[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-algolia-algolia-discovery-planning":3,"mdc-p83stc-key":42,"related-repo-algolia-algolia-discovery-planning":866,"related-org-algolia-algolia-discovery-planning":956},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":37,"sourceUrl":40,"mdContent":41},"algolia-discovery-planning","plan and audit Algolia search implementations","START HERE for any non-trivial Algolia work — building, adding, migrating, redesigning, auditing, or configuring search, browse, autocomplete, indexing, relevance, recommendations, personalization, merchandising, events, or analytics. Invoke this FIRST even when the task already seems scoped or the user names one specific feature (e.g. \"add InstantSearch\", \"build a storefront search\"): its job is to map the request to the full Algolia implementation lifecycle and load every companion skill each in-scope phase needs (algolia-search-implementation, algolia-data-modeling, algolia-index-configuration, algolia-ui-libraries, algolia-instantsearch-ui, algolia-autocomplete, algolia-events-insights, algolia-neuralsearch, algolia-agent-studio, algolia-release-qa) rather than jumping straight into a single skill. This skill plans and orchestrates; the focused companion skills and the official Algolia skills execute. Do NOT use for live account inspection or write actions; use algolia-mcp or algolia-cli for those.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"algolia","Algolia","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Falgolia.png",[12,16,17,20,23],{"name":13,"slug":14,"type":15},"Configuration","configuration","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"Search","search",{"name":21,"slug":22,"type":15},"Analytics","analytics",{"name":24,"slug":25,"type":15},"Personalization","personalization",8,"https:\u002F\u002Fgithub.com\u002Falgolia\u002Fskills","2026-08-01T06:06:03.021452","MIT",2,[32,33,8,34,35,19,36],"agent","ai-assistant","mcp","recommendations","skills",{"repoUrl":27,"stars":26,"forks":30,"topics":38,"description":39},[32,33,8,34,35,19,36],"Algolia skills for AI Agents","https:\u002F\u002Fgithub.com\u002Falgolia\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Falgolia-discovery-planning","---\nname: algolia-discovery-planning\ndescription: >\n  START HERE for any non-trivial Algolia work — building, adding, migrating, redesigning, auditing, or configuring search, browse, autocomplete, indexing, relevance, recommendations, personalization, merchandising, events, or analytics. Invoke this FIRST even when the task already seems scoped or the user names one specific feature (e.g. \"add InstantSearch\", \"build a storefront search\"): its job is to map the request to the full Algolia implementation lifecycle and load every companion skill each in-scope phase needs (algolia-search-implementation, algolia-data-modeling, algolia-index-configuration, algolia-ui-libraries, algolia-instantsearch-ui, algolia-autocomplete, algolia-events-insights, algolia-neuralsearch, algolia-agent-studio, algolia-release-qa) rather than jumping straight into a single skill. This skill plans and orchestrates; the focused companion skills and the official Algolia skills execute. Do NOT use for live account inspection or write actions; use algolia-mcp or algolia-cli for those.\nlicense: MIT\nmetadata:\n  author: algolia\n  version: \"0.4\"\n---\n\n# Algolia Discovery Planning\n\nUse this skill before implementation work when the business goal, data shape, UX, event strategy, or success criteria are unclear. The job is to slow down just enough to avoid encoding the wrong search strategy.\n\n## Orchestrate The Whole Library (do this first)\n\nThese skills are a **suite that spans the Algolia implementation lifecycle**, not a set of independent tools where one match wins. A request that looks like a single-skill task (\"build search\", \"add autocomplete\", \"index my data\") almost always spans several phases. Before writing code or changing settings:\n\n1. **Map the request to the lifecycle** and mark every phase that is in scope:\n\n   | Phase | Load this skill | Typical trigger |\n   | --- | --- | --- |\n   | Run a net-new build end to end | `algolia-search-implementation` | greenfield search\u002Fbrowse\u002Fecommerce build; keeps checkpoints, signposts, and deferrals explicit |\n   | Shape the data \u002F records | `algolia-data-modeling` | new index, catalog, variants, objectID, ETL |\n   | Configure relevance | `algolia-index-configuration` | searchable attrs, ranking, facets, synonyms, rules, replicas, sort |\n   | Choose the UI library | `algolia-ui-libraries` | framework fit, InstantSearch vs Autocomplete, SSR\u002Frouting, mobile SDKs, upgrades |\n   | Build the results\u002Fbrowse UI | `algolia-instantsearch-ui` | search results page, filters, category\u002Fbrowse |\n   | Build typeahead | `algolia-autocomplete` | query suggestions, autocomplete panel |\n   | Instrument behavior | `algolia-events-insights` | click\u002Fview\u002Fadd-to-cart\u002Fconversion, personalization, Recommend, A\u002FB |\n   | Pre-launch check | `algolia-release-qa` | before shipping or after a risky change |\n   | AI \u002F conversational | `algolia-agent-studio`, `algolia-neuralsearch` | agents, RAG, semantic\u002Fneural retrieval |\n\n2. **State the phase plan** to the user: which phases apply, which skill owns each, and the order (a greenfield build usually runs data-modeling → index-configuration → instantsearch-ui \u002F autocomplete → events-insights → release-qa).\n\n3. **Load each in-scope skill via the Skill tool for its phase, and actually apply its guidance.** Do not skip a phase silently, and do not substitute your own knowledge for a skill that applies. If you deliberately skip a phase, say so and why.\n\n4. **Never claim a skill informed the work unless it was opened.** Report which skills ran and what each changed.\n\nThis is the default behavior for any multi-phase task. Only collapse to a single focused skill for a genuinely narrow, single-phase ask (e.g. \"rename one facet label\").\n\n## Customer-Facing Standard\n\n- Ask only the questions needed to choose a responsible next step.\n- State assumptions plainly when proceeding with incomplete context.\n- Use public `academy.algolia.com` for learning alignment and public `algolia.com\u002Fdoc` for current implementation guidance when source-backed context is needed.