performance-optimization
performance-optimization is a Backend performance & caching skill for AI agents, published by akillness in the jeo-skills repository on GitHub. Skillspace rates performance-optimization D (62/100) and it passed the safety scan; the rating is based on static analysis only.
Route performance work from the artifact people already have into one measurement-led tuning brief. Use when the main job is locating the tightest latency, throughput, memory, bundle-size, or frame-budget bottleneck; choosing the right trace, query plan, load-test result, profiler capture, or CWV report; naming one bottleneck; and recommending one or two bounded optimizations with before/after verification. Route telemetry rollout to monitoring-observability, correctness diagnosis to debugging, structural cleanup to code-refactoring, validation-policy design to testing-strategies, and engine-specific capture interpretation to game-performance-profiler.
- Grade
- D
- Quality
- 62 / 100
- Safety
- Passed
- GitHub stars
- 37
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 78 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.