWRJ+iDL), the full image URL is https://m.media-amazon.com/images/I/<msaImageId>._AC_SR<W>,<H>_.jpg. Common sizes: SR768,1024 (large), SR256,256 (thumb). The imageMap field has 8 angle codes (MAIN, PAIR, FRNT, BACK, LEFT, RGHT, TOPP, BOTT) → each is its own msaImageId; expand the same way. thumbnailImageUrl is frequently null — fall back to msaImageId for the cover image.https://www.zappos.com/p/<slug>/product/<productId>/color/<colorId>. The productUrl and productSeoUrl fields on each listing entry are paths, not absolute URLs — prepend https://www.zappos.com.products.list represents one colorway of a product. Sibling colorways are in relatedStyles[] (same productId, different styleId + colorId). Total colorway count = relatedStyles.length + 1. The "different colors" indicator in the UI is computed from this array.badges[*].bid): observed values include NEW (new arrival), NWC (new with color refresh / new colorway), EXC (Zappos Exclusive), BST (best seller), CFP (Customer Favorite / "Customer Pick"). promoBadges is 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: true, free_returns: "365 days"). Zappos has carried free 365-day returns since 2009.--advanced-stealth --proxies the page will frequently load but with hydration shapes that suggest a "robot-friendly" variant — facets.navigation populated but products.list truncated, or facetZsoUrl values 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.curl to www.zappos.com — DNS resolution is blocked from this egress. All page fetches must route through the Browserbase session. Don't waste a turn trying./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."$$320.00") — a JSON-encoding artifact of Next.js's RSC $ prefix for reference markers. Strip one $ when parsing PDP prices. The search listing's price field is clean ("$139.95").hc_men_size, hc_women_size, hc_kids_size) — pick the right one based on the active txAttrFacet_Gender breadcrumb. Mixing them returns 0 results.pageCount is capped at ~50 (5000 results); searches that would return more are silently truncated. Use additional facets to narrow if the caller needs deeper drill.{
"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"
}
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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I recommend search-shoes for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: search-shoes is focused, and the summary matches what you get after install.
Registry listing for search-shoes matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in search-shoes — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
search-shoes is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added search-shoes from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
search-shoes reduced setup friction for our internal harness; good balance of opinion and flexibility.
search-shoes has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: search-shoes is the kind of skill you can hand to a new teammate without a long onboarding doc.
search-shoes fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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