\n- When an Academy metadata reference pack is available, use only its `title`, `url`, `course`, `module`, `learning_objectives`, and `updated_at` fields for structure. If it is stale or no match exists, fall back to live Academy\u002Fdocs lookup. Do not treat cached metadata as course content or implementation authority.\n- Do not require custom Academy\u002Fdocs access; use customer-provided sources only as optional context.\n- When live Algolia data, analytics, index inspection, settings changes, or account actions are needed, use Algolia MCP, the Algolia CLI, or official Algolia skills for the live operation, then apply this skill to interpret results and validate the customer-ready implementation path.\n- Produce a concrete next step, owner handoff, and validation artifact.\n\n## Official Companion Skills\n\n- Use official `algolia-mcp` when discovery needs live search, analytics, recommendations, or index discovery.\n- Use official `algolia-cli` when discovery identifies records, settings, rules, synonyms, keys, backups, or index operations.\n- Use official `algobot-cli` for Agent Studio, RAG, conversational AI, and agent configuration paths.\n- Use official `instantsearch` for production InstantSearch or Autocomplete implementation paths.\n\n## Source-Of-Truth Rules\n\n- Do not answer account-specific questions from memory. Use MCP, CLI, dashboard exports, customer-provided screenshots, or sample files when live evidence is needed.\n- Do not recommend a product capability, package, command, or API shape without checking the official skill or current public docs when it affects implementation.\n- If evidence is unavailable, label the recommendation as provisional and name the exact artifact needed to confirm it.\n\n## Workflow\n\n1. Identify the implementation surface: indexing, relevance configuration, Search UI, Autocomplete, events, analytics, recommendations, personalization, merchandising, migration, or QA.\n2. Ask only the questions that block a responsible first design. Do not ask every checklist item at once.\n3. If the user wants you to proceed without answers, state the assumptions and choose reversible defaults.\n4. If the customer does not have a dedicated development or Algolia team, produce a practical handoff plan before implementation: what can be decided now, what access is needed, what likely requires developer help, and the safest first milestone.\n5. Load **every** in-scope companion skill (usually several, per the lifecycle map above), in phase order, and apply each — do not stop at the first match:\n   - `$algolia-search-implementation` for net-new builds, as the checkpoint checklist that spans the phases below.\n   - `$algolia-data-modeling` for records, indices, replicas, ETL, and record identity.\n   - `$algolia-index-configuration` for relevance, ranking, facets, synonyms, rules, and replicas.\n   - `$algolia-events-insights` for click, conversion, view, and Add-to-Cart events.\n   - `$algolia-ui-libraries` for choosing the current UI library and docs path before UI implementation.\n   - `$algolia-instantsearch-ui` for search results pages and browse\u002Fcategory experiences.\n   - `$algolia-autocomplete` for query suggestions and typeahead experiences.\n   - `$algolia-release-qa` for launch checks, diagnostics, and regressions.\n\n## Context Questions\n\nRead `references\u002Fdiscovery-question-bank.md` when the task is ambiguous or customer-facing. Select 3-7 questions from the relevant section, then proceed with explicit assumptions.\n\nPrefer questions that uncover:\n\n- Business outcomes: revenue, conversion, deflection, content discovery, operational efficiency.\n- Users and journeys: searchers, merchandisers, developers, support agents, admins.\n- Content and data: product, article, location, user-generated, B2B catalog, secured content.\n- Ranking intent: exact match, popularity, margin, availability, freshness, geo, personalization, business priority.\n- UX shape: search page, category browse, federated search, autocomplete, filters, sort-by, recommendations.\n- Measurement: events available, conversion definitions, analytics ownership, A\u002FB testing appetite.\n- Governance: environments, index naming, access controls, rollout plan, rollback plan.\n\n## Academy And Customer Education Alignment\n\nWhen source-backed guidance is needed, search public Academy sources for relevant learning objectives and public Algolia docs for implementation details. Classify the request by maturity level: beginner implementation, production readiness, optimization, or AI readiness. Also classify the use case: ecommerce search, content search, B2B catalog, support knowledge base, marketplace, or AI shopping assistant.\n\n## Start Prompt\n\nWhen the user is unsure where to begin, offer this prompt:\n\n```text\nUse the Algolia Discovery Planning skill to help me choose the right implementation path. Ask me only the questions needed to understand my goal, data, search UI, events, and launch risk. Assume I may not know which technical details matter yet. Then recommend the next Algolia skill, the smallest useful first milestone, and the validation artifact I should create.\n```\n\n## Maturity Behavior\n\n- Beginner implementation: identify the simplest correct first milestone and the minimum source material needed.\n- Production readiness: identify launch blockers, owners, rollback needs, and validation artifacts.\n- Optimization: identify baseline metrics, representative query sets, and controlled improvement paths.\n- AI readiness: identify whether data, events, permissions, and relevance evaluation are strong enough for NeuralSearch, Agent Studio, or AI assistants.\n\n## Output Contract\n\nWhen discovery is needed, produce:\n\n- Known facts.\n- Open questions that materially change the design.\n- Assumptions you will use if the user wants you to continue.\n- Recommended next skill or implementation step.\n- Customer handoff notes when the team needs help from a platform owner, developer, analytics owner, or implementation vendor.\n- Evidence needed before implementation, such as sample records, settings export, analytics screenshots, event payloads, or code access.\n\nKeep the response short enough that the user can answer. The point is momentum with fewer hidden traps.