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Amazon Elasticache

AWSOfficialPublished by AWS

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Medium risk
  • Has scripts
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From the author

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.
— the description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Amazon ElastiCache skill provides a structured, modular approach to managing in-memory data stores on AWS. It guides developers through the entire lifecycle of ElastiCache and Valkey, from initial requirements gathering and provisioning to complex data modeling, AI-driven vector search, and operational monitoring. The skill enforces best practices for connection safety, security, and infrastructure-as-code, ensuring reliable performance for caching, session management, and GenAI workloads.

What it does

  • Assesses workload requirements to recommend optimal ElastiCache configurations.
  • Automates provisioning and connectivity setup for serverless and node-based clusters.
  • Provides implementation patterns for common use cases like cache-aside, session stores, and rate limiting.
  • Supports advanced AI workloads including semantic caching, RAG, and vector similarity search.
  • Delivers operational diagnostics for performance tuning, cost optimization, and troubleshooting.
  • Manages migration paths from self-managed Redis or between different deployment models.

When to use it

  • When you need to reduce database load or improve API response times with an in-memory cache.
  • When implementing AI agent memory, RAG retrieval, or semantic caching for LLM applications.
  • When migrating existing Redis workloads to a managed AWS environment.
  • When troubleshooting performance issues like high latency, replication lag, or memory pressure.
  • When setting up secure, production-ready caching infrastructure using IaC.

Advantages

  • Standardizes deployment and operational patterns across development teams.
  • Ensures compliance with security best practices like TLS and IAM authentication.
  • Reduces configuration errors through automated input validation and tagging.
  • Provides clear guidance on engine selection and version-specific feature availability.
  • Simplifies complex tasks like cross-region migration and vector search setup.

Usage examples

“I need to speed up my RDS database. Should I use ElastiCache?”
The agent will initiate the requirements sub-skill to analyze your workload, stack, and latency needs, then provide a recommendation on whether ElastiCache is the right fit.
“How do I set up a Valkey cluster for vector search?”
The agent will route to the genai sub-skill, verify you are using a node-based Valkey 8.2+ cluster, and provide the necessary configuration and code to enable vector similarity search.
“My cache hit rate is low and latency is spiking. Can you help me troubleshoot?”
The agent will activate the monitoring sub-skill to analyze your metrics, identify potential hot keys or memory imbalance, and suggest specific remediation steps.
“Help me migrate my self-managed Redis to ElastiCache.”
The agent will guide you through the migration sub-skill, performing preflight checks and validating your source configuration for a zero-downtime cutover.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/aws/amazon-elasticache/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip amazon-elasticache.zip -d .agents/skills

Documentation by AWS

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

  1. 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-modeling even without the word "pattern").

  2. Disambiguation: If the user's intent matches multiple sub-skills, apply these rules in order:

    • If .elasticache/requirements.json exists with infrastructure.endpoint set, prefer monitoring or data-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?"

  3. Check the Guardrails section before recommending an engine or deployment model.

  4. Read references/{sub-skill-id}/instructions.md for 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.

  5. If the request spans multiple sub-skills, execute them in pipeline order.

  6. If a sub-skill requires upstream context (engine, deployment model, endpoint) not yet in session memory, route to the upstream sub-skill first.

  7. If no sub-skill matches, activate requirements first.

  8. 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.

IDNameDomainSemantic CategoriesUpstreamDownstream
requirementsSolution FitGathers 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
setupCreate and ConnectProvisioning, 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, exportrequirements (optional)data-modeling, genai, monitoring
data-modelingApplication PatternsPicks 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 structuressetup (cache must exist)monitoring
genaiAI and Vector WorkloadsClassifies 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 integrationsetup (cache must exist)monitoring
monitoringOperate and ObserveDiagnoses 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
migrationEngine and Platform MigrationSelects 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) → monitoring

  • migration → setup → monitoring

  • monitoring → 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.

SectionOwnerKey fields
top-levelrequirementsengine, deployment_model, region, runtime, patterns, use_case, vpc_id, subnet_ids, security_group_ids
infrastructuresetupcache_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
genaigenaimode, mode_2_path, framework
migrationmigrationsource_type, source_host, migration_path, cutover_status

Ownership note: deployment_model is set by requirements during initial interview. migration may 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)

  1. Execution path. Use AWS CLI, SDK (boto3), CloudFormation, or CDK as the primary path for control-plane work. Use valkey-py as the primary path for data-plane work.

