Lists multiple specific concrete capabilities: eviction algorithms (with 8 named variants), TTL, cache loaders, sharding, stale-while-revalidate, missing key caching, and Prometheus metrics. Highly detailed and actionable.

Clearly answers both 'what' (in-memory caching with specific features) and 'when' with explicit triggers: 'Apply when using or adopting samber/hot, when the codebase imports github.com/samber/hot, or when the project repeatedly loads the same medium-to-low cardinality resources at high frequency and needs to reduce latency or backend pressure.'

Includes excellent natural trigger terms: 'caching', 'Golang', 'samber/hot', 'LRU', 'LFU', 'TTL', 'eviction', 'sharding', 'stale-while-revalidate', 'Prometheus metrics', and the full import path 'github.com/samber/hot'. These cover both library-specific and general caching terminology users would naturally use.

Highly distinctive — targets a specific Go library (samber/hot) with a named import path, specific eviction algorithms, and a clear niche in Go in-memory caching. Very unlikely to conflict with other skills unless another skill also covers this exact library.

The content is lean and efficient throughout. It assumes Claude's Go competence, avoids explaining what caching is or how Go generics work, and every section delivers actionable information. The algorithm table is dense but earns its space by being a decision tool rather than a tutorial.

Provides fully executable Go code for basic cache setup, loader pattern, and TTL configuration. The capacity sizing section gives a concrete formula with a worked example. Common mistakes list specific failure modes (panics, OOM) with exact causes.

The capacity sizing section has a clear 3-step process, and the common mistakes section serves as implicit validation checkpoints. However, there's no explicit workflow for adopting/integrating the cache into a project with validation steps (e.g., verify hit rate after deployment, load test before production). The skill covers multiple concerns but doesn't sequence them into a clear adoption workflow with feedback loops.

The skill references three separate files (algorithm-guide.md, production-patterns.md, api-reference.md) with clear signaling, which is good structure. However, no bundle files were provided, so these references cannot be verified. The cross-references section at the bottom is well-organized. The main content itself is appropriately scoped as an overview, though the common mistakes and best practices sections could arguably be in a reference file to keep the main skill leaner.