Amazon Elasticache
Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.
- Skill ID
- aws/agent-toolkit-for-aws/amazon-elasticache
- Publisher
- aws
- Repository
- agent-toolkit-for-aws
- Installs
- 0
- Files
- 80
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
aws/agent-toolkit-for-aws/amazon-elasticacheInstalls these files- SKILL.md
- assets/examples/sample_commandstats.csv
- assets/examples/sample_input_simple.csv
- references/data-modeling/command-availability.md
- references/data-modeling/common-patterns.md
- references/data-modeling/instructions.md
- references/data-modeling/memcached-recipe.md
- references/data-modeling/recipe-gallery.md
- references/genai/agent-memory.md
- references/genai/elasticache-search.md
- references/genai/embedding-providers.md
- references/genai/framework-guide.md
- references/genai/instructions.md
- references/genai/rag-retrieval.md
- references/genai/semantic-cache.md
- references/genai/session-store.md
- references/migration/auth-migration.md
- references/migration/feature-comparison.md
- references/migration/global-datastore-operations.md
- references/migration/instructions.md
- references/migration/rollback-procedures.md
- references/migration/self-managed-migration.md
- references/migration/sizing-assessment.md
- references/migration/topology-validation.md
- references/migration/upgrade-patching.md
- references/migration/valkey-migration-guide.md
- references/monitoring/alarm-packs.md
- references/monitoring/big-key-hunter.md
- references/monitoring/client-tuning-and-diagnostics.md
- references/monitoring/cloudwatch-dashboards.md
- references/monitoring/cost-reporting.md
- references/monitoring/event-routing.md
- references/monitoring/hot-key-detection.md
- references/monitoring/instructions.md
- references/monitoring/key-space-distribution-by-prefix.md
- references/monitoring/log-delivery.md
- references/monitoring/slot-memory-imbalance-detection.md
- references/monitoring/slow-log-cross-signal-diagnosis.md
- references/monitoring/troubleshooting.md
- references/requirements/instructions.md
- references/setup/auth-model-selector.md
- references/setup/cluster-topology.md
- references/setup/connection-guide.md
- references/setup/connectivity-diagnostics.md
- references/setup/create-secure-cache.md
- references/setup/engine-selection.md
- references/setup/iac-best-practices.md
- references/setup/iac-reference.md
- references/setup/iam-policies.md
- references/setup/instructions.md
- references/setup/serverless-vs-node.md
- references/setup/service-quotas.md
- references/shared-foundation/architecture-diagrams.md
- references/shared-foundation/attribution.md
- references/shared-foundation/boundary-doc.md
- references/shared-runtime/api-gateway.md
- references/shared-runtime/ecs.md
- references/shared-runtime/eks.md
- references/shared-runtime/lambda.md
- references/shared-runtime/rds-acceleration.md
- references/shared-runtime/secret-injection.md
- references/shared-security/config-guardrails.md
- references/shared-security/encryption-defaults.md
- references/shared-security/vpc-patterns.md
- references/shared-ux/action-safety.md
- references/shared-ux/error-remediation.md
- references/shared-ux/production-readiness.md
- scripts/collect_metrics.sh
- scripts/command_classifier.py
- scripts/find_tunnel_host.py
- scripts/generate_dashboards.py
- scripts/input_validator.py
- scripts/migration_preflight.py
- scripts/price_calculator.py
- scripts/pricing.py
- scripts/security_audit.py
- scripts/serverless_estimator.py
- scripts/start_tunnel.py
- scripts/test_connection.py
- scripts/validate_references.py
What this skill tells the agent
ElastiCache
A modular ElastiCache toolkit organized as a registry of sub-skills. Each sub-skill handles one domain of ElastiCache work. The router below matches user intent to the right sub-skill, then loads only the references needed for that sub-skill.
How this skill works
- Match the user's request against the semantic categories in the registry below. Match on meaning, not exact wording ("help me figure out which data structures to use" matches
data-modelingeven without the word "pattern"). - Disambiguation: If the user's intent matches multiple sub-skills, apply these rules in order:
- If
.elasticache/requirements.jsonexists withinfrastructure.endpointset, prefermonitoringordata-modeling(the user has an existing cache). - If no cache exists (no requirements.json or no endpoint), prefer
requirements. - If still ambiguous, ask one clarifying question: "Are you looking to set up something new, or troubleshoot something existing?"
- Check the Guardrails section before recommending an engine or deployment model.
- Read
references/{sub-skill-id}/instructions.mdfor the matched sub-skill. If the file is not found at a relative path, check your prompt or environment for the skill directory absolute path and retry with{skill-directory}/references/{sub-skill-id}/instructions.md. - If the request spans multiple sub-skills, execute them in pipeline order.
- If a sub-skill requires upstream context (engine, deployment model, endpoint) not yet in session memory, route to the upstream sub-skill first.
- If no sub-skill matches, activate
requirementsfirst. - If a script or CLI call fails, show the error to the user and suggest a specific fix before retrying.
