Explore skills

Apply the modernization changes from analysis.md (produced by java-modernization-review) to create a Java 21 version of the reviewed file. Only run after the user has reviewed and approved analysis.md.

Creates a brief, pyramid-structured wrap-up report for completed work. Use it when the user asks to wrap up work, summarize findings, produce a handoff, explain what changed, or report what remains open.

Deployment strategies, release safety, health checks, and rollback. Use when designing or documenting deploy/rollback, CI/CD, or production readiness. Applies to any stack or platform.

You are a workflow automation expert specializing in creating efficient CI/CD pipelines, GitHub Actions workflows, and automated development processes. Design automation that reduces manual work, improves consistency, and accelerates delivery while maintaining quality and security.

This skill allows the agent to perform an Open Source Health Check on the current project. It analyzes the technology stack, programming language distribution, and open-source licenses of dependencies.

Use for analyzing TYPO3 Forge issues, submitting patches to Gerrit, contributing core bug fixes, documentation, cherry-pick workflows, or CI debugging. The core main branch is v15-in-progress (PHP 8.4/8.5); v14.3 LTS bugfixes are backported from branch 14.3 via cherry-pick.

Elite code review expert specializing in modern AI-powered code

Use when deploying, configuring, integrating, or troubleshooting GlitchTip, including self-hosted installation, SDK setup, source maps, sentry-cli, uptime monitoring, alerting, environment variables, Docker Compose, Helm, social auth, and migration from Sentry.

Use when building, configuring, deploying, or troubleshooting ZeroClaw AI agent infrastructure, including provider setup, channel binding, memory backends, config.toml authoring, CLI usage, Docker/native runtime, and migration from other agent frameworks.

This concise communication mode reduces output tokens by 60-70% by removing filler, hedging, and pleasantries, while maintaining natural, readable English. It can elaborate on request and is activated by specific user commands or requests for token efficiency.

This concise communication mode reduces output tokens by 60-70% while maintaining natural, readable English. It removes filler and pleasantries, but preserves grammar and flow, elaborating only upon request.

This concise communication mode reduces output tokens by 60-70% while maintaining natural, readable English, dropping filler and pleasantries. It elaborates on request and is triggered by specific user commands or requests for token efficiency.