What is amazon-elasticache?

A toolkit for Amazon ElastiCache, AWS's managed in-memory data store service for Redis, Valkey, and Memcached.

Use it to plan or troubleshoot caches, choose an in-memory engine, design cache-aside patterns, and manage sessions or frequently read data.

It helps address slow responses, database read load, throttling, scaling pressure, and the cost of repeated data lookups by guiding cache design and setup.

amazon-elasticache — Skill for Claude Code and Codex by aws

About the project

Agent Toolkit for AWS is a collection of AWS-supported MCP servers, skills, plugins, commands, and hooks that help AI coding agents build, deploy, and manage applications on AWS. It is used by developers working with AWS services through agents such as Claude Code, Codex, Cursor, and Kiro. The catalogue entries are the toolkit's own agent extensions for AWS development and operations.

aws/agent-toolkit-for-aws · 2,886 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add aws/agent-toolkit-for-aws --skill amazon-elasticache

Clone the repo

git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/amazon-elasticache/github.svg)](https://agentmods.dev/skills/aws/agent-toolkit-for-aws/amazon-elasticache)

Your own site

<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/amazon-elasticache"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/amazon-elasticache/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/amazon-elasticache"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/amazon-elasticache.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 222 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,158 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

Socket pass 24 Jul 2026
Snyk pass 24 Jul 2026
NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Anti-Refusal · line 73
Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.

Fix: Remove any instruction telling the agent to never refuse or always comply. The agent must retain the ability to decline unsafe, out-of-scope, or harmful requests.
medium Excessive Agency · line 98
Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00222	$0.04158
Opus 5.5	$0.00089	$0.01663
Sonnet 5.5	$0.00044	$0.00832
Haiku 4.5	$0.00022	$0.00416

Measured 1mo ago against content hash 1cfa793c277f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

amazon-elasticache scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/specialized-skills/database-skills/amazon-elasticache/SKILL.md · 136 lines

How it starts

The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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-modeling even without the word "pattern").
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?"
Check the Guardrails section before recommending an engine or deployment model.
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.
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 requirements first.
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`

Read the full file on GitHub · 136 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 136 lines · 222 tokens per session scan A 1cfa793c277f

Subscribe to this mod's changes

amazon-elasticache is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,886 stars, last pushed yesterday), licensed Apache-2.0. It adds 222 tokens to every session and 4,158 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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