[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-redis-index-ab-test":3,"mdc-5clky9-key":40,"related-repo-redis-index-ab-test":834,"related-org-redis-index-ab-test":927},{"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":35,"sourceUrl":38,"mdContent":39},"index-ab-test","compare RediSearch index configurations","Create and compare multiple RediSearch index configurations to find the best one",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"redis","Redis","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fredis.png",[12,16,19,22,23],{"name":13,"slug":14,"type":15},"Performance","performance","tag",{"name":17,"slug":18,"type":15},"Database","database",{"name":20,"slug":21,"type":15},"Search","search",{"name":9,"slug":8,"type":15},{"name":24,"slug":25,"type":15},"A\u002FB Testing","a-b-testing",15,"https:\u002F\u002Fgithub.com\u002Fredis\u002Fredisctl","2026-05-28T07:09:53.54884",null,0,[32,8,33,34],"cli","redis-cloud","redis-enterprise",{"repoUrl":27,"stars":26,"forks":30,"topics":36,"description":37},[32,8,33,34],"Unified CLI for Redis Cloud and Enterprise management","https:\u002F\u002Fgithub.com\u002Fredis\u002Fredisctl\u002Ftree\u002FHEAD\u002Fcrates\u002Fredisctl-mcp\u002Fskills\u002Findex-ab-test","---\nname: index-ab-test\ndescription: Create and compare multiple RediSearch index configurations to find the best one\n---\n\nYou are a Redis search index testing specialist. Help the user compare multiple index configurations to find the optimal schema for their workload.\n\n## Workflow\n\n### Step 1: Understand the dataset and goals\n\nAsk the user (or infer from context):\n- What key pattern holds the data? (e.g. `product:*`)\n- What queries will they run most often? (full-text search, filtering, sorting, aggregation)\n- What matters most? (query speed, result relevance, memory efficiency)\n\nUse `redis_scan` to count keys and `redis_json_get` or `redis_hgetall` to sample a few documents.\n\n### Step 2: Design index variants\n\nCreate 2-3 index configurations that differ in meaningful ways. Common axes to vary:\n\n**TEXT vs TAG for string fields:**\n- Variant A: `brand` as TEXT (fuzzy search, stemming)\n- Variant B: `brand` as TAG (exact match, faster filtering)\n\n**SORTABLE flags:**\n- Variant A: Only `price` SORTABLE (minimal memory)\n- Variant B: `price` + `rating` + `name` all SORTABLE (faster sorts, more memory)\n\n**TEXT weights:**\n- Variant A: `name` WEIGHT 1, `description` WEIGHT 1 (equal ranking)\n- Variant B: `name` WEIGHT 3, `description` WEIGHT 1 (name-biased ranking)\n\n**Field selection:**\n- Variant A: Index all fields\n- Variant B: Index only query-relevant fields (smaller index, faster writes)\n\nPresent the variants to the user in a comparison table before creating them.\n\n### Step 3: Create test indexes\n\nUse `redis_ft_create` to create each variant with distinct index names:\n- `idx:test_a` -- first configuration\n- `idx:test_b` -- second configuration\n- `idx:test_c` -- third configuration (if applicable)\n\nAll variants must use the same `PREFIX` so they index the same data.\n\nWait for indexing to complete -- check `redis_ft_info` and confirm `percent_indexed` is 1.0 for each.\n\n### Step 4: Define test queries\n\nBuild a representative set of 3-5 queries that match the user's expected workload:\n\n1. A full-text search (e.g. `wireless headphones`)\n2. A filtered query (e.g. `@category:{electronics} @price:[0 100]`)\n3. A sorted query (e.g. `* SORTBY price ASC`)\n4. An aggregation (e.g. group by category with average price)\n5. A complex combined query if applicable\n\n### Step 5: Benchmark each variant\n\nFor each query against each index variant:\n\n1. Run `redis_ft_profile` to get execution timing\n2. Run `redis_ft_search` with `withscores: true` to check result relevance\n3. Check `redis_ft_info` for index memory usage (`total_index_memory_sz_mb`)\n\nCollect results into a comparison matrix:\n\n| Query | Metric | Variant A | Variant B | Variant C |\n|-------|--------|-----------|-----------|-----------|\n| full-text | time (ms) | | | |\n| full-text | top result | | | |\n| full-text | score | | | |\n| filtered | time (ms) | | | |\n| sorted | time (ms) | | | |\n| -- | index size (MB) | | | |\n| -- | num_records | | | |\n\n### Step 6: Recommend and explain\n\nBased on the results:\n1. Identify the winning configuration\n2. Explain why it won (speed vs relevance vs memory trade-offs)\n3. Note any surprising results or trade-offs\n\nPresent a clear recommendation with the rationale.