performance-optimization
Optimize performance for memory, CPU, network, and rendering. Use when improving FPS, reducing memory usage, or fixing performance bottlenecks.
Browse published AI skills maintained by lovedragonball. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
34Repository stars across indexed skills
1,088Repository forks
170Average repository stars per indexed skill
32Repository stars may repeat across skills from the same repository.
Optimize performance for memory, CPU, network, and rendering. Use when improving FPS, reducing memory usage, or fixing performance bottlenecks.
Build Chrome Extensions with Manifest V3, background service workers, content scripts, and message passing. Use when developing TikTok Uploader extension or any browser extensions.
Implement auto-retry, resume mechanisms, and graceful error recovery. Use when building resilient systems like TikTok Uploader that need to handle failures gracefully.
Build Electron desktop applications with IPC, window management, and packaging. Use when working on TitanMirror desktop app or any cross-platform desktop applications.
Automated CI/CD pipeline setup with GitHub Actions, deployment strategies, and automation workflows. Use for build automation, testing, and deployment.
Write and run tests including unit tests, integration tests, and E2E tests. Use when ensuring code quality, writing test cases, or setting up testing frameworks.
Real-time communication with Socket.IO including connection management, events, rooms, and error handling. Use when working on TitanMirror or any real-time applications.
Modernize legacy applications and codebases. Use for COBOL conversion, framework upgrades, and technical debt reduction.
Design RESTful APIs with proper routes, validation, error handling, and documentation. Use when building backend services for PSI Engine or other server applications.
Nx, Turborepo, and Lerna workflows for monorepo management. Use for large-scale projects with multiple packages.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.