skill-code-review
Multi-LLM code review pipeline using consensus-based analysis to detect security, architectural, and quality issues.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
360 skills found
Multi-LLM code review pipeline using consensus-based analysis to detect security, architectural, and quality issues.
Automated migration guide for Kotlin Multiplatform (KMP) projects upgrading to Android Gradle Plugin (AGP) 9.0+, covering plugin replacement, DSL updates, and project structure restructuring.
A specialized Git assistant for executing safe interactive rebases, managing commit history, and resolving merge conflicts with automated safety backups.
Manage Fly.io edge infrastructure: deploy apps, scale machines, configure volumes, secrets, and networking via the Fly.io Machines API. Python-based, zero-dependency.
Unit and integration test your Encore.ts backend applications using Vitest, including support for isolated test databases and service mocking.
A structured development process for Python projects using TDD, the uv package manager, and automated testing workflows.
Implement secure, tokenless npm publishing in GitHub Actions using OIDC, provenance attestations, and monorepo-friendly configuration.
Tools for deploying, managing, and monitoring DataRobot models, including prediction environment configuration, champion/challenger workflows, and deployment operations.
Master design system architecture: implement design tokens, multi-brand theming, component libraries, and automated design-to-code pipelines for scalable UI foundations.
A deep reasoning protocol that ensures systematic analysis, multi-hypothesis generation, and rigorous verification for complex architectural, debugging, and high-stakes tasks.
Expert CLI guides for AI agents, featuring senior engineer workflows, safety guardrails, and operational patterns for cloud, IaC, containers, databases, and dev tools.
Generate professional pull request descriptions using Grey Haven Studio standards, ensuring clear summaries, motivation, technical implementation details, and testing strategies.