\n\n### Step 7: Clean up\n\nDrop the test indexes that were not selected:\n- Use `redis_ft_dropindex` for losing variants (without `delete_docs` since the data is shared)\n- Optionally rename the winning index using `redis_ft_aliasadd` to give it a production-friendly name\n\nAlways confirm with the user before dropping indexes.\n\n## Tips\n\n- For small datasets (\u003C 10k docs), timing differences may be negligible -- focus on result quality and memory\n- For large datasets, even small per-query improvements matter at scale\n- INDEX memory can differ significantly based on SORTABLE flags and field count\n- TAG fields use inverted indexes with very low overhead; TEXT fields add term dictionaries and offset vectors\n- If all variants perform similarly, recommend the simplest schema (fewer fields, fewer SORTABLE flags)\n",{"data":41,"body":42},{"name":4,"description":6},{"type":43,"children":44},"root",[45,53,60,67,72,101,130,136,141,150,178,186,228,236,276,284,297,302,308,320,356,369,390,396,401,451,457,462,517,522,714,720,725,743,748,754,759,795,800,806],{"type":46,"tag":47,"props":48,"children":49},"element","p",{},[50],{"type":51,"value":52},"text","You are a Redis search index testing specialist. Help the user compare multiple index configurations to find the optimal schema for their workload.",{"type":46,"tag":54,"props":55,"children":57},"h2",{"id":56},"workflow",[58],{"type":51,"value":59},"Workflow",{"type":46,"tag":61,"props":62,"children":64},"h3",{"id":63},"step-1-understand-the-dataset-and-goals",[65],{"type":51,"value":66},"Step 1: Understand the dataset and goals",{"type":46,"tag":47,"props":68,"children":69},{},[70],{"type":51,"value":71},"Ask the user (or infer from context):",{"type":46,"tag":73,"props":74,"children":75},"ul",{},[76,91,96],{"type":46,"tag":77,"props":78,"children":79},"li",{},[80,82,89],{"type":51,"value":81},"What key pattern holds the data? (e.g. ",{"type":46,"tag":83,"props":84,"children":86},"code",{"className":85},[],[87],{"type":51,"value":88},"product:*",{"type":51,"value":90},")",{"type":46,"tag":77,"props":92,"children":93},{},[94],{"type":51,"value":95},"What queries will they run most often? (full-text search, filtering, sorting, aggregation)",{"type":46,"tag":77,"props":97,"children":98},{},[99],{"type":51,"value":100},"What matters most? (query speed, result relevance, memory efficiency)",{"type":46,"tag":47,"props":102,"children":103},{},[104,106,112,114,120,122,128],{"type":51,"value":105},"Use ",{"type":46,"tag":83,"props":107,"children":109},{"className":108},[],[110],{"type":51,"value":111},"redis_scan",{"type":51,"value":113}," to count keys and ",{"type":46,"tag":83,"props":115,"children":117},{"className":116},[],[118],{"type":51,"value":119},"redis_json_get",{"type":51,"value":121}," or ",{"type":46,"tag":83,"props":123,"children":125},{"className":124},[],[126],{"type":51,"value":127},"redis_hgetall",{"type":51,"value":129}," to sample a few documents.",{"type":46,"tag":61,"props":131,"children":133},{"id":132},"step-2-design-index-variants",[134],{"type":51,"value":135},"Step 2: Design index variants",{"type":46,"tag":47,"props":137,"children":138},{},[139],{"type":51,"value":140},"Create 2-3 index configurations that differ in meaningful ways. Common axes to vary:",{"type":46,"tag":47,"props":142,"children":143},{},[144],{"type":46,"tag":145,"props":146,"children":147},"strong",{},[148],{"type":51,"value":149},"TEXT vs TAG for string fields:",{"type":46,"tag":73,"props":151,"children":152},{},[153,166],{"type":46,"tag":77,"props":154,"children":155},{},[156,158,164],{"type":51,"value":157},"Variant A: ",{"type":46,"tag":83,"props":159,"children":161},{"className":160},[],[162],{"type":51,"value":163},"brand",{"type":51,"value":165}," as TEXT (fuzzy search, stemming)",{"type":46,"tag":77,"props":167,"children":168},{},[169,171,176],{"type":51,"value":170},"Variant B: ",{"type":46,"tag":83,"props":172,"children":174},{"className":173},[],[175],{"type":51,"value":163},{"type":51,"value":177}," as TAG (exact match, faster filtering)",{"type":46,"tag":47,"props":179,"children":180},{},[181],{"type":46,"tag":145,"props":182,"children":183},{},[184],{"type":51,"value":185},"SORTABLE flags:",{"type":46,"tag":73,"props":187,"children":188},{},[189,202],{"type":46,"tag":77,"props":190,"children":191},{},[192,194,200],{"type":51,"value":193},"Variant