code-review-orchestrator
Delegate specialized reviews to subagents and synthesize their findings. Use when reviewing code.
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Delegate specialized reviews to subagents and synthesize their findings. Use when reviewing code.
Use when building multi-day features, avoiding long-lived branches, features taking >1 day, changes touching multiple systems, or high-risk changes needing gradual rollout - break features into deployable incre...
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before as...
Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared state or dependencies - dispatches fresh subagent fo...
AI agent follows implementation plans systematically with progress tracking, quality gates, and blocker resolution. Use when implementing features, following plans, or executing tasks.
Docker containerization with best practices for builds, compose, and production deployment
Orchestrate autonomous project development through state-driven execution
Model deployment strategies including serving infrastructure, containerization, model packaging, versioning, and production deployment patterns.
The agent implements GitHub Actions CI/CD workflows with builds, tests, and deployments. Use when setting up continuous integration, automating deployments, creating reusable actions, or implementing security s...
ML development workflow covering experiment design, baseline establishment, iterative improvement, and experiment tracking best practices.
AI agent requests effective code reviews through proper preparation, clear descriptions, and appropriate reviewer selection. Use when creating PRs, requesting feedback, or submitting for review.
AI agent validates work through evidence-based verification with comprehensive checklists, automated validation, and proof-of-work documentation. Use when completing tasks, preparing for review, or ensuring qua...
Protecting AI applications - input/output guards, toxicity detection, PII protection, injection defense, constitutional AI. Use when securing AI systems, preventing misuse, or ensuring compliance.
AI agent practices test-first development with the Red-Green-Refactor cycle for confident, well-designed code. Use when implementing features, fixing bugs, or establishing testing practices.
The agent implements feature flag systems for trunk-based development, canary releases, and A/B testing. Use when implementing gradual rollouts, kill switches, or experiment-driven development.
Consumer-driven contract testing with Pact, schema validation, provider verification, and CI/CD integration.
The agent implements a centralized workflow configuration system for Git workflows, enabling set-once-use-everywhere automation for trunk-based development, gitflow, and github-flow patterns with integrated tes...
AI agent identifies and fixes common testing anti-patterns that lead to flaky, slow, or unmaintainable test suites. Use when reviewing tests, debugging test failures, or improving test quality.
The agent implements Kubernetes container orchestration with deployments, services, Helm charts, and production patterns. Use when deploying containerized applications, configuring autoscaling, managing secrets...
AI agent completes development branches with comprehensive quality gates, clean git history, and thorough PR preparation. Use when wrapping up features, preparing for merge, or finalizing PRs.
Enterprise monorepo patterns with Turborepo and Nx including task orchestration, caching, and CI/CD optimization
Managing LangGraph Cloud deployments through listing, filtering, and cleanup operations. Use when managing test deployments, cleaning up orphaned resources, filtering by name patterns, or handling bulk deletion...
1800+ lines of DevOps mastery - CI/CD, code signing, app store, monitoring, Fastlane with production-ready code examples.
Manage Git branches, commits, and pull requests following best practices
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.