「engineer」の検索結果

Systematically fix all failing tests after business logic changes or refactoring

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive guide for creating Claude Code agents with proper structure, triggering conditions, system prompts, and validation - combines official Anthropic best practices with proven patterns

日本語の概要は準備中です。原文の説明を表示しています。

Review an existing GitHub pull request and post inline review comments on its diff. Use when the changes are on an opened PR rather than your local working tree.

日本語の概要は準備中です。原文の説明を表示しています。

Create pull requests using GitHub CLI with proper templates and formatting

日本語の概要は準備中です。原文の説明を表示しています。

Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Use when tasks do not depend on each other and can run side by side.

日本語の概要は準備中です。原文の説明を表示しています。

Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Comprehensive multi-perspective review using specialized judges with debate and consensus building

日本語の概要は準備中です。原文の説明を表示しています。

Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification

日本語の概要は準備中です。原文の説明を表示しています。

creates draft task file in .specs/tasks/draft/ with original user intent

日本語の概要は準備中です。原文の説明を表示しています。

Add line-specific review comments to pull requests using GitHub CLI API

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Generate ideas in one shot using creative sampling

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Use when found gap or repetative issue, that produced by you or implemenataion agent. Esentially use it each time when you say "You absolutly right, I should have done it differently." -> need create rule for this issue so it not appears again.

日本語の概要は準備中です。原文の説明を表示しています。

Load all open issues from GitHub and save them as markdown files

日本語の概要は準備中です。原文の説明を表示しています。

Use when creating or developing, before writing code or implementation plans - refines rough ideas into fully-formed designs through collaborative questioning, alternative exploration, and incremental validation. Don't use during clear 'mechanical' processes

日本語の概要は準備中です。原文の説明を表示しています。

Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop

日本語の概要は準備中です。原文の説明を表示しています。

Prepare and manage durable MEX team handoffs. Use when the user asks to hand work to a person or team, prepare an end-of-session handoff, save what the next engineer needs, create a MEX Relay, take or close a Relay, or explicitly invokes /mex-relay or $mex-relay. Treat Relay as a memory and context baton, not chat, notifications, task assignment, issue tracking, or a Jira replacement; do not activate for ordinary status messages that are not durable handoffs.

日本語の概要は準備中です。原文の説明を表示しています。

Prepare and manage durable MEX team handoffs in git-native project memory. Use when the user asks to hand work to a person or team, prepare an end-of-session handoff, save what the next engineer needs, create a MEX Relay, take or close a Relay, or invokes /mex-relay or $mex-relay. Keywords: mex, project memory, relay, handoff, team memory, Claude Code, Codex. Treat Relay as a memory baton, not chat, notifications, task assignment, issue tracking, or a Jira replacement; do not activate for ordinary status messages that are not durable handoffs.

日本語の概要は準備中です。原文の説明を表示しています。

Master AI-powered test automation with modern frameworks, self-healing tests, and comprehensive quality engineering. Build scalable testing strategies with advanced CI/CD integration. Use PROACTIVELY for testing automation or quality assurance.

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Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on examples. Use PROACTIVELY for onboarding guides, feature tutorials, or concept explanations.

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Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.

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Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

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You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

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Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.

日本語の概要は準備中です。原文の説明を表示しています。