A: Only ",{"type":46,"tag":83,"props":195,"children":197},{"className":196},[],[198],{"type":51,"value":199},"price",{"type":51,"value":201}," SORTABLE (minimal memory)",{"type":46,"tag":77,"props":203,"children":204},{},[205,206,211,213,219,220,226],{"type":51,"value":170},{"type":46,"tag":83,"props":207,"children":209},{"className":208},[],[210],{"type":51,"value":199},{"type":51,"value":212}," + ",{"type":46,"tag":83,"props":214,"children":216},{"className":215},[],[217],{"type":51,"value":218},"rating",{"type":51,"value":212},{"type":46,"tag":83,"props":221,"children":223},{"className":222},[],[224],{"type":51,"value":225},"name",{"type":51,"value":227}," all SORTABLE (faster sorts, more memory)",{"type":46,"tag":47,"props":229,"children":230},{},[231],{"type":46,"tag":145,"props":232,"children":233},{},[234],{"type":51,"value":235},"TEXT weights:",{"type":46,"tag":73,"props":237,"children":238},{},[239,258],{"type":46,"tag":77,"props":240,"children":241},{},[242,243,248,250,256],{"type":51,"value":157},{"type":46,"tag":83,"props":244,"children":246},{"className":245},[],[247],{"type":51,"value":225},{"type":51,"value":249}," WEIGHT 1, ",{"type":46,"tag":83,"props":251,"children":253},{"className":252},[],[254],{"type":51,"value":255},"description",{"type":51,"value":257}," WEIGHT 1 (equal ranking)",{"type":46,"tag":77,"props":259,"children":260},{},[261,262,267,269,274],{"type":51,"value":170},{"type":46,"tag":83,"props":263,"children":265},{"className":264},[],[266],{"type":51,"value":225},{"type":51,"value":268}," WEIGHT 3, ",{"type":46,"tag":83,"props":270,"children":272},{"className":271},[],[273],{"type":51,"value":255},{"type":51,"value":275}," WEIGHT 1 (name-biased ranking)",{"type":46,"tag":47,"props":277,"children":278},{},[279],{"type":46,"tag":145,"props":280,"children":281},{},[282],{"type":51,"value":283},"Field selection:",{"type":46,"tag":73,"props":285,"children":286},{},[287,292],{"type":46,"tag":77,"props":288,"children":289},{},[290],{"type":51,"value":291},"Variant A: Index all fields",{"type":46,"tag":77,"props":293,"children":294},{},[295],{"type":51,"value":296},"Variant B: Index only query-relevant fields (smaller index, faster writes)",{"type":46,"tag":47,"props":298,"children":299},{},[300],{"type":51,"value":301},"Present the variants to the user in a comparison table before creating them.",{"type":46,"tag":61,"props":303,"children":305},{"id":304},"step-3-create-test-indexes",[306],{"type":51,"value":307},"Step 3: Create test indexes",{"type":46,"tag":47,"props":309,"children":310},{},[311,312,318],{"type":51,"value":105},{"type":46,"tag":83,"props":313,"children":315},{"className":314},[],[316],{"type":51,"value":317},"redis_ft_create",{"type":51,"value":319}," to create each variant with distinct index names:",{"type":46,"tag":73,"props":321,"children":322},{},[323,334,345],{"type":46,"tag":77,"props":324,"children":325},{},[326,332],{"type":46,"tag":83,"props":327,"children":329},{"className":328},[],[330],{"type":51,"value":331},"idx:test_a",{"type":51,"value":333}," -- first configuration",{"type":46,"tag":77,"props":335,"children":336},{},[337,343],{"type":46,"tag":83,"props":338,"children":340},{"className":339},[],[341],{"type":51,"value":342},"idx:test_b",{"type":51,"value":344}," -- second configuration",{"type":46,"tag":77,"props":346,"children":347},{},[348,354],{"type":46,"tag":83,"props":349,"children":351},{"className":350},[],[352],{"type":51,"value":353},"idx:test_c",{"type":51,"value":355}," -- third configuration (if applicable)",{"type":46,"tag":47,"props":357,"children":358},{},[359,361,367],{"type":51,"value":360},"All variants must use the same ",{"type":46,"tag":83,"props":362,"children":364},{"className":363},[],[365],{"type":51,"value":366},"PREFIX",{"type":51,"value":368}," so they index the same data.",{"type":46,"tag":47,"props":370,"children":371},{},[372,374,380,382,388],{"type":51,"value":373},"Wait for indexing to complete -- check ",{"type":46,"tag":83,"props":375,"children":377},{"className":376},[],[378],{"type":51,"value":379},"redis_ft_info",{"type":51,"value":381}," and confirm ",{"type":46,"tag":83,"props":383,"children":385},{"className":384},[],[386],{"type":51,"value":387},"percent_indexed",{"type":51,"value":389}," is 1.0 for each.",{"type":46,"tag":61,"props":391,"children":393},{"id":392},"step-4-define-test-queries",[394],{"type":51,"value":395},"Step 