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Given an index name, perform a comprehensive health check and identify optimization opportunities.\n\n## Workflow\n\n### Step 1: Gather index metadata\n\nUse `redis_ft_info` on the target index. Extract:\n- `num_docs` and `max_doc_id` (gap indicates deleted docs needing GC)\n- `num_records` (total inverted index entries)\n- `total_index_memory_sz_mb` (index memory footprint)\n- `percent_indexed` (should be 1.0; less means indexing is in progress or stalled)\n- `hash_indexing_failures` (non-zero means documents failed to index)\n- Schema definition (all fields, types, flags)\n\n### Step 2: Check data coverage\n\n1. Use `redis_scan` with the index prefix to count total keys matching the prefix\n2. Compare with `num_docs` -- if they differ significantly, investigate:\n   - Keys created after index (should auto-index)\n   - Keys with wrong structure (missing required fields)\n   - Indexing failures\n\n### Step 3: Analyze field efficiency\n\nFor each field in the schema, evaluate:\n\n**TEXT fields:**\n- Is NOSTEM appropriate? (product codes, IDs, SKUs should use NOSTEM)\n- Is the WEIGHT justified? (default 1.0 is fine for most fields)\n- Would TAG be better? (low-cardinality fields like status, category, brand)\n\n**TAG fields:**\n- Is SEPARATOR set correctly? (default comma; change if values contain commas)\n- Should CASESENSITIVE be enabled? (rare, but needed for case-sensitive identifiers)\n\n**NUMERIC fields:**\n- Is SORTABLE needed? (only if users sort by this field)\n- SORTABLE adds ~4-8 bytes per document -- worthwhile only for sort\u002Frange queries\n\n**Unused fields:**\n- Run sample queries with `redis_ft_search` using `@field:value` for each field\n- If a field is never queried, it's adding memory overhead without value\n\n### Step 4: Memory analysis\n\nCalculate:\n- **Index memory per document**: `total_index_memory_sz_mb * 1024 * 1024 \u002F num_docs`\n- **Overhead ratio**: Compare index memory to data memory (use `redis_memory_usage` on sample keys)\n- If index memory exceeds 50% of data memory, investigate which fields dominate\n\n### Step 5: Query performance check\n\nIf the user provides typical queries (or you can infer them):\n1. Run `redis_ft_profile` on each query\n2. Check for:\n   - Full index scans (no field-specific filter) -- suggests missing TAG\u002FNUMERIC fields\n   - High scorer count vs low result count -- suggests query is matching too broadly\n   - Slow intersect\u002Funion operations -- suggests high-cardinality TEXT fields that should be TAG\n\n### Step 6: Report\n\nPresent findings as:\n\n**Health Summary:**\n- Index status (healthy \u002F needs attention \u002F critical)\n- Document coverage (indexed \u002F total)\n- Indexing failures (count and likely cause)\n\n**Optimization Opportunities:**\n\n| Finding | Impact | Recommendation |\n|---------|--------|---------------|\n| `brand` is TEXT but has 45 distinct values | Memory + speed | Change to TAG |\n| `description` has SORTABLE but no sort queries | 8 bytes\u002Fdoc wasted | Remove SORTABLE |\n| `sku` is TEXT without NOSTEM | Stemming corrupts lookups | Add NOSTEM |\n| 3 fields never queried | Unnecessary memory | Consider removing |\n\n**Recommended Action:**\n- If changes are needed, suggest using the index-migration skill for a zero-downtime swap\n- If the index is healthy, say so -- don't recommend changes for the sake of changes\n\n## Tips\n\n- An index with 0 hash_indexing_failures and num_docs matching key count is healthy\n- TEXT fields with SORTABLE are unusual -- TEXT is for search, not sorting (use a separate NUMERIC or TAG field)\n- Index memory grows linearly with document count; if it grows faster, something is wrong\n- For JSON indexes, check that all fields have aliases -- raw JSONPath in queries is error-prone\n",{"data":38,"body":39},{"name":4,"description":6},{"type":40,"children":41},"root",[42,50,57,64,78,151,157,203,209,214,223,241,249,262,270,283,291,320,326,331,373,379,384,423,429,434,442,460,468,591,599,612,618],{"type":43,"tag":44,"props":45,"children":46},"element","p",{},[47],{"type":48,"value":49},"text","You are a Redis search index auditor. 