Imported from aws/agent-toolkit-for-aws (skills/specialized-skills/database-skills/amazon-elasticache/SKILL.md) via skills.sh. Install upstream with npx skills add aws/agent-toolkit-for-aws --skill amazon-elasticache. Copyright stays with the author.

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.

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.

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

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.