Content
77%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-structured performance optimization skill that excels at workflow methodology and decision-making frameworks. The decision tree mapping pprof signals to optimization strategies is particularly valuable. The main weakness is that the core optimization patterns (the 'if X bottleneck, then apply Y' content promised in the description) are entirely delegated to reference files that weren't provided in the bundle, making the main skill more of a routing document than a self-contained guide. The actionability would benefit from at least one concrete before/after code example inline.
Suggestions
Include at least 1-2 inline before/after code examples for the highest-ROI optimization (e.g., allocation reduction with sync.Pool or slice preallocation) so the skill is actionable even without reference files.
Provide the bundle reference files (references/memory.md, references/cpu.md, etc.) so the progressive disclosure structure can be fully evaluated and the skill delivers on its promised optimization patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It avoids explaining what Go, GC, or pprof are. Every section delivers actionable patterns or decision tables without padding. The philosophy section is brief and justified by the optimization context. | 3 / 3 |
Actionability | The iterative methodology provides concrete commands (benchstat, go test -bench flags), and the decision tree maps signals to actions. However, the actual optimization patterns are delegated to reference files, and the main skill lacks executable code examples showing before/after optimization patterns. The common mistakes table is actionable but mostly declarative. | 2 / 3 |
Workflow Clarity | The iterative optimization cycle is clearly sequenced with 8 explicit steps including baseline measurement, single-change discipline, statistical comparison via benchstat, and a repeat loop. The 'Rule Out External Bottlenecks First' section adds a validation checkpoint before even starting optimization. The decision tree provides clear routing from diagnosis to action. | 3 / 3 |
Progressive Disclosure | The skill has excellent structure with a clear overview, decision tree routing to deep dives, and well-signaled references to 6 reference files and multiple cross-referenced skills. However, no bundle files were provided, so the referenced files (references/memory.md, references/cpu.md, etc.) cannot be verified to exist, and the actual optimization patterns that are the core value of this skill are entirely in those unverifiable references. | 2 / 3 |
Total | 10 / 12 Passed |