  2. Response depth. Summary (2-3 sentences) for "should I" or "which" questions. Standard (recommendation + config + code + next steps) by default. Expert (full decision matrix with alternatives, cost, security caveats) for "why" or "compare all" questions. Escalate on user request; never downgrade unprompted.

  3. Session memory. Track region, VPC, engine, deployment model, auth model, compute runtime, and language. Carry forward across sub-skills. Do not re-ask. If the user overrides a value, update it everywhere. Inferred values (from workspace scan or IaC) must be re-confirmed before high-risk decisions (engine, deployment model, security posture); low-risk inferences (language, framework, region) can be used as defaults silently.

  4. Source priority. Always answer from skill-local files first (sub-skill references, then scripts/). Do not fetch external documentation or web search unless the local files cannot answer the query. When local files are insufficient, fall back to official AWS docs: https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/ for features and https://aws.amazon.com/elasticache/pricing/ for pricing. Never invent price points or version constraints. If the user references a Valkey or Redis version, feature, or pricing tier not covered in local files, fall back to https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/ before answering. Do not extrapolate from local content that may be outdated.

  5. Freshness disclaimer. When outputting pricing, version constraints, or feature availability, include a one-line disclaimer: "For current pricing see https://aws.amazon.com/elasticache/pricing/. For current feature availability see https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/."

  6. Connection safety. Never create a Valkey/Redis client at module level (top of file, import time). Initialize connections inside a function or on first use. Module-level connections crash applications that import the module before the cache is reachable.

  7. Code environment. Use python3 (not python) in all commands and shebangs. Do not generate pip install or pytest commands in application code. Prefer the agent's file-writing tool over shell redirection for writing files.

  8. Input validation (write operations only). Before running any create, modify, or delete CLI command or SDK call that uses user-provided values (cache name, region, subnet IDs, security group IDs, VPC ID, KMS key, snapshot name), validate them with scripts/input_validator.py. Run python3 scripts/input_validator.py cache_name=<val> region=<val> ... or import validate_all as a library. Skip validation for read-only operations (describe, list) where bad input is harmless.

  9. Resource tagging (always apply on resource creation). When creating any cache (create-serverless-cache, create-replication-group, create-cache-cluster), ALWAYS include these tags:

    --tags Key=managed_by,Value=aws-skills Key=skill,Value=elasticache Key=skill_version,Value=1.0.0 Key=created_by,Value=elasticache-skill Key=generation_model,Value={your-model-id}

    Example: --tags Key=managed_by,Value=aws-skills Key=skill,Value=elasticache Key=skill_version,Value=1.0.0 Key=created_by,Value=elasticache-skill Key=generation_model,Value=claude-sonnet-4-20250514

    Set generation_model to your own model identifier. Include these tags even if the user does not mention tagging. If the user provides additional tags, append these to their tags rather than replacing them.

Reference loading

Load additional references only when the current turn's answer requires them.

On-demand pointers (not preloaded; fetch when the trigger applies):

  • references/shared-ux/production-readiness.md — when the user asks if their cache is ready for production, or after setup completes and the user wants to go to production

  • references/shared-ux/action-safety.md — before any destructive action (risk levels, never-auto-execute list)

  • references/shared-ux/error-remediation.md — when the user hits a specific ElastiCache error code (MOVED, CROSSSLOT, CLUSTERDOWN, MULTI/EXEC+IAM, etc.)

  • references/shared-foundation/boundary-doc.md — when the user asks what this skill covers

  • references/shared-foundation/attribution.md — when generating CLI commands, SDK code, or IaC templates

  • references/shared-foundation/architecture-diagrams.md — when the user asks for architecture diagrams or visual reference

  • references/shared-runtime/lambda.md — when connecting from Lambda (cold start gotchas, IAM auth code, lazy init)

  • references/shared-runtime/ecs.md — when connecting from ECS (SIGTERM shutdown, connection pool drain, task definition)

  • references/shared-runtime/eks.md — when connecting from EKS (IRSA, service mesh bypass, SecurityGroupPolicy CRD)

  • references/shared-runtime/api-gateway.md — when integrating with API Gateway (no direct path, caching layers comparison)

