Content
79%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted skill that efficiently covers algorithm selection, core usage, capacity sizing, and common pitfalls for samber/hot. Its strengths are excellent conciseness, fully executable code examples, and a highly actionable algorithm selection table. The main weaknesses are the absence of an explicit adoption/integration workflow with validation checkpoints, and the referenced bundle files (algorithm-guide.md, production-patterns.md, api-reference.md) not being provided.
Suggestions
Add an explicit adoption workflow section with steps like: 1. Profile access patterns → 2. Select algorithm → 3. Size cache from memory budget → 4. Configure with TTL/jitter/janitor → 5. Deploy with Prometheus metrics → 6. Validate hit rate > 80% → 7. Tune algorithm/capacity if needed.
Provide the referenced bundle files (references/algorithm-guide.md, references/production-patterns.md, references/api-reference.md) to fulfill the progressive disclosure promises made in the skill body.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | 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. The capacity sizing section could be slightly tighter but the worked example justifies its inclusion. | 3 / 3 |
Actionability | Provides fully executable Go code for basic cache setup, TTL, and loader patterns. The algorithm selection table gives concrete constants and clear guidance. Common mistakes list specific failure modes (panics, OOM) with exact causes. The capacity sizing section includes a concrete calculation example. | 3 / 3 |
Workflow Clarity | The capacity sizing section has a clear 3-step process, and common mistakes serve as implicit validation checkpoints. However, there's no explicit workflow for adopting/integrating the cache (e.g., measure → choose algorithm → size → configure → monitor → tune), and no feedback loop for validating that the cache is working correctly after setup. For a library that can panic at runtime on misconfiguration, explicit validation steps would be valuable. | 2 / 3 |
Progressive Disclosure | References to algorithm-guide.md, production-patterns.md, and api-reference.md are well-signaled and one level deep. However, no bundle files were provided, so these references point to non-existent files, undermining the progressive disclosure structure. The cross-references to other skills are a nice touch but the core referenced files are missing. | 2 / 3 |
Total | 10 / 12 Passed |