\n",{"data":43,"body":46},{"name":4,"description":6,"license":29,"metadata":44},{"author":8,"version":45},"0.4",{"type":47,"children":48},"root",[49,57,63,70,83,368,373,379,478,484,536,542,560,566,692,698,711,716,754,760,765,771,776,788,794,817,823,828,861],{"type":50,"tag":51,"props":52,"children":53},"element","h1",{"id":4},[54],{"type":55,"value":56},"text","Algolia Discovery Planning",{"type":50,"tag":58,"props":59,"children":60},"p",{},[61],{"type":55,"value":62},"Use this skill before implementation work when the business goal, data shape, UX, event strategy, or success criteria are unclear. The job is to slow down just enough to avoid encoding the wrong search strategy.",{"type":50,"tag":64,"props":65,"children":67},"h2",{"id":66},"orchestrate-the-whole-library-do-this-first",[68],{"type":55,"value":69},"Orchestrate The Whole Library (do this first)",{"type":50,"tag":58,"props":71,"children":72},{},[73,75,81],{"type":55,"value":74},"These skills are a ",{"type":50,"tag":76,"props":77,"children":78},"strong",{},[79],{"type":55,"value":80},"suite that spans the Algolia implementation lifecycle",{"type":55,"value":82},", not a set of independent tools where one match wins. A request that looks like a single-skill task (\"build search\", \"add autocomplete\", \"index my data\") almost always spans several phases. Before writing code or changing settings:",{"type":50,"tag":84,"props":85,"children":86},"ol",{},[87,338,348,358],{"type":50,"tag":88,"props":89,"children":90},"li",{},[91,96,98],{"type":50,"tag":76,"props":92,"children":93},{},[94],{"type":55,"value":95},"Map the request to the lifecycle",{"type":55,"value":97}," and mark every phase that is in scope:",{"type":50,"tag":99,"props":100,"children":101},"table",{},[102,126],{"type":50,"tag":103,"props":104,"children":105},"thead",{},[106],{"type":50,"tag":107,"props":108,"children":109},"tr",{},[110,116,121],{"type":50,"tag":111,"props":112,"children":113},"th",{},[114],{"type":55,"value":115},"Phase",{"type":50,"tag":111,"props":117,"children":118},{},[119],{"type":55,"value":120},"Load this skill",{"type":50,"tag":111,"props":122,"children":123},{},[124],{"type":55,"value":125},"Typical trigger",{"type":50,"tag":127,"props":128,"children":129},"tbody",{},[130,154,176,198,220,242,264,286,308],{"type":50,"tag":107,"props":131,"children":132},{},[133,139,149],{"type":50,"tag":134,"props":135,"children":136},"td",{},[137],{"type":55,"value":138},"Run a net-new build end to end",{"type":50,"tag":134,"props":140,"children":141},{},[142],{"type":50,"tag":143,"props":144,"children":146},"code",{"className":145},[],[147],{"type":55,"value":148},"algolia-search-implementation",{"type":50,"tag":134,"props":150,"children":151},{},[152],{"type":55,"value":153},"greenfield search\u002Fbrowse\u002Fecommerce build; keeps checkpoints, signposts, and deferrals explicit",{"type":50,"tag":107,"props":155,"children":156},{},[157,162,171],{"type":50,"tag":134,"props":158,"children":159},{},[160],{"type":55,"value":161},"Shape the data \u002F records",{"type":50,"tag":134,"props":163,"children":164},{},[165],{"type":50,"tag":143,"props":166,"children":168},{"className":167},[],[169],{"type":55,"value":170},"algolia-data-modeling",{"type":50,"tag":134,"props":172,"children":173},{},[174],{"type":55,"value":175},"new index, catalog, variants, objectID, ETL",{"type":50,"tag":107,"props":177,"children":178},{},[179,184,193],{"type":50,"tag":134,"props":180,"children":181},{},[182],{"type":55,"value":183},"Configure relevance",{"type":50,"tag":134,"props":185,"children":186},{},[187],{"type":50,"tag":143,"props":188,"children":190},{"className":189},[],[191],{"type":55,"value":192},"algolia-index-configuration",{"type":50,"tag":134,"props":194,"children":195},{},[196],{"type":55,"value":197},"searchable attrs, ranking, facets, synonyms, rules, replicas, sort",{"type":50,"tag":107,"props":199,"children":200},{},[201,206,215],{"type":50,"tag":134,"props":202,"children":203},{},[204],{"type":55,"value":205},"Choose the UI library",{"type":50,"tag":134,"props":207,"children":208},{},[209],{"type":50,"tag":143,"props":210,"children":212},{"className":211},[],[213],{"type":55,"value":214},"algolia-ui-libraries",{"type":50,"tag":134,"props":216,"children":217},{},[218],{"type":55,"value":219},"framework fit, InstantSearch vs Autocomplete, SSR\u002Frouting, mobile SDKs, upgrades",{"type":50,"tag":107,"props":221,"children":222},{},[223,228,237],{"type":50,"tag":134,"props":224,"children":225},{},[226],{"type":55,"value":227},"Build the results\u002Fbrowse UI",{"type":50,"tag":134,"props":229,"children":230},{},[231],{"type":50,"tag":143,"props":232,"children":234},{"className":233},[],[235],{"type":55,"value":236},"algolia-instantsearch-ui",{"type":50,"tag":134,"props":238,"children":239},{},[240],{"type":55,"value":241},"search results page, filters, category\u002Fbrowse",{"type":50,"tag":107,"props":243,"children":244},{},[245,250,259],{"type":50,"tag":134,"props":246,"children":247},{},[248],{"type":55,"value":249},"Build typeahead",{"type":50,"tag":134,"props":251,"children":252},{},[253],{"type":50,"tag":143,"props":254,"children":256},{"className":255},[],[257],{"type":55,"value":258},"algolia-autocomplete",{"type":50,"tag":134,"props":260,"children":261},{},[262],{"type":55,"value":263},"query suggestions, autocomplete panel",{"type":50,"tag":107,"props":265,"children":266},{},[267,272,281],{"type":50,"tag":134,"props":268,"children":269},{},[270],{"type":55,"value":271},"Instrument behavior",{"type":50,"tag":134,"props":273,"children":274},{},[275],{"type":50,"tag":143,"props":276,"children":278},{"className":277},[],[279],{"type":55,"value":280},"algolia-events-insights",{"type":50,"tag":134,"props":282,"children":283},{},[284],{"type":55,"value":285},"click\u002Fview\u002Fadd-to-cart\u002Fconversion, personalization, Recommend, A\u002FB",{"type":50,"tag":107,"props":287,"children":288},{},[289,294,303],{"type":50,"tag":134,"props":290,"children":291},{},[292],{"type":55,"value":293},"Pre-launch check",{"type":50,"tag":134,"props":295,"children":296},{},[297],{"type":50,"tag":143,"props":298,"children":300},{"className":299},[],[301],{"type":55,"value":302},"algolia-release-qa",{"type":50,"tag":134,"props":304,"children":305},{},[306],{"type":55,"value":307},"before shipping or after a risky change",{"type":50,"tag":107,"props":309,"children":310},{},[311,316,333],{"type":50,"tag":134,"props":312,"children":313},{},[314],{"type":55,"value":315},"AI \u002F