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Redis dataset and recommend an optimal RediSearch index schema",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[736,737,738,739],{"name":665,"slug":666,"type":15},{"name":20,"slug":21,"type":15},{"name":9,"slug":8,"type":15},{"name":727,"slug":728,"type":15},"2026-05-28T07:09:48.520606",17,{"items":743,"total":912},[744,766,780,793,804,820,834,854,868,883,898,905],{"slug":745,"name":745,"fn":746,"description":747,"org":748,"tags":749,"stars":763,"repoUrl":764,"updatedAt":765},"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},[750,753,756,759,762],{"name":751,"slug":752,"type":15},"Agents","agents",{"name":754,"slug":755,"type":15},"AI Infrastructure","ai-infrastructure",{"name":757,"slug":758,"type":15},"Backend","backend",{"name":760,"slug":761,"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":767,"name":767,"fn":768,"description":769,"org":770,"tags":771,"stars":763,"repoUrl":764,"updatedAt":779},"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},[772,773,774,777,778],{"name":695,"slug":696,"type":15},{"name":20,"slug":21,"type":15},{"name":775,"slug":776,"type":15},"Infrastructure","infrastructure",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-27T07:19:38.757599",{"slug":781,"name":781,"fn":782,"description":783,"org":784,"tags":785,"stars":763,"repoUrl":764,"updatedAt":792},"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},[786,787,790,791],{"name":757,"slug":758,"type":15},{"name":788,"slug":789,"type":15},"Caching","caching",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-27T07:19:42.616757",{"slug":794,"name":794,"fn":795,"description":796,"org":797,"tags":798,"stars":763,"repoUrl":764,"updatedAt":803},"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},[799,800,801,802],{"name":695,"slug":696,"type":15},{"name":665,"slug":666,"type":15},{"name":20,"slug":21,"type":15},{"name":9,"slug":8,"type":15},"2026-05-27T07:19:41.317954",{"slug":805,"name":805,"fn":806,"description":807,"org":808,"tags":809,"stars":763,"repoUrl":764,"updatedAt":819},"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},[810,813,816,817,818],{"name":811,"slug":812,"type":15},"Debugging","debugging",{"name":814,"slug":815,"type":15},"Monitoring","monitoring",{"name":684,"slug":685,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-05-27T07:19:47.780873",{"slug":821,"name":821,"fn":822,"description":823,"org":824,"tags":825,"stars":763,"repoUrl":764,"updatedAt":833},"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},[826,827,828,831,832],{"name":695,"slug":696,"type":15},{"name":20,"slug":21,"type":15},{"name":829,"slug":830,"type":15},"Engineering","engineering",{"name":9,"slug":8,"type":15},{"name":727,"slug":728,"type":15},"2026-06-24T07:39:43.089819",{"slug":835,"name":835,"fn":836,"description":837,"org":838,"tags":839,"stars":763,"repoUrl":764,"updatedAt":853},"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},[840,843,846,849,850],{"name":841,"slug":842,"type":15},"Access Control","access-control",{"name":844,"slug":845,"type":15},"Authentication","authentication",{"name":847,"slug":848,"type":15},"Compliance","compliance",{"name":9,"slug":8,"type":15},{"name":851,"slug":852,"type":15},"Security","security","2026-05-27T07:19:40.030241",{"slug":855,"name":855,"fn":856,"description":857,"org":858,"tags":859,"stars":763,"repoUrl":764,"updatedAt":867},"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},[860,861,862,865,866],{"name":754,"slug":755,"type":15},{"name":788,"slug":789,"type":15},{"name":863,"slug":864,"type":15},"LLM","llm",{"name":9,"slug":8,"type":15},{"name":727,"slug":728,"type":15},"2026-05-27T07:19:43.897283",{"slug":869,"name":869,"fn":870,"description":871,"org":872,"tags":873,"stars":880,"repoUrl":881,"updatedAt":882},"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},[874,875,878,879],{"name":751,"slug":752,"type":15},{"name":876,"slug":877,"type":15},"File Storage","file-storage",{"name":760,"slug":761,"type":15},{"name":9,"slug":8,"type":15},22,"https:\u002F\u002Fgithub.com\u002Fredis\u002Fagent-filesystem","2026-04-10T04:51:05.904489",{"slug":884,"name":884,"fn":885,"description":886,"org":887,"tags":888,"stars":880,"repoUrl":881,"updatedAt":897},"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},[889,892,893,896],{"name":890,"slug":891,"type":15},"Codex","codex",{"name":760,"slug":761,"type":15},{"name":894,"slug":895,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-04-10T04:51:07.268248",{"slug":644,"name":644,"fn":645,"description":646,"org":899,"tags":900,"stars":23,"repoUrl":24,"updatedAt":657},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[901,902,903,904],{"name":650,"slug":651,"type":15},{"name":20,"slug":21,"type":15},{"name":654,"slug":655,"type":15},{"name":9,"slug":8,"type":15},{"slug":659,"name":659,"fn":660,"description":661,"org":906,"tags":907,"stars":23,"repoUrl":24,"updatedAt":672},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[908,909,910,911],{"name":665,"slug":666,"type":15},{"name":20,"slug":21,"type":15},{"name":669,"slug":670,"type":15},{"name":9,"slug":8,"type":15},27]