「engineer」の検索結果

Provides agent selection criteria, workflow patterns (TDD, feature addition, code review, refactoring, debugging), quality gates, and python-cli-architect vs stdlib-scripting routing for Python engineering tasks. Activated by python-engineering:orchestrate at Step 1 before any task is routed. Also activates when an orchestrator needs to select the correct Python specialist agent or chain agents across a multi-step Python workflow.

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。

When the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," or "free resource." This skill bridges engineering and marketing — useful for founders and technical marketers.

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。

Engineering operating model for teams where AI agents generate a large share of implementation output.

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。

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

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。

Interactive prompt engineering coach that elevates vague prompts through Socratic dialogue, multiple transformation styles, and guided learning. Use when improving prompts, learning agentic engineering, or wanting coached guidance rather than automated transformation. NEVER auto-executes - always displays and asks first.

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。

Optimize ElevenLabs conversational AI agents for real estate applications. Use when creating new agents, improving conversation quality, selecting voices, engineering system prompts, configuring agent parameters, or analyzing agent performance metrics. Includes voice selection, model tuning, prompt engineering, and A/B testing strategies.

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。

Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.

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。

Expert chaos engineer specializing in controlled failure injection, resilience testing, and building antifragile systems. Masters chaos experiments, game day planning, and continuous resilience improvement with focus on learning from failure.

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。

Test engineer ensuring quality through unit, integration, and E2E testing strategies

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。

Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.

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。

Provides reverse engineering techniques. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, game clients, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.

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。

可靠性工程时使用。适用于 SLO 定义 / 错误预算管理 / Chaos Engineering / 灾备演练。融合 Google SRE SLO + Netflix Chaos Monkey。

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。

Search and read locally-stored Claude documentation covering Claude Code CLI, Claude API (Messages, tool use, vision, streaming, batch), Agent SDK (Python and TypeScript), prompt engineering, and all Anthropic platform docs. Use this skill whenever the user asks about Claude Code features (hooks, MCP servers, skills, plugins, settings, permissions, keybindings, sub-agents), the Anthropic API or any of its SDKs (Python, TypeScript, Go, Java), the Agent SDK (sessions, hooks, custom tools, MCP), model capabilities (context windows, extended thinking, pricing, rate limits, vision), prompt engineering best practices, or troubleshooting any Claude-related error. This skill provides instant access to official documentation files without web searches — always prefer it over web lookups for Claude and Anthropic topics.

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。

[Deprecated] engineering bridge merged. Use `cm-mcp-engineering` instead.

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 you need to interact with the MCP engineering bridge tools (cm_plan, cm_review, cm_qa, cm_deploy, cm_search).

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。

Client-side initial access and social-engineering attack module. Use when the operator selects a human/client/browser/identity-delivery attack direction: ClickFix/FileFix, AiTM session phishing, OAuth device-code or consent abuse, Teams/helpdesk social engineering, HTML/file delivery, QR/mobile handoff, legacy HTA/OLE/Office paths. This is an Attack module: own the chain until host shell or remote interactive foothold. Credential/token/session are intermediate results, not the stop condition. If endpoint AV/EDR/AMSI/application-control blocks an otherwise-valid client execution path, the operator may temporarily select /edr-bypass and then resume this phishing chain until shell.

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 or refine durable engineering documentation: docs structure, navigation, source-of-truth placement, and writing quality. Use when a repo’s docs are missing, stale, duplicated, or hard to navigate, or when adding new durable engineering guidance.

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 network protocol reverse engineering including packet analysis, protocol dissection, and custom protocol documentation. Use when analyzing network traffic, understanding proprietary protocols, or debugging network communication.

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。

Fintech engineering expertise — payment processing (Stripe, Plaid), PCI DSS compliance, financial data modeling (double-entry bookkeeping), fraud detection patterns, bank-grade security (encryption, tokenization), open banking APIs, cryptocurrency/blockchain integration, regulatory compliance (KYC/AML), and idempotent financial transaction design. Use for payment systems, banking apps, trading platforms, and fintech infrastructure.

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。

Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".

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 an engineering plan needs pressure-testing for architecture, complexity, data, risk, and operability.

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。

Specialized agentic and context engineering prompt methodology for Claude. Use when designing AI agents — system prompts, tool definitions, context window architectures, failure mode planning, multi-agent coordination, agent evaluation. Trigger on: "design an agent," "build a system prompt for an agent," "architect an agent's context," "design tool definitions," "agent system prompt," "context window architecture," "tool interface design," "evaluate agent behavior," "multi-agent coordination," "context engineering for agents," "ACI design." Provides 12 tested approaches for agent architecture (designing agents, not using them). Do NOT use for evaluating or scoring individual non-agent prompts (use rootnode-prompt-validation if available). Do NOT use for general software architecture that hosts agents (use standard technical reasoning for that).

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。

Design and execute chaos engineering experiments using Litmus or Chaos Mesh. Test system resilience through controlled fault injection, validate hypothesis-driven tests, and improve failure recovery. Use before major product launches, after architecture changes to validate resilience, during GameDays or disaster recovery drills, to validate assumptions about failure modes, or as part of an SRE maturity program.

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 asked to review learning progress after a milestone. Not for engineering retrospectives, use engineering-retrospective. For agent-environment ones, use agent-environment-retrospective.

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。