conversational",{"type":50,"tag":134,"props":317,"children":318},{},[319,325,327],{"type":50,"tag":143,"props":320,"children":322},{"className":321},[],[323],{"type":55,"value":324},"algolia-agent-studio",{"type":55,"value":326},", ",{"type":50,"tag":143,"props":328,"children":330},{"className":329},[],[331],{"type":55,"value":332},"algolia-neuralsearch",{"type":50,"tag":134,"props":334,"children":335},{},[336],{"type":55,"value":337},"agents, RAG, semantic\u002Fneural retrieval",{"type":50,"tag":88,"props":339,"children":340},{},[341,346],{"type":50,"tag":76,"props":342,"children":343},{},[344],{"type":55,"value":345},"State the phase plan",{"type":55,"value":347}," to the user: which phases apply, which skill owns each, and the order (a greenfield build usually runs data-modeling → index-configuration → instantsearch-ui \u002F autocomplete → events-insights → release-qa).",{"type":50,"tag":88,"props":349,"children":350},{},[351,356],{"type":50,"tag":76,"props":352,"children":353},{},[354],{"type":55,"value":355},"Load each in-scope skill via the Skill tool for its phase, and actually apply its guidance.",{"type":55,"value":357}," Do not skip a phase silently, and do not substitute your own knowledge for a skill that applies. If you deliberately skip a phase, say so and why.",{"type":50,"tag":88,"props":359,"children":360},{},[361,366],{"type":50,"tag":76,"props":362,"children":363},{},[364],{"type":55,"value":365},"Never claim a skill informed the work unless it was opened.",{"type":55,"value":367}," Report which skills ran and what each changed.",{"type":50,"tag":58,"props":369,"children":370},{},[371],{"type":55,"value":372},"This is the default behavior for any multi-phase task. Only collapse to a single focused skill for a genuinely narrow, single-phase ask (e.g. \"rename one facet label\").",{"type":50,"tag":64,"props":374,"children":376},{"id":375},"customer-facing-standard",[377],{"type":55,"value":378},"Customer-Facing Standard",{"type":50,"tag":380,"props":381,"children":382},"ul",{},[383,388,393,414,463,468,473],{"type":50,"tag":88,"props":384,"children":385},{},[386],{"type":55,"value":387},"Ask only the questions needed to choose a responsible next step.",{"type":50,"tag":88,"props":389,"children":390},{},[391],{"type":55,"value":392},"State assumptions plainly when proceeding with incomplete context.",{"type":50,"tag":88,"props":394,"children":395},{},[396,398,404,406,412],{"type":55,"value":397},"Use public ",{"type":50,"tag":143,"props":399,"children":401},{"className":400},[],[402],{"type":55,"value":403},"academy.algolia.com",{"type":55,"value":405}," for learning alignment and public ",{"type":50,"tag":143,"props":407,"children":409},{"className":408},[],[410],{"type":55,"value":411},"algolia.com\u002Fdoc",{"type":55,"value":413}," for current implementation guidance when source-backed context is needed.",{"type":50,"tag":88,"props":415,"children":416},{},[417,419,425,426,432,433,439,440,446,447,453,455,461],{"type":55,"value":418},"When an Academy metadata reference pack is available, use only its ",{"type":50,"tag":143,"props":420,"children":422},{"className":421},[],[423],{"type":55,"value":424},"title",{"type":55,"value":326},{"type":50,"tag":143,"props":427,"children":429},{"className":428},[],[430],{"type":55,"value":431},"url",{"type":55,"value":326},{"type":50,"tag":143,"props":434,"children":436},{"className":435},[],[437],{"type":55,"value":438},"course",{"type":55,"value":326},{"type":50,"tag":143,"props":441,"children":443},{"className":442},[],[444],{"type":55,"value":445},"module",{"type":55,"value":326},{"type":50,"tag":143,"props":448,"children":450},{"className":449},[],[451],{"type":55,"value":452},"learning_objectives",{"type":55,"value":454},", and ",{"type":50,"tag":143,"props":456,"children":458},{"className":457},[],[459],{"type":55,"value":460},"updated_at",{"type":55,"value":462}," fields for structure. If it is stale or no match exists, fall back to live Academy\u002Fdocs lookup. Do not treat cached metadata as course content or implementation authority.",{"type":50,"tag":88,"props":464,"children":465},{},[466],{"type":55,"value":467},"Do not require custom Academy\u002Fdocs access; use customer-provided sources only as optional context.",{"type":50,"tag":88,"props":469,"children":470},{},[471],{"type":55,"value":472},"When live Algolia data, analytics, index inspection, settings changes, or account actions are needed, use Algolia MCP, the Algolia CLI, or official Algolia skills for the live operation, then apply this skill to interpret results and validate the customer-ready implementation path.",{"type":50,"tag":88,"props":474,"children":475},{},[476],{"type":55,"value":477},"Produce a concrete next step, owner handoff, and validation artifact.",{"type":50,"tag":64,"props":479,"children":481},{"id":480},"official-companion-skills",[482],{"type":55,"value":483},"Official Companion Skills",{"type":50,"tag":380,"props":485,"children":486},{},[487,500,512,524],{"type":50,"tag":88,"props":488,"children":489},{},[490,492,498],{"type":55,"value":491},"Use official ",{"type":50,"tag":143,"props":493,"children":495},{"className":494},[],[496],{"type":55,"value":497},"algolia-mcp",{"type":55,"value":499}," when discovery needs live search, analytics, recommendations, or index discovery.",{"type":50,"tag":88,"props":501,"children":502},{},[503,504,510],{"type":55,"value":491},{"type":50,"tag":143,"props":505,"children":507},{"className":506},[],[508],{"type":55,"value":509},"algolia-cli",{"type":55,"value":511}," when discovery identifies records, settings, rules, synonyms, keys, backups, or index operations.",{"type":50,"tag":88,"props":513,"children":514},{},[515,516,522],{"type":55,"value":491},{"type":50,"tag":143,"props":517,"children":519},{"className":518},[],[520],{"type":55,"value":521},"algobot-cli",{"type":55,"value":523}," for Agent Studio, RAG, conversational AI, and agent configuration paths.",{"type":50,"tag":88,"props":525,"children":526},{},[527,528,534],{"type":55,"value":491},{"type":50,"tag":143,"props":529,"children":531},{"className":530},[],[532],{"type":55,"value":533},"instantsearch",{"type":55,"value":535}," for production InstantSearch or Autocomplete implementation paths.",{"type":50,"tag":64,"props":537,"children":539},{"id":538},"source-of-truth-rules",[540],{"type":55,"value":541},"Source-Of-Truth Rules",{"type":50,"tag":380,"props":543,"children":544},{},[545,550,555],{"type":50,"tag":88,"props":546,"children":547},{},[548],{"type":55,"value":549},"Do not answer account-specific questions from memory. Use MCP, CLI, dashboard