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Conduct an adaptive interactive interview on any topic and produce a structured markdown report, summary, or implementation plan. Use when the user says "interview me", "interview me about", "run an interview on", "help me think through", or wants guided discovery on any subject. Also triggers on "talk me through", "explore X with me", "help me plan Y", or "walk me through my thinking on Z" when the user wants a guided conversation rather than a direct answer. Applies broadly — career decisions, project ideas, product direction, personal goals, research questions, team problems — not just technical topics.

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Build custom AI agents in Claude Code from a user's problem statement. This skill analyzes the user's use case, asks smart clarifying questions, researches the internet for similar agents (GitHub repos, blogs, Claude Code community patterns), and then architects and builds production-ready Claude Code agents — including subagents, skills, hooks, slash commands, MCP integrations, and CLAUDE.md configuration. Use this skill whenever the user wants to create an agent, build an automation workflow, set up a Claude Code subagent, design a multi-agent system, or says things like 'build me an agent for...', 'automate this with Claude Code', 'I want a subagent that...', 'help me create a workflow', 'set up an agent pipeline', or any variation of wanting Claude Code to do something specialized. Also trigger when the user mentions agent architecture, agent SDK, agentic workflows, or task delegation in Claude Code — even if they don't use the word 'agent' explicitly.

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Use when Codex must make a high-quality judgment on any complex problem, not only technical problems. Trigger when a question has high complexity, uncertainty, cost of being wrong, long-term impact, value tradeoffs, possible structural causes, or when the user asks for 全面思考, 深度理解, 系统性分析, 帮我想清楚, 从根上看, 这件事到底怎么看, 方法论, 复杂问题拆解, 系统方案, 架构判断, 策略判断, or 根因分析. Also trigger for project and engineering high-risk scenarios such as multi-module, multi-environment, multi-data-source, multi-state, financial truth, live trading, ledger, revenue, customer assets, order governance, broker interfaces, online availability, major solution selection, and system closure.

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Use when adding support for a new model architecture to imp, porting a model family, or debugging a model that loads but produces wrong output - "add support for <model>", "new arch", loader detection, chat template, tokenizer parity, RoPE variant, "outputs garbage", "prompt-blind", "digits scrambled", "NaN logits", "describes a different picture", "does it fit in VRAM". Do NOT use for kernel performance (sm120-cuda-expert) or quant-format questions (quant-formats).

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Use when keeping imp's docs and config examples coherent after a change - updating docs/internals/ARCHITECTURE.md / README / docs/GOAL.md / docs/MODELS.md / docs/roadmap.md / imp.conf.example / CHANGELOG / MISSION_JOURNAL, or "is this doc stale", "document this change", "the example config is out of date", "the README says X but the code does Y", "add a roadmap ledger row". Do NOT use for layer/frontmatter/lint questions (docs-layers), structural code audits (codebase-audit), measuring perf or refreshing the perf baseline (benchmark-cuda), or the agent's private memory (MEMORY.md).

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Use when a question is about *structure* rather than text - who calls or launches a symbol, where it is defined, what a change would reach, whether something is dead, how a request gets from the API to a kernel. Triggers on "who calls", "who launches this kernel", "where is X defined", "what breaks if I change", "is this still used", "is this dead", "trace the path from X to Y", "blast radius", "what depends on this header". Do NOT use for free-text search (`rg` is better and cheaper) or to open a file whose path you already know.

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Shell out to OpenAI Codex CLI for headless code generation, analysis, and question-answering. Optimized for code tasks. Requires OPENAI_API_KEY env var.

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Guide developers in creating Model Context Protocol (MCP) servers. Use for building MCP tools that enable LLMs to interact with external services. Covers TypeScript (primary) and Python FastMCP (secondary), tool annotations, Zod/Pydantic validation, and evaluation question creation.

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Component type definitions, discovery questions, and settings validation for fullstack-typescript projects.

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Discover required technical components through targeted questions based on classified requirements.

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Guided requirements gathering through structured questions to create comprehensive specifications. Produces a complete requirements document via non-blocking conversational interaction.

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Validate a bug bounty finding before writing a report — the 7-Question Gate, 4 pre-submission gates, always-rejected list, conditional chain table, CVSS 3.1 quick reference, and severity decision guide. Use BEFORE writing any report to avoid N/A and low quality submissions.

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Expert PE review of a submittal (shop drawings, product data, samples, equipment brochures) against specs, drawings, code questions and trade coordination. Produces findings with both sources, trade routing and a completeness gate. Triggers: 'review submittal', 'review shop drawings'.

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Phase 2 of 5 — collaborative system design plus change-footprint mapping; outputs an ARCH doc for generate-tasks. Use only when the user asks to run Phase 2, plan the architecture, or design the solution — never trigger automatically from a design question.

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Guide Git branching strategies, branch naming, and merge operations. Use when: creating branches, merging, pull requests, Git workflow questions. Keywords: branch, merge, PR, pull request, GitFlow, GitHub Flow, 分支, 合併, 工作流程.

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Find the most valuable commercial insight and the monetization point closest to lawful revenue for a project, product, service, website, community, content account, open-source tool, or business idea. Use when a user asks how to monetize, who pays, why people do not buy, what to charge, whether to keep building, what to stop, what to do next, or asks to continue or deepen an earlier business analysis. Answer the user's actual question before asking for more information and carry the analysis forward without repeating it. When the buyer, purchased result, or delivery ability is unknown, do not select them from industry stereotypes; use explicit conditional candidates and an evidence-gathering next step. Not for investment advice, income forecasts, get-rich-quick schemes, or guaranteed results.

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Configure SDLC team formation and SDLC method for this project. Asks the four-option SDLC method question (solo / single-team / programme / assured), then recommends and installs team plugins based on project type, language, tech stack, and support needs.

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Commission a project to an SDLC option bundle (solo / single-team / programme / assured). Asks structured questions, recommends an option × level, installs the bundle, and records the decision in .sdlc/team-config.json.

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Query the project knowledge base. Wraps the research-librarian agent. The librarian reads the shelf-index, identifies the 2-4 most relevant library files for the question, deep-reads only those, and returns structured evidence with citations. Stateless — reads the index fresh on every query.

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Prevents premature execution on ambiguous requests. Analyzes request clarity using 5W1H decomposition, surfaces hidden assumptions, and generates structured clarifying questions before work begins. Use at the start of any non-trivial task, or when a request could be interpreted multiple ways. Triggers on "뭘 원하는건지", "요구사항 정리", "clarify", "what exactly", "scope", "requirements", "정확히 뭘", "before we start".

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Configure and manage MCP (Model Context Protocol) servers in Claude Code CLI. Use this skill when adding, removing, listing, or troubleshooting MCP servers, or when questions arise about MCP configuration, transport types, scopes, or authentication. Essential for connecting Claude Code to external tools, databases, and APIs.

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Systematically gather comprehensive travel itinerary requirements through structured discovery questions, MCP-powered research (Perplexity, Exa), and expert detail gathering. Use when users request travel planning, itinerary creation, or trip assistance requiring deep research and personalized requirements gathering. Outputs detailed requirements specification with day-by-day itineraries.

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Use when stress-testing a proposal, plan, architecture or decision before committing to it, such as pre-mortems, assumption audits, "should we even do this?" questions, or when everyone agrees too easily. Not for code review; it challenges strategy, approach and hidden risks.

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Ask Kimi a read-only free-form question about the current repository. Use for prose explanations, flow tracing, module comparisons, or conceptual reasoning where Codex should delegate the answer to local kimi-code rather than perform implementation.

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。