  • references/shared-runtime/rds-acceleration.md — when caching RDS/Aurora queries (thundering herd, stampede protection, invalidation)

  • references/shared-runtime/secret-injection.md — when the user asks about credential management per compute platform

  • references/shared-security/encryption-defaults.md — when adding encryption to an existing unencrypted cluster (TLS two-step migration, at-rest immutability)

  • references/shared-security/config-guardrails.md — when the user wants continuous compliance monitoring (AWS Config rules, custom Lambda rules)

  • references/shared-security/vpc-patterns.md — when debugging port/security-group issues (port 6380 serverless reader, anti-patterns)

Folder convention: references/ contains 10 folders. 6 match the sub-skills (requirements, setup, data-modeling, genai, monitoring, migration) and are routing destinations. The 4 shared-* folders (shared-foundation, shared-ux, shared-security, shared-runtime) are cross-cutting material loaded on demand, not routing destinations.

Guardrails

PriorityRule
CRITICALVector search MUST use node-based Valkey 8.2 or above. Serverless does NOT support vector search. Never suggest serverless for vector search. Apply this regardless of which sub-skill activates.
CRITICALDo not invent price points or version constraints. Use scripts/price_calculator.py and current AWS docs when precision matters.
HIGHDo not recommend Memcached when the user needs persistence, replication, RBAC or IAM auth, sorted sets, streams, pub/sub, or vector search.
HIGHDo not assume local laptop access works directly. ElastiCache is VPC-centric; explain VPC, tunnel, or jump-host access when needed.
STANDARDDo not trigger on every generic Redis mention. Trigger when the user is clearly asking about AWS, managed caching, migration, connectivity, pricing, operations, or AWS service integration.
STANDARDFor ambiguous "cache" requests inside AWS contexts, activate this skill and start with requirements.

Product truths

  • ElastiCache Serverless deploys in under a minute and removes infrastructure management.

  • Valkey serverless pricing is 33% lower than other supported engines; node-based Valkey pricing is 20% lower.

  • Serverless caches have in-transit encryption always enabled (cannot be disabled).

  • IAM auth is available for all ElastiCache Valkey versions (7.2 is the baseline Valkey version on ElastiCache) and Redis OSS 7.0+.

  • Valkey version ladder: 7.2 (baseline), 8.0 (20% more data per node (capacity improvement), per-slot metrics), 8.1 (Bloom filters, COMMANDLOG, SET IFEQ, 20% less memory via new hash table (efficiency improvement)), 8.2 (vector search), 9.0 (recommended default for new clusters). Recommend Valkey 9.0 for new clusters unless a specific feature dictates otherwise.

  • Vector search is available for Valkey 8.2 or above on node-based clusters (recommend 9.0).

  • Global Datastore is available for node-based clusters only. It does not support IPv6 or Local Zones. Global Datastore supports AUTH and RBAC. Cross-region failover must be promoted manually (no autofailover across regions). At-rest encryption must be enabled on all clusters in the Global Datastore, but each cluster can use a separate KMS key per region.

  • Online migration from self-managed Redis to ElastiCache requires: (source) AUTH must not be enabled, protected-mode set to no, replication and administrative commands must not be renamed (e.g., sync, psync, info, config, command, cluster); (target) encryption in-transit disabled, Multi-AZ enabled, engine version Redis OSS 5.0.6+ or Valkey 7.2+, not part of a Global Datastore, data tiering disabled. Shard counts must match between source and target. All source Redis instances must use the same port. Online migration is not supported for serverless caches (node-based targets only). See references/migration/topology-validation.md for the full checklist.

Files

references/requirements/instructions.md

Solution Fit

Gather enough context to recommend the right ElastiCache configuration, then hand off to the appropriate sub-skill.

Loading

Read this file first. Load scripts/price_calculator.py when estimating cost.

Check for existing context

Before starting, check if .elasticache/requirements.json exists from a previous session. If it does, read it, present the values to the user, and ask if anything has changed. If confirmed accurate, skip directly to Hand off.