exports, customer-provided screenshots, or sample files when live evidence is needed.",{"type":50,"tag":88,"props":551,"children":552},{},[553],{"type":55,"value":554},"Do not recommend a product capability, package, command, or API shape without checking the official skill or current public docs when it affects implementation.",{"type":50,"tag":88,"props":556,"children":557},{},[558],{"type":55,"value":559},"If evidence is unavailable, label the recommendation as provisional and name the exact artifact needed to confirm it.",{"type":50,"tag":64,"props":561,"children":563},{"id":562},"workflow",[564],{"type":55,"value":565},"Workflow",{"type":50,"tag":84,"props":567,"children":568},{},[569,574,579,584,589],{"type":50,"tag":88,"props":570,"children":571},{},[572],{"type":55,"value":573},"Identify the implementation surface: indexing, relevance configuration, Search UI, Autocomplete, events, analytics, recommendations, personalization, merchandising, migration, or QA.",{"type":50,"tag":88,"props":575,"children":576},{},[577],{"type":55,"value":578},"Ask only the questions that block a responsible first design. Do not ask every checklist item at once.",{"type":50,"tag":88,"props":580,"children":581},{},[582],{"type":55,"value":583},"If the user wants you to proceed without answers, state the assumptions and choose reversible defaults.",{"type":50,"tag":88,"props":585,"children":586},{},[587],{"type":55,"value":588},"If the customer does not have a dedicated development or Algolia team, produce a practical handoff plan before implementation: what can be decided now, what access is needed, what likely requires developer help, and the safest first milestone.",{"type":50,"tag":88,"props":590,"children":591},{},[592,594,599,601],{"type":55,"value":593},"Load ",{"type":50,"tag":76,"props":595,"children":596},{},[597],{"type":55,"value":598},"every",{"type":55,"value":600}," in-scope companion skill (usually several, per the lifecycle map above), in phase order, and apply each — do not stop at the first match:\n",{"type":50,"tag":380,"props":602,"children":603},{},[604,615,626,637,648,659,670,681],{"type":50,"tag":88,"props":605,"children":606},{},[607,613],{"type":50,"tag":143,"props":608,"children":610},{"className":609},[],[611],{"type":55,"value":612},"$algolia-search-implementation",{"type":55,"value":614}," for net-new builds, as the checkpoint checklist that spans the phases below.",{"type":50,"tag":88,"props":616,"children":617},{},[618,624],{"type":50,"tag":143,"props":619,"children":621},{"className":620},[],[622],{"type":55,"value":623},"$algolia-data-modeling",{"type":55,"value":625}," for records, indices, replicas, ETL, and record identity.",{"type":50,"tag":88,"props":627,"children":628},{},[629,635],{"type":50,"tag":143,"props":630,"children":632},{"className":631},[],[633],{"type":55,"value":634},"$algolia-index-configuration",{"type":55,"value":636}," for relevance, ranking, facets, synonyms, rules, and replicas.",{"type":50,"tag":88,"props":638,"children":639},{},[640,646],{"type":50,"tag":143,"props":641,"children":643},{"className":642},[],[644],{"type":55,"value":645},"$algolia-events-insights",{"type":55,"value":647}," for click, conversion, view, and Add-to-Cart events.",{"type":50,"tag":88,"props":649,"children":650},{},[651,657],{"type":50,"tag":143,"props":652,"children":654},{"className":653},[],[655],{"type":55,"value":656},"$algolia-ui-libraries",{"type":55,"value":658}," for choosing the current UI library and docs path before UI implementation.",{"type":50,"tag":88,"props":660,"children":661},{},[662,668],{"type":50,"tag":143,"props":663,"children":665},{"className":664},[],[666],{"type":55,"value":667},"$algolia-instantsearch-ui",{"type":55,"value":669}," for search results pages and browse\u002Fcategory experiences.",{"type":50,"tag":88,"props":671,"children":672},{},[673,679],{"type":50,"tag":143,"props":674,"children":676},{"className":675},[],[677],{"type":55,"value":678},"$algolia-autocomplete",{"type":55,"value":680}," for query suggestions and typeahead experiences.",{"type":50,"tag":88,"props":682,"children":683},{},[684,690],{"type":50,"tag":143,"props":685,"children":687},{"className":686},[],[688],{"type":55,"value":689},"$algolia-release-qa",{"type":55,"value":691}," for launch checks, diagnostics, and regressions.",{"type":50,"tag":64,"props":693,"children":695},{"id":694},"context-questions",[696],{"type":55,"value":697},"Context Questions",{"type":50,"tag":58,"props":699,"children":700},{},[701,703,709],{"type":55,"value":702},"Read ",{"type":50,"tag":143,"props":704,"children":706},{"className":705},[],[707],{"type":55,"value":708},"references\u002Fdiscovery-question-bank.md",{"type":55,"value":710}," when the task is ambiguous or customer-facing. Select 3-7 questions from the relevant section, then proceed with explicit assumptions.",{"type":50,"tag":58,"props":712,"children":713},{},[714],{"type":55,"value":715},"Prefer questions that uncover:",{"type":50,"tag":380,"props":717,"children":718},{},[719,724,729,734,739,744,749],{"type":50,"tag":88,"props":720,"children":721},{},[722],{"type":55,"value":723},"Business outcomes: revenue, conversion, deflection, content discovery, operational efficiency.",{"type":50,"tag":88,"props":725,"children":726},{},[727],{"type":55,"value":728},"Users and journeys: searchers, merchandisers, developers, support agents, admins.",{"type":50,"tag":88,"props":730,"children":731},{},[732],{"type":55,"value":733},"Content and data: product, article, location, user-generated, B2B catalog, secured content.",{"type":50,"tag":88,"props":735,"children":736},{},[737],{"type":55,"value":738},"Ranking intent: exact match, popularity, margin, availability, freshness, geo, personalization, business priority.",{"type":50,"tag":88,"props":740,"children":741},{},[742],{"type":55,"value":743},"UX shape: search page, category browse, federated search, autocomplete, filters, sort-by, recommendations.",{"type":50,"tag":88,"props":745,"children":746},{},[747],{"type":55,"value":748},"Measurement: events available, conversion definitions, analytics ownership, A\u002FB testing appetite.",{"type":50,"tag":88,"props":750,"children":751},{},[752],{"type":55,"value":753},"Governance: environments, index naming, access controls, rollout plan, rollback plan.",{"type":50,"tag":64,"props":755,"children":757},{"id":756},"academy-and-customer-education-alignment",[758],{"type":55,"value":759},"Academy And Customer