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.

Quality

90%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

85%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-engineered router skill: clear sequenced workflow with validation checkpoints and excellent progressive disclosure via verified on-demand references. Minor conciseness and inline-code gains are available by trimming the registry table and product-truths list.

Suggestions

Trim the Product truths list and the registry table's longer cells to the highest-signal facts, pushing the rest into the relevant sub-skill reference files.

Add one or two short inline code snippets (e.g., a minimal valkey-py lazy-init client) in the body so the most common action is copy-paste ready without loading a reference.

Dimension	Reasoning	Score
Conciseness	The body is dense and reference-anchored rather than padded, but the six-row sub-skill registry table and the multi-section 'Product truths' list carry detail that is slightly heavier than a lean overview needs to be.	4 / 5
Actionability	Concrete scripts (input_validator.py, price_calculator.py), exact CLI tag examples, and explicit file paths make guidance executable; actual code is deferred to references, which is justified but keeps it just below fully copy-paste-ready.	4 / 5
Workflow Clarity	A numbered eight-step routing process, ordered disambiguation rules, an input-validation gate before write operations, and an explicit 'show the error and suggest a fix before retrying' feedback loop match the clear-sequence-with-validation anchor.	5 / 5
Progressive Disclosure	SKILL.md is a navigable overview with on-demand, one-level-deep pointers (all verified to exist) and a stated folder convention; content is appropriately split across the 10 reference folders with clear signaling.	5 / 5
	Total	18 / 20 Passed

Description

96%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exceptionally thorough description that explicitly pairs concrete capabilities with natural user trigger phrases and a clear negative-trigger boundary. Its only weakness is length-driven overlap risk with broader Redis/Valkey tooling.

Suggestions

Tighten the description by consolidating the long trigger enumeration; the same coverage could be conveyed with fewer clauses to reduce token weight and overlap with generic Redis skills.

Consider foregrounding the single highest-signal trigger ('AWS ElastiCache provisioning, operations, or migration') earlier so the skill's niche is immediately apparent.

Dimension	Reasoning	Score
Specificity	Lists many concrete capabilities across caching strategies (cache-aside, session stores, rate limiting, leaderboards), GenAI/ML retrieval (vector similarity search, semantic caching, RAG), and ElastiCache lifecycle (provisioning, TLS, RBAC, IAM auth, Global Datastore, migration), matching the comprehensive-coverage anchor.	5 / 5
Completeness	Explicitly answers both what it does and when to use it via repeated 'Activate when'/'Activate for' clauses with concrete trigger phrases, and adds a 'Do not trigger for' exclusion clause, matching the top anchor.	5 / 5
Trigger Term Quality	Comprehensive natural trigger phrases including synonyms and specific pain points users say ('slow API responses', 'DynamoDB throttling', 'RDS/Aurora scaling pressure', 'Bedrock latency or cost') plus engine names and pattern names, matching the comprehensive-synonyms anchor.	5 / 5
Distinctiveness Conflict Risk	A clear ElastiCache niche with explicit negative boundary ('Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches'); minor overlap risk with self-managed Redis/Valkey skills keeps it just below 5.	4 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
referenced_paths_exist	Referenced path issues: 16 deeper-than-1-level	Warning

	Total	14 / 16 Passed

Repository: aws/agent-toolkit-for-aws
Path: skills/specialized-skills/database-skills/amazon-elasticache/SKILL.md
Commit: 8ea26bd

Reviewed: 4 days ago

Table of Contents

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