DevOps & Deployment for Claude Code

Drives one agent-harness loop iteration to completion — reads the plan and state files, executes exactly one task with the task skill's own tools, lets the controller verify it, and reports the directive. Use when a goal has been compiled into an agent-harness plan and tasks need executing ("run the next harness task", "drive this loop until it escalates or closes"). Use PROACTIVELY after goal_compiler.py writes a plan. NOT for compiling goals (main session does that), authoring workflows (cs-wo

Coordinator for AgentHub multi-agent collaboration sessions. Dispatches N parallel subagents in isolated git worktrees via the Agent tool, monitors. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

Analyzes Cypress or Selenium test suites and creates a file-by-file migration plan. Invoked by /pw:migrate before conversion starts.. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

P10 CTO/架构委员会 Agent。定义技术战略方向、组织 agent 团队拓扑、建设基础能力。当面对超大型项目（5+ agents, 3+ sprints）、需要战略级架构决策、或需要跨多个 P9 协调时使用。触发词：CTO 模式、P10、战略规划、架构委员会、组织设计、定义技术方向。

只读边界检查 Agent：在改动测试、CI、状态、发布或权限配置前，提醒需要用户确认和证据说明；不执行实现。

FastLED CI/Build System Agent Guidelines

Diagnoses and fixes PlatformIO board upload/monitor issues automatically

FastLED Testing Agent Guidelines

Work from the pasted data only.

Work from the pasted data only.

Performs preflight validation, config merging, TU enumeration, and work directory setup for zeroize-audit. Produces merged-config.yaml, preflight.json, and orchestrator-state.json.

Compiles and runs all PoCs for zeroize-audit findings. Produces poc_validation_results.json consumed by the verification agent and the orchestrator.

Generates runtime validation test harnesses (C tests, MSAN, Valgrind targets) for confirmed zeroize-audit findings. Produces a Makefile for automated test execution.

Use when browsing, searching, installing, or removing Claude Code agents from the awesome-claude-code-subagents community collection.

Use when working on AI system design, model implementation, and production deployment, including multiple AI frameworks and tools, with emphasis on building scalable, efficient, and ethical AI solutions from research to production.

Use when working on build system optimization, compilation strategies, and developer productivity, including modern build tools, caching mechanisms, and creating fast, reliable build pipelines that scale with team growth.

Use when working on multi-cloud strategies, scalable architectures, and cost-effective solutions, including AWS, Azure, and GCP, with emphasis on security, performance, and compliance while designing resilient cloud-native systems.

Use when working on modern .NET development, ASP.NET Core, and cloud-native applications, including C# 12 features, Blazor, and cross-platform development with emphasis on performance and clean architecture.

Use when building scalable data pipelines, ETL/ELT processes, and data infrastructure, including big data technologies and cloud platforms, with emphasis on reliable, efficient, and cost-optimized data platforms.

Use when working on high-availability systems, performance optimization, and disaster recovery, including PostgreSQL, MySQL, MongoDB, and Redis, with emphasis on reliability, scalability, and operational excellence.

Use when working on package management, security auditing, and version conflict resolution across multiple ecosystems, including dependency optimization, supply chain security, and automated updates, with emphasis on maintaining stable, secure, and efficient dependency trees.

Use when working on CI/CD pipelines, release automation, and deployment strategies, including blue-green, canary, and rolling deployments, with emphasis on zero-downtime releases and rapid rollback capabilities.

Use when bridging development and operations with comprehensive automation, monitoring, and infrastructure management, including CI/CD, containerization, and cloud platforms, with emphasis on culture, collaboration, and continuous improvement.

Use when working on rapid detection, diagnosis, and resolution of production issues, including observability tools, root cause analysis, and automated remediation, with emphasis on minimizing downtime and preventing recurrence.

Use when working on .NET Core, .NET 10, modern C#, minimal APIs, cross-platform services, cloud-native applications, and high-performance microservices.

Use when working on build performance, tooling efficiency, and workflow automation, including development environment optimization, with emphasis on reducing friction, accelerating feedback loops, and maximizing developer productivity and satisfaction.

Use when working on high-performance systems, concurrent programming, and cloud-native microservices, including idiomatic Go patterns with emphasis on simplicity, efficiency, and reliability.

Use when routing tasks across PowerShell, .NET, infrastructure, Azure, and M365 subagents. Prefers PowerShell-based automation as the default implementation language.

Use when working on enterprise-grade applications, Spring ecosystem, and cloud-native development, including modern Java features, reactive programming, and microservices patterns, with emphasis on scalability and maintainability.

Use when working on container orchestration, cluster management, and cloud-native architectures, including production-grade deployments, security hardening, and performance optimization, with emphasis on scalability and reliability.

Use when working on Laravel 10+ with modern PHP practices, including elegant syntax, Eloquent ORM, queue systems, and enterprise features, with emphasis on building scalable web applications and APIs.

Use when working on large language model architecture, deployment, and optimization, including LLM system design, fine-tuning strategies, and production serving, with emphasis on building scalable, efficient, and safe LLM applications.

Use when working on Exchange Online, Teams, SharePoint, licensing, Graph API automation, and secure identity operations.

Use when working on production model deployment, serving infrastructure, and scalable ML systems, including model optimization, real-time inference, and edge deployment, with emphasis on reliability and performance at scale.

Use when designing scalable microservice ecosystems, including service boundaries, communication patterns, and operational excellence in cloud-native environments.

Use when working on machine learning model lifecycle, production deployment, and ML system optimization, including both traditional ML and deep learning, with emphasis on building scalable, reliable ML systems from training to serving.

Use when working on ML infrastructure, platform engineering, and operational excellence for machine learning systems, including CI/CD for ML, model versioning, and scalable ML platforms, with emphasis on reliability and automation.

Use when working on cloud and hybrid network architectures, security, and performance optimization, including network design, troubleshooting, and automation, with emphasis on reliability, scalability, and zero-trust principles.

Use when working on internal developer platforms, self-service infrastructure, and developer experience, including platform APIs, GitOps workflows, and golden path templates, with emphasis on empowering developers and accelerating delivery.

Use when working on database administration, performance optimization, and high availability. Deep expertise in PostgreSQL internals, advanced features, and enterprise deployment, with emphasis on reliability and peak performance.