Fast path (skip the interview)

If the user already knows what they want, don't interview them. Run the workspace scan silently, then route:

  • User names a specific product or engine ("create a Valkey serverless cache") -> skip to Summarize and hand off. Ask only for missing critical values (region, VPC). Serverless supports Valkey 7.2, 8.0, and 8.1. If user requests Redis OSS, note that Valkey is the recommended forward path. Redis OSS remains available (highest version on ElastiCache: 7.1), but Valkey receives new feature investments (vector search, Bloom filters, memory-efficient hash table, COMMANDLOG, and more). If the user needs vector search (semantic caching, agentic memory, RAG), note that Valkey 8.2+ is required and is available on node-based clusters only (not available on Serverless or data-tiering r6gd instances).

  • User names a specific pattern ("session store", "rate limit my API", "semantic cache for Bedrock") -> confirm inferences from scan, skip to Summarize and hand off.

  • User names a specific task ("migrate from Redis to Valkey", "check my cache costs") -> route directly to matching sub-skill. Do not run this flow.

Full flow (for unclear or exploratory requests)

1. Workspace Scan (always run first, silently)

Scan the user's workspace for: language/framework, compute/deployment model, networking/region, cache commands, and existing caching solutions. If workspace has docker-compose with a Redis image, redis.conf, .env with Redis URLs, or ElastiCache in existing IaC, route to migration. Map discovered commands to patterns:

Commands in codeLikely pattern
INCR, EXPIRE, DECRRate limiting or counters
HSET, HGET, HMSET, HMGETSessions, carts, or profile/state
ZADD, ZRANGE, ZRANGEBYSCORELeaderboards or ranking
XADD, XREADGROUP, XACKStreams or durable queues
PUBLISH, SUBSCRIBEReal-time messaging / pub/sub
GET, SET, SETEX with serialized valuesCache-aside / query caching
Embedding generation, vector similarityGenAI (route to genai)
2. Present Inferences

Present what you found in a single confirmation block with evidence from file paths.

Inference safety hierarchy:

  • Low risk (use as default, mention but don't ask): Language, framework, region

  • Medium risk (present and ask to confirm): Use case pattern, deployment model

  • High risk (always confirm explicitly): Engine choice, security posture, multi-region

If the workspace scan reveals nothing, skip to step 3.

3. Evaluate fit

If caching with ElastiCache is clearly the right approach, skip to step 4.

If the user is unsure, evaluate:

When ElastiCache is NOT the right fit:

SymptomBetter alternativeCan ElastiCache complement it?
Need complex queries, joins, ACID transactionsRDS, AuroraYes, as a read cache in front of it
Need a DynamoDB-specific transparent cacheDAXYes, as a general cross-service cache alongside DAX
Need CDN / static content deliveryCloudFrontYes, as an app-layer cache behind CloudFront
Need a durable message queue with exactly-once deliverySQSYes, for rate limiting in front of SQS
Need an event bus with routing rulesEventBridgeNo
Need search over massive archival datasetsOpenSearchYes, as a fast real-time layer alongside OpenSearch
Slow DB reads but every query is uniqueDB indexing, read replicas, query optimizationNo
Need a durable primary database with Redis/Valkey API compatibilityMemoryDBNo, use MemoryDB instead of ElastiCache when the workload requires a durable primary database with microsecond reads and single-digit ms writes

Common "use both" patterns:

  • ElastiCache + RDS/Aurora: Cache-aside for read acceleration

  • ElastiCache + DynamoDB: General cross-service cache layer

  • ElastiCache + Bedrock: Semantic cache to reduce LLM cost and latency

  • ElastiCache + OpenSearch: Fast real-time search layer with OpenSearch for archival analytics

  • ElastiCache + SQS: Rate limiting and deduplication in front of a queue

Shared or application-local? (only when the role is cache layer)

FactorApplication-local sufficientShared (ElastiCache) needed
Multiple app instances need the same dataNoYes
Data must survive app restartsNoYes
Atomic operations across instancesNoYes
Rich data structures (sorted sets, streams, hashes)NoYes
Single instance, simple memoizationYesOverkill

If application-local is sufficient, recommend it and stop.

4. Route or ask

If the scan + user's message reveal the job, route immediately. Otherwise ask remaining questions 2-3 at a time, skipping anything the scan already answered.

Questions (only what remains unknown):

CategoryQuestion
GoalBuild new, migrate existing, or troubleshoot?
Data sourceWhat is your primary data store?
Multi-regionSingle-region or multi-region?
AI workloadWorking with embeddings, LLM inference, or agent memory?
ComplianceAny regulatory requirements (HIPAA, PCI DSS, SOC 2, FedRAMP)?
TrafficExpected request rate? Steady or spiky?
ConnectionsConcurrent connections?
Data sizeGB of cached data?
LatencySub-millisecond critical, or single-digit ms acceptable?
StalenessHow stale can the data be?
BudgetCost sensitive?