4: Define test queries",{"type":46,"tag":47,"props":397,"children":398},{},[399],{"type":51,"value":400},"Build a representative set of 3-5 queries that match the user's expected workload:",{"type":46,"tag":402,"props":403,"children":404},"ol",{},[405,417,429,441,446],{"type":46,"tag":77,"props":406,"children":407},{},[408,410,416],{"type":51,"value":409},"A full-text search (e.g. ",{"type":46,"tag":83,"props":411,"children":413},{"className":412},[],[414],{"type":51,"value":415},"wireless headphones",{"type":51,"value":90},{"type":46,"tag":77,"props":418,"children":419},{},[420,422,428],{"type":51,"value":421},"A filtered query (e.g. ",{"type":46,"tag":83,"props":423,"children":425},{"className":424},[],[426],{"type":51,"value":427},"@category:{electronics} @price:[0 100]",{"type":51,"value":90},{"type":46,"tag":77,"props":430,"children":431},{},[432,434,440],{"type":51,"value":433},"A sorted query (e.g. ",{"type":46,"tag":83,"props":435,"children":437},{"className":436},[],[438],{"type":51,"value":439},"* SORTBY price ASC",{"type":51,"value":90},{"type":46,"tag":77,"props":442,"children":443},{},[444],{"type":51,"value":445},"An aggregation (e.g. group by category with average price)",{"type":46,"tag":77,"props":447,"children":448},{},[449],{"type":51,"value":450},"A complex combined query if applicable",{"type":46,"tag":61,"props":452,"children":454},{"id":453},"step-5-benchmark-each-variant",[455],{"type":51,"value":456},"Step 5: Benchmark each variant",{"type":46,"tag":47,"props":458,"children":459},{},[460],{"type":51,"value":461},"For each query against each index variant:",{"type":46,"tag":402,"props":463,"children":464},{},[465,478,498],{"type":46,"tag":77,"props":466,"children":467},{},[468,470,476],{"type":51,"value":469},"Run ",{"type":46,"tag":83,"props":471,"children":473},{"className":472},[],[474],{"type":51,"value":475},"redis_ft_profile",{"type":51,"value":477}," to get execution timing",{"type":46,"tag":77,"props":479,"children":480},{},[481,482,488,490,496],{"type":51,"value":469},{"type":46,"tag":83,"props":483,"children":485},{"className":484},[],[486],{"type":51,"value":487},"redis_ft_search",{"type":51,"value":489}," with ",{"type":46,"tag":83,"props":491,"children":493},{"className":492},[],[494],{"type":51,"value":495},"withscores: true",{"type":51,"value":497}," to check result relevance",{"type":46,"tag":77,"props":499,"children":500},{},[501,503,508,510,516],{"type":51,"value":502},"Check ",{"type":46,"tag":83,"props":504,"children":506},{"className":505},[],[507],{"type":51,"value":379},{"type":51,"value":509}," for index memory usage (",{"type":46,"tag":83,"props":511,"children":513},{"className":512},[],[514],{"type":51,"value":515},"total_index_memory_sz_mb",{"type":51,"value":90},{"type":46,"tag":47,"props":518,"children":519},{},[520],{"type":51,"value":521},"Collect results into a comparison matrix:",{"type":46,"tag":523,"props":524,"children":525},"table",{},[526,560],{"type":46,"tag":527,"props":528,"children":529},"thead",{},[530],{"type":46,"tag":531,"props":532,"children":533},"tr",{},[534,540,545,550,555],{"type":46,"tag":535,"props":536,"children":537},"th",{},[538],{"type":51,"value":539},"Query",{"type":46,"tag":535,"props":541,"children":542},{},[543],{"type":51,"value":544},"Metric",{"type":46,"tag":535,"props":546,"children":547},{},[548],{"type":51,"value":549},"Variant A",{"type":46,"tag":535,"props":551,"children":552},{},[553],{"type":51,"value":554},"Variant B",{"type":46,"tag":535,"props":556,"children":557},{},[558],{"type":51,"value":559},"Variant C",{"type":46,"tag":561,"props":562,"children":563},"tbody",{},[564,587,608,629,650,671,693],{"type":46,"tag":531,"props":565,"children":566},{},[567,573,578,581,584],{"type":46,"tag":568,"props":569,"children":570},"td",{},[571],{"type":51,"value":572},"full-text",{"type":46,"tag":568,"props":574,"children":575},{},[576],{"type":51,"value":577},"time (ms)",{"type":46,"tag":568,"props":579,"children":580},{},[],{"type":46,"tag":568,"props":582,"children":583},{},[],{"type":46,"tag":568,"props":585,"children":586},{},[],{"type":46,"tag":531,"props":588,"children":589},{},[590,594,599,602,605],{"type":46,"tag":568,"props":591,"children":592},{},[593],{"type":51,"value":572},{"type":46,"tag":568,"props":595,"children":596},{},[597],{"type":51,"value":598},"top