Education Alignment",{"type":50,"tag":58,"props":761,"children":762},{},[763],{"type":55,"value":764},"When source-backed guidance is needed, search public Academy sources for relevant learning objectives and public Algolia docs for implementation details. Classify the request by maturity level: beginner implementation, production readiness, optimization, or AI readiness. Also classify the use case: ecommerce search, content search, B2B catalog, support knowledge base, marketplace, or AI shopping assistant.",{"type":50,"tag":64,"props":766,"children":768},{"id":767},"start-prompt",[769],{"type":55,"value":770},"Start Prompt",{"type":50,"tag":58,"props":772,"children":773},{},[774],{"type":55,"value":775},"When the user is unsure where to begin, offer this prompt:",{"type":50,"tag":777,"props":778,"children":783},"pre",{"className":779,"code":781,"language":55,"meta":782},[780],"language-text","Use the Algolia Discovery Planning skill to help me choose the right implementation path. Ask me only the questions needed to understand my goal, data, search UI, events, and launch risk. Assume I may not know which technical details matter yet. Then recommend the next Algolia skill, the smallest useful first milestone, and the validation artifact I should create.\n","",[784],{"type":50,"tag":143,"props":785,"children":786},{"__ignoreMap":782},[787],{"type":55,"value":781},{"type":50,"tag":64,"props":789,"children":791},{"id":790},"maturity-behavior",[792],{"type":55,"value":793},"Maturity Behavior",{"type":50,"tag":380,"props":795,"children":796},{},[797,802,807,812],{"type":50,"tag":88,"props":798,"children":799},{},[800],{"type":55,"value":801},"Beginner implementation: identify the simplest correct first milestone and the minimum source material needed.",{"type":50,"tag":88,"props":803,"children":804},{},[805],{"type":55,"value":806},"Production readiness: identify launch blockers, owners, rollback needs, and validation artifacts.",{"type":50,"tag":88,"props":808,"children":809},{},[810],{"type":55,"value":811},"Optimization: identify baseline metrics, representative query sets, and controlled improvement paths.",{"type":50,"tag":88,"props":813,"children":814},{},[815],{"type":55,"value":816},"AI readiness: identify whether data, events, permissions, and relevance evaluation are strong enough for NeuralSearch, Agent Studio, or AI assistants.",{"type":50,"tag":64,"props":818,"children":820},{"id":819},"output-contract",[821],{"type":55,"value":822},"Output Contract",{"type":50,"tag":58,"props":824,"children":825},{},[826],{"type":55,"value":827},"When discovery is needed, produce:",{"type":50,"tag":380,"props":829,"children":830},{},[831,836,841,846,851,856],{"type":50,"tag":88,"props":832,"children":833},{},[834],{"type":55,"value":835},"Known facts.",{"type":50,"tag":88,"props":837,"children":838},{},[839],{"type":55,"value":840},"Open questions that materially change the design.",{"type":50,"tag":88,"props":842,"children":843},{},[844],{"type":55,"value":845},"Assumptions you will use if the user wants you to continue.",{"type":50,"tag":88,"props":847,"children":848},{},[849],{"type":55,"value":850},"Recommended next skill or implementation step.",{"type":50,"tag":88,"props":852,"children":853},{},[854],{"type":55,"value":855},"Customer handoff notes when the team needs help from a platform owner, developer, analytics owner, or implementation vendor.",{"type":50,"tag":88,"props":857,"children":858},{},[859],{"type":55,"value":860},"Evidence needed before implementation, such as sample records, settings export, analytics screenshots, event payloads, or code access.",{"type":50,"tag":58,"props":862,"children":863},{},[864],{"type":55,"value":865},"Keep the response short enough that the user can answer. The point is momentum with fewer hidden traps.",{"items":867,"total":955},[868,884,896,910,923,936,947],{"slug":521,"name":521,"fn":869,"description":870,"org":871,"tags":872,"stars":26,"repoUrl":27,"updatedAt":883},"build conversational AI with Algolia","Use for anything AI\u002Fagent\u002Fconversational built on Algolia: algobot CLI, Agent Studio, RAG systems, conversational product discovery, genAI content generation from search results (carousels, descriptions, headers), chatbots or recommendation agents using Algolia as retrieval, config-as-code workflows, multi-environment deploy (dev\u002Fstaging\u002Fprod), memory and personalization, MCP tool integrations, conversation history \u002F GDPR retention, or adding a chat widget alongside InstantSearch. Trigger on: \"algobot\", \"Agent Studio\", \"RAG with Algolia\", \"conversational experience\", \"AI agent\" + Algolia, \"genAI carousel\", \"chat widget\", or building AI features on top of Algolia search. Do NOT use for raw index ops (records, synonyms, settings) — use algolia-cli. Do NOT use for pure frontend search UI (InstantSearch, autocomplete) with no AI\u002Fagent layer.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[873,876,879,882],{"name":874,"slug":875,"type":15},"Agents","agents",{"name":877,"slug":878,"type":15},"Automation","automation",{"name":880,"slug":881,"type":15},"LLM","llm",{"name":18,"slug":19,"type":15},"2026-07-12T08:27:37.649724",{"slug":324,"name":324,"fn":885,"description":886,"org":887,"tags":888,"stars":26,"repoUrl":27,"updatedAt":895},"build conversational agents with Algolia Agent Studio","Product-specific Algolia Agent Studio implementation, validation, and optimization guidance. Use when planning, building, integrating, or auditing Agent Studio agents, AI-powered conversational experiences, LLM provider setup, Algolia Search tools, client-side tools, MCP tools, memory, prompting, conversations, turn context, caching, analytics, feedback, authentication, approved domains, guardrails, or tool security. Do NOT use for live Agent Studio configuration, dry runs, publish\u002Fdeploy actions, or config-as-code operations; use the official algobot-cli skill instead. Do NOT use for generic non-Algolia RAG or chatbot architecture unless Agent Studio is the target product.