Sub-skill registry
Each entry has: an ID (directory name under references/), a domain description, semantic categories for matching, and upstream/downstream dependencies.
| ID | Name | Domain | Semantic Categories | Upstream | Downstream |
|---|---|---|---|---|---|
requirements | Solution Fit | Gathers workload, stack, scale, latency, persistence, and budget through workspace scan + structured interview. Decides whether ElastiCache is the right service and hands off with a routing recommendation. | I need a cache, speed up my app, reduce database load, lower Bedrock cost, should I use ElastiCache, what's best for my workload, evaluating cache options, ElastiCache vs X, Valkey vs X, vague new workload | — | setup, data-modeling, genai, monitoring, migration |
setup | Create and Connect | Provisioning, connectivity, security, authentication, IaC, deployment choice. Gets the user to a working cache with least friction. Covers engine selection, serverless vs node-based, VPC, TLS, RBAC/IAM, jump-host/SSM tunnels, CLI/SDK/CFN/CDK/Terraform starters. | create a cache, set up ElastiCache, provision, Valkey cluster, connect Lambda/ECS/EKS/EC2, VPC, security groups, TLS, RBAC, IAM auth, jump host, SSM tunnel, CloudFormation, CDK, Terraform, engine selection, serverless vs node-based, backup, snapshot, restore, export | requirements (optional) | data-modeling, genai, monitoring |
data-modeling | Application Patterns | Picks data structures, key schema, TTL strategy, invalidation approach, and client code for non-AI patterns: cache-aside, session store, rate limiting, leaderboards, counters, pub/sub, streams, shopping carts, job queues, activity feeds. | session store, rate limiting, leaderboard, cache-aside, query caching, counters, streams, pub/sub, shopping cart, job queue, activity feed, key schema, TTL, invalidation, data structures | setup (cache must exist) | monitoring |
genai | AI and Vector Workloads | Classifies request into Mode 1 (plain cache), Mode 2 (semantic response cache), or Mode 3 (full vector search). Selects Valkey and forces node-based Valkey 8.2 or above (recommend 9.0) when server-side vector similarity is needed. Covers semantic caching, agent memory, RAG retrieval, recommendation, personalization, conversation/session persistence for AI agents, and framework wiring (Strands, mem0, LangChain). | semantic cache, RAG, agent memory, conversational memory, vector search, embeddings, recommendation, personalization, Bedrock latency, Bedrock cost, LLM caching, Strands, mem0, LangChain, conversation history, AI session store, embedding provider, framework integration | setup (cache must exist) | monitoring |
monitoring | Operate and Observe | Diagnoses performance, cost, and reliability using metrics first, then recommends the smallest change. Covers dashboards, alarms, log delivery, cost reporting, event routing, troubleshooting high CPU / memory / replication lag / connection spikes / low hit rate / hot keys / big keys / slot imbalance / latency spike root cause. | cache is slow, cost too high, hit rate low, high CPU, memory pressure, replication lag, connection spikes, dashboards, alarms, CloudWatch, cost comparison, troubleshoot, hot key, uneven shard load, one node pinned, big key, memory bloat, which key is biggest, keyspace distribution, prefix analysis, cost attribution by tenant, memory imbalance, one shard full, slot memory skew, latency spike, slow command incident, root cause for latency bump | — | setup, migration |
migration | Engine and Platform Migration | Selects the migration path and sequences preflight, validation, cutover, and rollback. Covers self-managed Redis → ElastiCache, Redis OSS → Valkey, node-based ↔ serverless, version upgrades. Hard validate-before-migrate gate. | migrate, Redis OSS to Valkey, self-managed to ElastiCache, node-based to serverless, serverless to node-based, engine upgrade, version upgrade, zero-downtime cutover, rollback | — | setup, monitoring |
Pipeline order
Sub-skills run independently, but common multi-step journeys follow these pipelines:
requirements→setup→ (data-modeling|genai) →monitoringmigration→setup→monitoringmonitoring→setup|migration(if metrics indicate)
State handoff: requirements.json
.elasticache/requirements.json is the single source of truth for cross-sub-skill state. Each sub-skill reads it at start and writes its section after completing work. Read before writing; merge, do not overwrite.
| Section | Owner | Key fields |
|---|---|---|
| top-level | requirements | engine, deployment_model, region, runtime, patterns, use_case, vpc_id, subnet_ids, security_group_ids |
infrastructure | setup | cache_name, resource_id, engine_version, topology, endpoint, port, auth_model, tls, client_library, execution_path, access_mode, tunnel_instance_id, embedding_provider, embedding_model, embedding_dim, embedding_module |
genai | genai | mode, mode_2_path, framework |
migration | migration | source_type, source_host, migration_path, cutover_status |
Ownership note:deployment_modelis set byrequirementsduring initial interview.migrationmay update it after an engine or deployment model switch (e.g., node-based to serverless).
requirements.json should include "schema_version": 1 and "last_updated": "<ISO timestamp>" at the top level. Every sub-skill that writes to requirements.json must update last_updated. If last_updated is older than 7 days, warn the user that cached state may be stale.
requirements.json tracks one active cache. If the user works with multiple caches in the same project, confirm which cache is active before reading or writing state.
When a sub-skill needs upstream context (engine, endpoint, auth model), check requirements.json first. If the field is null or the file does not exist, route to the upstream sub-skill.
Global rules (apply to every sub-skill)
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