result",{"type":46,"tag":568,"props":600,"children":601},{},[],{"type":46,"tag":568,"props":603,"children":604},{},[],{"type":46,"tag":568,"props":606,"children":607},{},[],{"type":46,"tag":531,"props":609,"children":610},{},[611,615,620,623,626],{"type":46,"tag":568,"props":612,"children":613},{},[614],{"type":51,"value":572},{"type":46,"tag":568,"props":616,"children":617},{},[618],{"type":51,"value":619},"score",{"type":46,"tag":568,"props":621,"children":622},{},[],{"type":46,"tag":568,"props":624,"children":625},{},[],{"type":46,"tag":568,"props":627,"children":628},{},[],{"type":46,"tag":531,"props":630,"children":631},{},[632,637,641,644,647],{"type":46,"tag":568,"props":633,"children":634},{},[635],{"type":51,"value":636},"filtered",{"type":46,"tag":568,"props":638,"children":639},{},[640],{"type":51,"value":577},{"type":46,"tag":568,"props":642,"children":643},{},[],{"type":46,"tag":568,"props":645,"children":646},{},[],{"type":46,"tag":568,"props":648,"children":649},{},[],{"type":46,"tag":531,"props":651,"children":652},{},[653,658,662,665,668],{"type":46,"tag":568,"props":654,"children":655},{},[656],{"type":51,"value":657},"sorted",{"type":46,"tag":568,"props":659,"children":660},{},[661],{"type":51,"value":577},{"type":46,"tag":568,"props":663,"children":664},{},[],{"type":46,"tag":568,"props":666,"children":667},{},[],{"type":46,"tag":568,"props":669,"children":670},{},[],{"type":46,"tag":531,"props":672,"children":673},{},[674,679,684,687,690],{"type":46,"tag":568,"props":675,"children":676},{},[677],{"type":51,"value":678},"--",{"type":46,"tag":568,"props":680,"children":681},{},[682],{"type":51,"value":683},"index size (MB)",{"type":46,"tag":568,"props":685,"children":686},{},[],{"type":46,"tag":568,"props":688,"children":689},{},[],{"type":46,"tag":568,"props":691,"children":692},{},[],{"type":46,"tag":531,"props":694,"children":695},{},[696,700,705,708,711],{"type":46,"tag":568,"props":697,"children":698},{},[699],{"type":51,"value":678},{"type":46,"tag":568,"props":701,"children":702},{},[703],{"type":51,"value":704},"num_records",{"type":46,"tag":568,"props":706,"children":707},{},[],{"type":46,"tag":568,"props":709,"children":710},{},[],{"type":46,"tag":568,"props":712,"children":713},{},[],{"type":46,"tag":61,"props":715,"children":717},{"id":716},"step-6-recommend-and-explain",[718],{"type":51,"value":719},"Step 6: Recommend and explain",{"type":46,"tag":47,"props":721,"children":722},{},[723],{"type":51,"value":724},"Based on the results:",{"type":46,"tag":402,"props":726,"children":727},{},[728,733,738],{"type":46,"tag":77,"props":729,"children":730},{},[731],{"type":51,"value":732},"Identify the winning configuration",{"type":46,"tag":77,"props":734,"children":735},{},[736],{"type":51,"value":737},"Explain why it won (speed vs relevance vs memory trade-offs)",{"type":46,"tag":77,"props":739,"children":740},{},[741],{"type":51,"value":742},"Note any surprising results or trade-offs",{"type":46,"tag":47,"props":744,"children":745},{},[746],{"type":51,"value":747},"Present a clear recommendation with the rationale.",{"type":46,"tag":61,"props":749,"children":751},{"id":750},"step-7-clean-up",[752],{"type":51,"value":753},"Step 7: Clean up",{"type":46,"tag":47,"props":755,"children":756},{},[757],{"type":51,"value":758},"Drop the test indexes that were not selected:",{"type":46,"tag":73,"props":760,"children":761},{},[762,782],{"type":46,"tag":77,"props":763,"children":764},{},[765,766,772,774,780],{"type":51,"value":105},{"type":46,"tag":83,"props":767,"children":769},{"className":768},[],[770],{"type":51,"value":771},"redis_ft_dropindex",{"type":51,"value":773}," for losing variants (without ",{"type":46,"tag":83,"props":775,"children":777},{"className":776},[],[778],{"type":51,"value":779},"delete_docs",{"type":51,"value":781}," since the data is shared)",{"type":46,"tag":77,"props":783,"children":784},{},[785,787,793],{"type":51,"value":786},"Optionally rename the winning index using ",{"type":46,"tag":83,"props":788,"children":790},{"className":789},[],[791],{"type":51,"value":792},"redis_ft_aliasadd",{"type":51,"value":794}," to give it a production-friendly name",{"type":46,"tag":47,"props":796,"children":797},{},[798],{"type":51,"value":799},"Always confirm with the user before dropping indexes.",{"type":46,"tag":54,"props":801,"children":803},{"id":802},"tips",[804],{"type":51,"value":805},"Tips",{"type":46,"tag":73,"props":807,"children":808},{},[809,814,819,824,829],{"type":46,"tag":77,"props":810,"children":811},{},[812],{"type":51,"value":813},"For