Signal-to-route mapping:

User signalRoute to
Slow database readssetup + data-modeling (cache-aside)
Session storage across instancessetup + data-modeling (session)
Real-time rankings or scoringsetup + data-modeling (leaderboard)
API protection or throttlingsetup + data-modeling (rate limiter)
Reducing LLM costs or latencysetup + genai (semantic cache); server-side path requires Valkey 8.2 or above, app-side works on any version
AI agent memory across sessionssetup + genai (conversational memory), requires Valkey 8.2+
Search or recommendationssetup + genai (vector search / RAG), requires Valkey 8.2+ (node-based clusters only)
Real-time event distributionsetup + data-modeling (pub/sub or streams)

Compliance-to-configuration mapping:

If the user mentions a compliance framework, these ElastiCache settings are non-negotiable:

FrameworkRequired configuration
HIPAAIn-transit encryption (TLS), at-rest encryption, RBAC or IAM auth, Multi-AZ, slow log + engine log delivery enabled, CloudTrail API logging, VPC-only access
PCI DSSIn-transit encryption (TLS), at-rest encryption, RBAC or IAM auth, VPC-only access, no public endpoints, slow log + engine log delivery enabled, CloudTrail API logging, key rotation via Secrets Manager
SOC 2In-transit encryption (TLS), at-rest encryption, RBAC or IAM auth, CloudTrail enabled, security-relevant metric alarms (e.g., AuthenticationFailures, NewConnections) + CloudTrail
FedRAMPAll HIPAA requirements plus: GovCloud region, FIPS endpoints, Config rules for drift detection

When compliance is flagged, pass the requirement to setup so it enforces encryption and auth from the start. At-rest encryption cannot be added after cluster creation; getting this wrong requires a full cluster recreation.

AWS-stack translation table:

AWS contextDefault ElastiCache choice
RDS or Aurora read-heavy appQuery caching / cache-aside
DynamoDB read-heavy appElastiCache cache-aside. Mention DAX only if user wants DynamoDB-specific transparent cache.
LambdaVPC connectivity required. Default serverless unless node-based requirement applies.
ECS, EKS, or EC2Pick pattern per signal above.
Bedrock or AgentCoreRoute to genai, classify Mode 1/2/3.
Global app footprint (multi-region)Node-based with Global Datastore. Limited to specific instance families (M5, M6g, M7g, R5, R6g, R6gd, R7g, C7gn in size large+), max 2 secondary regions, no autofailover across regions (manual promotion only), no IPv6, no Local Zones.

Summarize and hand off

Summarize what you learned. Mark inferred values as "(inferred)" so the user can correct them.

Save requirements artifact

After the user confirms, write to .elasticache/requirements.json in the project root. Create .elasticache/ if needed. Use null for undetermined values. If the file exists with an infrastructure section, preserve it.

{
  "use_case": "cache-aside for Aurora read acceleration",
  "patterns": ["cache-aside"],
  "runtime": { "language": "python", "framework": "fastapi", "compute": "ecs" },
  "region": "us-east-1",
  "engine": "valkey",
  "deployment_model": null,
  "data_source": "aurora-postgresql",
  "multi_region": false,
  "ai_workload": false,
  "next_steps": ["setup", "data-modeling"],
  "infrastructure": null
}
Hand off

  1. setup to create and connect

  2. data-modeling or genai to implement the pattern

If the user's request spans multiple patterns, note that both can run on the same cache and plan the data model accordingly.

Freshness disclaimer

When your response includes pricing, version constraints, or feature availability, include the freshness disclaimer per SKILL.md Global Rule #5: "For current pricing see https://aws.amazon.com/elasticache/pricing/. For current feature availability see https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/."

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python, Needs Node.js

Security

What our scanner noticed· Medium risk

  • References to credential files
  • Instructions that use the network
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
AWS
Publisher
AWS (Official)
Source
aws/agent-toolkit-for-aws · skills/specialized-skills/database-skills/amazon-elasticache · commit 0a218f8
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • NOTICE (added by AutoGineer from NOTICE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download amazon-elasticache.zip