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8.475 skills encontradas
git-pr-workflows-pr-enhance
You are a PR optimization expert specializing in creating high-quality pull requests that facilitate efficient code reviews. Generate comprehensive PR descriptions, automate review processes, and ensu
makepad-deployment
CRITICAL: Use for Makepad packaging and deployment. Triggers on: deploy, package, APK, IPA, 打包, 部署, cargo-packager, cargo-makepad, WASM, Android, iOS, distribution, installer, .deb, .dmg, .nsis, GitHub Actions, CI, action, marketplace
gitlab-ci-patterns
Comprehensive GitLab CI/CD pipeline patterns for automated testing, building, and deployment.
gh-review-requests
Fetch unread GitHub notifications for open PRs where review is requested from a specified team or opened by a team member. Use when asked to "find PRs I need to review", "show my review requests", "what needs my review", "fetch GitHub review requests", or "check team review queue".
address-github-comments
Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
app-store-changelog
Generate user-facing App Store release notes from git history since the last tag.
changelog-generator
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
conductor-revert
Git-aware undo by logical work unit (track, phase, or task)
it-manager-pro
Elite IT Management Advisor specializing in data-driven strategy, executive communication, and human-centric leadership for the 2026 digital era.
devops-deploy
DevOps e deploy de aplicacoes — Docker, CI/CD com GitHub Actions, AWS Lambda, SAM, Terraform, infraestrutura como codigo e monitoramento.
codebase-audit-pre-push
Deep audit before GitHub push: removes junk files, dead code, security holes, and optimization issues. Checks every file line-by-line for production readiness.
llm-app-patterns
Production-ready patterns for building LLM applications, inspired by [Dify](https://github.com/langgenius/dify) and industry best practices.