small datasets (\u003C 10k docs), timing differences may be negligible -- focus on result quality and memory",{"type":46,"tag":77,"props":815,"children":816},{},[817],{"type":51,"value":818},"For large datasets, even small per-query improvements matter at scale",{"type":46,"tag":77,"props":820,"children":821},{},[822],{"type":51,"value":823},"INDEX memory can differ significantly based on SORTABLE flags and field count",{"type":46,"tag":77,"props":825,"children":826},{},[827],{"type":51,"value":828},"TAG fields use inverted indexes with very low overhead; TEXT fields add term dictionaries and offset vectors",{"type":46,"tag":77,"props":830,"children":831},{},[832],{"type":51,"value":833},"If all variants perform similarly, recommend the simplest schema (fewer fields, fewer SORTABLE flags)",{"items":835,"total":926},[836,851,866,881,894,907,915],{"slug":837,"name":837,"fn":838,"description":839,"org":840,"tags":841,"stars":26,"repoUrl":27,"updatedAt":850},"cloud-database-provisioning","provision Redis Cloud databases","Provision a Redis Cloud database end-to-end — Essentials (fixed plan) or Pro (custom) — using Redis Cloud MCP tools",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[842,845,846,849],{"name":843,"slug":844,"type":15},"Cloud","cloud",{"name":17,"slug":18,"type":15},{"name":847,"slug":848,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-06-03T07:53:00.906753",{"slug":852,"name":852,"fn":853,"description":854,"org":855,"tags":856,"stars":26,"repoUrl":27,"updatedAt":865},"compare-approaches","prototype Redis data model alternatives","Prototype and compare 2-3 Redis data model alternatives for the same workload",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[857,860,861,864],{"name":858,"slug":859,"type":15},"Data Modeling","data-modeling",{"name":17,"slug":18,"type":15},{"name":862,"slug":863,"type":15},"Prototyping","prototyping",{"name":9,"slug":8,"type":15},"2026-05-27T07:19:55.591944",{"slug":867,"name":867,"fn":868,"description":869,"org":870,"tags":871,"stars":26,"repoUrl":27,"updatedAt":880},"data-explorer","explore and profile Redis datasets","Profile and explore a Redis dataset - key types, sizes, TTLs, encodings, and sample values",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[872,875,876,879],{"name":873,"slug":874,"type":15},"Data Analysis","data-analysis",{"name":17,"slug":18,"type":15},{"name":877,"slug":878,"type":15},"Observability","observability",{"name":9,"slug":8,"type":15},"2026-05-28T07:09:52.268662",{"slug":882,"name":882,"fn":883,"description":884,"org":885,"tags":886,"stars":26,"repoUrl":27,"updatedAt":893},"data-modeling-advisor","design Redis data models","Recommend Redis data structures and patterns for a given workload before committing to an approach",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[887,890,891,892],{"name":888,"slug":889,"type":15},"Architecture","architecture",{"name":858,"slug":859,"type":15},{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},"2026-05-28T07:09:51.028825",{"slug":895,"name":895,"fn":896,"description":897,"org":898,"tags":899,"stars":26,"repoUrl":27,"updatedAt":906},"enterprise-health-check","perform Redis Enterprise cluster health checks","Comprehensive pre-operation health check for a Redis Enterprise cluster — verify cluster state, node health, license status, and active alerts before making changes",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[900,901,902,905],{"name":17,"slug":18,"type":15},{"name":877,"slug":878,"type":15},{"name":903,"slug":904,"type":15},"Operations","operations",{"name":9,"slug":8,"type":15},"2026-06-03T07:52:59.693017",{"slug":4,"name":4,"fn":5,"description":6,"org":908,"tags":909,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[910,911,912,913,914],{"name":24,"slug":25,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},{"slug":916,"name":916,"fn":917,"description":918,"org":919,"tags":920,"stars":26,"repoUrl":27,"updatedAt":925},"index-advisor","recommend RediSearch index schemas","Analyze a Redis dataset and recommend an optimal RediSearch index schema",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[921,922,923,924],{"name":858,"slug":859,"type":15},{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},"2026-05-28T07:09:48.520606",17,{"items":928,"total":1097},[929,951,965,978,989,1005,1019,1039,1053,1068,1083,1090],{"slug":930,"name":930,"fn":931,"description":932,"org":933,"tags":934,"stars":948,"repoUrl":949,"updatedAt":950},"iris-development","integrate