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[889,890,893,894],{"name":874,"slug":875,"type":15},{"name":891,"slug":892,"type":15},"AI","ai",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"2026-08-01T06:06:28.033767",{"slug":258,"name":258,"fn":897,"description":898,"org":899,"tags":900,"stars":26,"repoUrl":27,"updatedAt":909},"build Algolia autocomplete and query suggestions","Build and review Algolia Autocomplete and query suggestion experiences. Use when planning or implementing typeahead, query suggestions, recent searches, popular searches, federated autocomplete panels, product\u002Fcontent suggestions, detached mobile mode, plugins, keyboard navigation, insights events, or Autocomplete integration with InstantSearch. For net-new search or ecommerce builds, start with algolia-discovery-planning, which loads algolia-search-implementation so source strategy, data contract, and event taxonomy decisions are visible before autocomplete is marked ready. Do NOT use for full search results pages, browse pages, filters, pagination, or current refinements; use algolia-instantsearch-ui instead. Do NOT use for choosing between Algolia UI libraries; use algolia-ui-libraries. Do NOT use as the source of truth for current Autocomplete package APIs; use the official instantsearch skill and current docs alongside this customer-readiness skill.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[901,902,905,906],{"name":9,"slug":8,"type":15},{"name":903,"slug":904,"type":15},"Frontend","frontend",{"name":18,"slug":19,"type":15},{"name":907,"slug":908,"type":15},"UI Components","ui-components","2026-08-01T06:06:29.968123",{"slug":509,"name":509,"fn":911,"description":912,"org":913,"tags":914,"stars":26,"repoUrl":27,"updatedAt":922},"manage Algolia indices and accounts","Use this skill whenever a user wants to execute operations against Algolia indices or accounts — deleting records, copying\u002Fmigrating indices, backing up data, importing\u002Fexporting records, managing API keys, editing synonyms, configuring rules, changing settings like facets, clearing indices, or automating Algolia in CI\u002FCD pipelines. The key signal is that the user wants to *act on* their Algolia data or configuration (server-side \u002F backend \u002F admin operations), regardless of whether they mention \"CLI\" or \"command line.\" If someone names a specific Algolia index and wants to change, move, query, or manage it, use this skill. Do NOT use for frontend search UI work (InstantSearch, React components, autocomplete widgets), Algolia dashboard GUI questions, or evaluating Algolia vs. other providers.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[915,918,921],{"name":916,"slug":917,"type":15},"CLI","cli",{"name":919,"slug":920,"type":15},"Data Engineering","data-engineering",{"name":18,"slug":19,"type":15},"2026-07-12T08:27:35.085246",{"slug":924,"name":924,"fn":925,"description":926,"org":927,"tags":928,"stars":26,"repoUrl":27,"updatedAt":935},"algolia-crawler","crawl websites into Algolia indices","Use this skill whenever a user wants to crawl one or more web pages or a whole site and turn them into an Algolia index using the Algolia CLI — especially for RAG, AI search, semantic search, or Agent Studio retrieval. Triggers: \"index my website\u002Fdocs with Algolia\", \"set up the Algolia Crawler\", \"crawl this page for RAG\", \"scrape my site into Algolia\", \"build a knowledge base for my AI agent from these URLs\", writing or debugging a crawler recordExtractor, or handling JavaScript-rendered pages that won't index. It guides ingestion end-to-end with `algolia crawler` commands: inspect the page, write a RAG-optimized recordExtractor, validate with `algolia crawler test` BEFORE indexing, apply index settings explicitly, then reindex. Do NOT use for building the chatbot\u002Fagent layer itself (use algobot-cli), raw record\u002Fsynonym\u002Fsettings ops on an existing index (use algolia-cli), frontend search UI (use instantsearch), or read-only search\u002Fanalytics (use algolia-mcp).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[929,930,931,932],{"name":877,"slug":878,"type":15},{"name":919,"slug":920,"type":15},{"name":18,"slug":19,"type":15},{"name":933,"slug":934,"type":15},"Web Scraping","web-scraping","2026-07-12T08:27:40.981109",{"slug":170,"name":170,"fn":937,"description":938,"org":939,"tags":940,"stars":26,"repoUrl":27,"updatedAt":946},"design Algolia data models and indices","Algolia data modeling and indexing guidance. Use before or alongside indexing records or building Algolia search UI for net-new search, browse, autocomplete, ecommerce, personalization, Dynamic Re-Ranking, recommendations, or analytics-aware implementations. Makes record shape, objectID, display fields, facets, ranking fields, and event attribution explicit decisions. Use for records, variants, SKUs, indices, replicas, searchable and faceting attributes, denormalization, merchandising fields, timestamps, inventory, event attribution, indexing pipelines, partial updates, secured data, multi-language or multi-region strategies, and migrations. Do NOT use for live imports, exports, record mutations, settings changes, or account actions; use algolia-cli or algolia-mcp. Do NOT use for frontend UI implementation; use algolia-instantsearch-ui, algolia-autocomplete, algolia-ui-libraries, or the official instantsearch skill.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[941,942,945],{"name":9,"slug":8,"type":15},{"name":943,"slug":944,"type":15},"Data Modeling","data-modeling",{"name":18,"slug":19,"type":15},"2026-08-01T06:06:01.500756",{"slug":4,"name":4,"fn":5,"description":6,"org":948,"tags":949,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[950,951,952,953,954],{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},{"name":13,"slug":14,"type":15},{"name":24,"slug":25,"type":15},{"name":18,"slug":19,"type":15},18,{"items":957,"total":1075},[958,974,981,988,995,1001,1008,1014,1022,1032,1042,1063],{"slug":959,"name":959,"fn":960,"description":961,"org":962,"tags":963,"stars":971,"repoUrl":972,"updatedAt":973},"algolia-docsearch-mcp","search developer documentation with Algolia","Use this skill when the user asks about public developer documentation, SDKs, APIs, libraries, frameworks, setup, configuration, or code examples. Fetch current docs from Algolia DocSearch MCP and cite source