You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.
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Guide changes through code checks, testing, commits, and pull request preparation. Use when coordinating a complete Git workflow and verifying readiness for collaboration.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern git best practices including Conventional Commits, automated testing, and structured PR creation.
[Extended thinking: This workflow coordinates multiple specialized agents to ensure code quality before commits are made. The code-reviewer agent performs initial quality checks, test-automator ensures all tests pass, and deployment-engineer verifies production readiness. By orchestrating these agents sequentially with context passing, we prevent broken code from entering the repository while maintaining high velocity. The workflow supports both trunk-based and feature-branch strategies with configurable options for different team needs.]
Target branch: $ARGUMENTS (defaults to 'main' if not specified)
Supported flags:
--skip-tests: Skip automated test execution (use with caution)--draft-pr: Create PR as draft for work-in-progress--no-push: Perform all checks but don't push to remote--squash: Squash commits before pushing--conventional: Enforce Conventional Commits format strictly--trunk-based: Use trunk-based development workflow--feature-branch: Use feature branch workflow (default)In case of issues after merge:
git revert <commit-hash>(feature|bugfix|hotfix|docs|chore)/<ticket-id>-<brief-description>还没有评价。欢迎分享你的使用感受。
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You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.
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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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Migrate from AngularJS to Angular using hybrid mode, incremental component rewriting, and dependency injection updates. Use when upgrading AngularJS applications, planning framework migrations, or modernizing legacy Angular code.
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