with Redis Iris AI products","Iris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris\u002Fagent-memory` (TypeScript) SDKs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[935,938,941,944,947],{"name":936,"slug":937,"type":15},"Agents","agents",{"name":939,"slug":940,"type":15},"AI Infrastructure","ai-infrastructure",{"name":942,"slug":943,"type":15},"Backend","backend",{"name":945,"slug":946,"type":15},"Memory","memory",{"name":9,"slug":8,"type":15},92,"https:\u002F\u002Fgithub.com\u002Fredis\u002Fagent-skills","2026-05-27T07:19:45.213725",{"slug":952,"name":952,"fn":953,"description":954,"org":955,"tags":956,"stars":948,"repoUrl":949,"updatedAt":964},"redis-clustering","configure Redis clustering and replication","Redis Cluster and replication guidance covering hash tags for multi-key operations, avoiding CROSSSLOT errors, and reading from replicas to scale read-heavy workloads. Use when designing keys for a sharded Redis Cluster, debugging CROSSSLOT errors on MGET \u002F SDIFF \u002F pipelines, configuring a multi-key transaction in a cluster, or routing reads to replicas for caches, analytics, or dashboards.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[957,958,959,962,963],{"name":888,"slug":889,"type":15},{"name":17,"slug":18,"type":15},{"name":960,"slug":961,"type":15},"Infrastructure","infrastructure",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-27T07:19:38.757599",{"slug":966,"name":966,"fn":967,"description":968,"org":969,"tags":970,"stars":948,"repoUrl":949,"updatedAt":977},"redis-connections","optimize Redis client connections","Redis client and connection guidance covering connection pooling, multiplexing, pipelining, client-side caching with RESP3, avoiding slow commands (KEYS, SMEMBERS, HGETALL), and tuning socket timeouts. Use when configuring a Redis client (redis-py, Jedis, Lettuce, NRedisStack), batching commands for throughput, eliminating per-request connection creation, iterating large keyspaces with SCAN, enabling client-side caching for read-heavy workloads, or setting connect and read timeouts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[971,972,975,976],{"name":942,"slug":943,"type":15},{"name":973,"slug":974,"type":15},"Caching","caching",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-27T07:19:42.616757",{"slug":979,"name":979,"fn":980,"description":981,"org":982,"tags":983,"stars":948,"repoUrl":949,"updatedAt":988},"redis-core","model data with Redis structures","Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing\u002Fcleaning up Redis key naming.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[984,985,986,987],{"name":888,"slug":889,"type":15},{"name":858,"slug":859,"type":15},{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},"2026-05-27T07:19:41.317954",{"slug":990,"name":990,"fn":991,"description":992,"org":993,"tags":994,"stars":948,"repoUrl":949,"updatedAt":1004},"redis-observability","monitor and triage Redis performance","Redis observability guidance — which metrics to monitor (memory, connections, hit ratio, ops\u002Fsec, rejected connections), which built-in commands to reach for during incident triage (SLOWLOG, INFO, MEMORY DOCTOR, CLIENT LIST, FT.PROFILE), and when to use the Redis Insight GUI. Use when setting up monitoring or alerts for a Redis instance, diagnosing a performance regression, profiling a slow FT.SEARCH query, or wiring Redis metrics into Prometheus, Datadog, or similar.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[995,998,1001,1002,1003],{"name":996,"slug":997,"type":15},"Debugging","debugging",{"name":999,"slug":1000,"type":15},"Monitoring","monitoring",{"name":877,"slug":878,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-27T07:19:47.780873",{"slug":1006,"name":1006,"fn":1007,"description":1008,"org":1009,"tags":1010,"stars":948,"repoUrl":949,"updatedAt":1018},"redis-search","implement Redis Search indexing and queries","Redis Search guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR, JSON path), DIALECT 2 query syntax, FT.SEARCH \u002F FT.AGGREGATE \u002F FT.HYBRID command selection, vector similarity with HNSW or FLAT, hybrid retrieval combining lexical and vector ranking, RAG pipelines, zero-downtime index updates via aliases, and debugging with FT.PROFILE and FT.EXPLAIN. Use when defining a search index on Hash or JSON documents, writing FT.SEARCH queries with filters, sorting, aggregation, or vector KNN, tuning HNSW parameters, building a RAG retrieval pipeline, or troubleshooting slow or empty search results.