URLs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[964,965,968,970],{"name":9,"slug":8,"type":15},{"name":966,"slug":967,"type":15},"Documentation","documentation",{"name":969,"slug":34,"type":15},"MCP",{"name":18,"slug":19,"type":15},4365,"https:\u002F\u002Fgithub.com\u002Falgolia\u002Fdocsearch","2026-08-01T06:06:11.572314",{"slug":521,"name":521,"fn":869,"description":870,"org":975,"tags":976,"stars":26,"repoUrl":27,"updatedAt":883},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[977,978,979,980],{"name":874,"slug":875,"type":15},{"name":877,"slug":878,"type":15},{"name":880,"slug":881,"type":15},{"name":18,"slug":19,"type":15},{"slug":324,"name":324,"fn":885,"description":886,"org":982,"tags":983,"stars":26,"repoUrl":27,"updatedAt":895},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[984,985,986,987],{"name":874,"slug":875,"type":15},{"name":891,"slug":892,"type":15},{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},{"slug":258,"name":258,"fn":897,"description":898,"org":989,"tags":990,"stars":26,"repoUrl":27,"updatedAt":909},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[991,992,993,994],{"name":9,"slug":8,"type":15},{"name":903,"slug":904,"type":15},{"name":18,"slug":19,"type":15},{"name":907,"slug":908,"type":15},{"slug":509,"name":509,"fn":911,"description":912,"org":996,"tags":997,"stars":26,"repoUrl":27,"updatedAt":922},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[998,999,1000],{"name":916,"slug":917,"type":15},{"name":919,"slug":920,"type":15},{"name":18,"slug":19,"type":15},{"slug":924,"name":924,"fn":925,"description":926,"org":1002,"tags":1003,"stars":26,"repoUrl":27,"updatedAt":935},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1004,1005,1006,1007],{"name":877,"slug":878,"type":15},{"name":919,"slug":920,"type":15},{"name":18,"slug":19,"type":15},{"name":933,"slug":934,"type":15},{"slug":170,"name":170,"fn":937,"description":938,"org":1009,"tags":1010,"stars":26,"repoUrl":27,"updatedAt":946},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1011,1012,1013],{"name":9,"slug":8,"type":15},{"name":943,"slug":944,"type":15},{"name":18,"slug":19,"type":15},{"slug":4,"name":4,"fn":5,"description":6,"org":1015,"tags":1016,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1017,1018,1019,1020,1021],{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},{"name":13,"slug":14,"type":15},{"name":24,"slug":25,"type":15},{"name":18,"slug":19,"type":15},{"slug":280,"name":280,"fn":1023,"description":1024,"org":1025,"tags":1026,"stars":26,"repoUrl":27,"updatedAt":1031},"instrument Algolia events for analytics","Algolia event instrumentation guidance for Insights, analytics, personalization, Dynamic Re-Ranking, Recommend, and merchandising feedback loops. Use for search, autocomplete, browse, ecommerce, personalization, recommendations, or analytics instrumentation. Makes event decisions explicit before an Algolia UI is considered ready. Use when implementing or auditing clickedObjectIDsAfterSearch, convertedObjectIDsAfterSearch, viewedObjectIDs, addedToCartObjectIDsAfterSearch, purchasedObjectIDsAfterSearch, userToken, queryID, eventName, eventSubtype, or frontend\u002Fbackend event pipelines. Do NOT use for live analytics retrieval, top-query inspection, or account-aware diagnostics; use algolia-mcp. Do NOT use for framework-specific InstantSearch or Autocomplete APIs; use the official instantsearch skill alongside this planning and validation skill.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1027,1028,1029,1030],{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},{"name":24,"slug":25,"type":15},{"name":18,"slug":19,"type":15},"2026-08-01T06:06:02.009712",{"slug":192,"name":192,"fn":1033,"description":1034,"org":1035,"tags":1036,"stars":26,"repoUrl":27,"updatedAt":1041},"configure Algolia index settings and relevance","Algolia index settings and relevance configuration guidance. Use when configuring searchableAttributes, attributesForFaceting, customRanking, ranking, replicas, virtual replicas, rules, synonyms, typo tolerance, distinct, filters, optional filters, merchandising, browse\u002Fcategory relevance, or A\u002FB-testable relevance changes. Do NOT use for live settings writes, backups, copies, or operational account tasks; use algolia-cli or algolia-mcp instead. Do NOT use for record-shape or variant strategy; use algolia-data-modeling instead.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1037,1038,1039,1040],{"name":9,"slug":8,"type":15},{"name":13,"slug":14,"type":15},{"name":943,"slug":944,"type":15},{"name":18,"slug":19,"type":15},"2026-08-01T06:06:34.19163",{"slug":236,"name":236,"fn":1043,"description":1044,"org":1045,"tags":1046,"stars":26,"repoUrl":27,"updatedAt":1062},"build Algolia InstantSearch frontend experiences","Build and review Algolia InstantSearch experiences in JavaScript, React, Vue, Angular (via InstantSearch.js; Angular InstantSearch is deprecated), or compatible frontend stacks. Use when planning or reviewing search results pages, browse\u002Fcategory pages, routing, widgets, filters, facets, sort-by, pagination, infinite hits, current refinements, insights middleware, SSR, or UI-state synchronization. For net-new search UI builds, start with algolia-discovery-planning, which loads algolia-search-implementation so data contract and event taxonomy decisions are visible before UI is marked ready. Do NOT use for autocomplete\u002Ftypeahead experiences before the user commits to a results page; use algolia-autocomplete instead. Do NOT use for choosing between Algolia UI libraries; use algolia-ui-libraries. Do NOT use as the source of truth for current framework APIs or code-level implementation details; use the official instantsearch skill and current docs alongside this customer-readiness skill.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1047,1048,1051,1052,1055,1058,1059],{"name":9,"slug":8,"type":15},{"name":1049,"slug":1050,"type":15},"Angular","angular",{"name":903,"slug":904,"type":15},{"name":1053,"slug":1054,"type":15},"JavaScript","javascript",{"name":1056,"slug":1057,"type":15},"React","react",{"name":18,"slug":19,"type":15},{"name":1060,"slug":1061,"type":15},"Vue","vue","2026-08-01T06:06:33.66513",{"slug":497,"name":497,"fn":1064,"description":1065,"org":1066,"tags":1067,"stars":26,"repoUrl":27,"updatedAt":1074},"search Algolia indices and retrieve analytics","Search Algolia indices via the Algolia MCP server, retrieve analytics (top searches, no-result rates, click positions, user counts), and get product recommendations (bought-together, related, trending). Triggers on search, indexing, analytics, Algolia, recommendations, MCP.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1068,1069,1072,1073],{"name":21,"slug":22,"type":15},{"name":1070,"slug":1071,"type":15},"API Development","api-development",{"name":969,"slug":34,"type":15},{"name":18,"slug":19,"type":15},"2026-07-12T08:27:36.376387",19]