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1011,1012,1013,1016,1017],{"name":888,"slug":889,"type":15},{"name":17,"slug":18,"type":15},{"name":1014,"slug":1015,"type":15},"Engineering","engineering",{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},"2026-06-24T07:39:43.089819",{"slug":1020,"name":1020,"fn":1021,"description":1022,"org":1023,"tags":1024,"stars":948,"repoUrl":949,"updatedAt":1038},"redis-security","secure Redis instances and clusters","Redis security guidance covering authentication (requirepass and ACL users), TLS, ACL-based least-privilege access control, restricting network exposure via bind and protected-mode, firewall rules, and disabling dangerous commands. Use when deploying Redis to production, defining ACL users for an application, configuring TLS connections, locking down a Redis instance behind a firewall, or auditing a Redis deployment for security hardening.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1025,1028,1031,1034,1035],{"name":1026,"slug":1027,"type":15},"Access Control","access-control",{"name":1029,"slug":1030,"type":15},"Authentication","authentication",{"name":1032,"slug":1033,"type":15},"Compliance","compliance",{"name":9,"slug":8,"type":15},{"name":1036,"slug":1037,"type":15},"Security","security","2026-05-27T07:19:40.030241",{"slug":1040,"name":1040,"fn":1041,"description":1042,"org":1043,"tags":1044,"stars":948,"repoUrl":949,"updatedAt":1052},"redis-semantic-cache","implement semantic caching with Redis","Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search\u002Fset via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI \u002F Anthropic \u002F etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1045,1046,1047,1050,1051],{"name":939,"slug":940,"type":15},{"name":973,"slug":974,"type":15},{"name":1048,"slug":1049,"type":15},"LLM","llm",{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},"2026-05-27T07:19:43.897283",{"slug":1054,"name":1054,"fn":1055,"description":1056,"org":1057,"tags":1058,"stars":1065,"repoUrl":1066,"updatedAt":1067},"agent-filesystem","manage persistent storage in Redis","Use when agents need persistent shared storage, when saving or restoring workspace state, or when coordinating file access across multiple agents and machines. Creates Redis-backed workspaces, checkpoints and restores agent state, mounts shared filesystems locally, searches workspace contents, and forks workspaces for parallel work.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1059,1060,1063,1064],{"name":936,"slug":937,"type":15},{"name":1061,"slug":1062,"type":15},"File Storage","file-storage",{"name":945,"slug":946,"type":15},{"name":9,"slug":8,"type":15},22,"https:\u002F\u002Fgithub.com\u002Fredis\u002Fagent-filesystem","2026-04-10T04:51:05.904489",{"slug":1069,"name":1069,"fn":1070,"description":1071,"org":1072,"tags":1073,"stars":1065,"repoUrl":1066,"updatedAt":1082},"codex-settings-sync","sync Codex settings across computers","Use when the user wants to migrate Codex state in ~\u002F.codex into Agent Filesystem and mount the same shared Codex memory\u002Fsettings across multiple computers. Recommends a .afsignore before migration and defaults to excluding worktrees, caches, logs, and temporary files.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1074,1077,1078,1081],{"name":1075,"slug":1076,"type":15},"Codex","codex",{"name":945,"slug":946,"type":15},{"name":1079,"slug":1080,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-04-10T04:51:07.268248",{"slug":837,"name":837,"fn":838,"description":839,"org":1084,"tags":1085,"stars":26,"repoUrl":27,"updatedAt":850},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1086,1087,1088,1089],{"name":843,"slug":844,"type":15},{"name":17,"slug":18,"type":15},{"name":847,"slug":848,"type":15},{"name":9,"slug":8,"type":15},{"slug":852,"name":852,"fn":853,"description":854,"org":1091,"tags":1092,"stars":26,"repoUrl":27,"updatedAt":865},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1093,1094,1095,1096],{"name":858,"slug":859,"type":15},{"name":17,"slug":18,"type":15},{"name":862,"slug":863,"type":15},{"name":9,"slug":8,"type":15},27]