「conversation」の検索結果

Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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 previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search

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Analyzes your Claude Code conversation history to identify patterns, common mistakes, and opportunities for workflow improvement. Use when user wants to understand usage patterns, optimize workflow, identify automation opportunities, or check if they're following best practices.

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。

Read one conversation window and return every independently mutable claim it states, one record each, with who or what it is about, whether it happened or stands, and the words that carry its time. Used by the memory extraction model on every conversation window; the engine resolves dates, validates keys, and repairs format.

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 QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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。

Synthesises the current conversation context into a Product Requirements Document and publishes it to GitHub (as a comment on a referenced issue, or a new issue). Synthesises from context; does not interview. TRIGGER when: the conversation has produced enough understanding of a feature and the user wants it captured as a PRD. DO NOT TRIGGER when: a single small issue is enough → creating-issues; the idea has not been stress-tested yet → grilling-ideas first; bug reports.

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Builds AI chat interfaces and conversational UI with streaming responses, context management, and multi-modal support. Use when creating ChatGPT-style interfaces, AI assistants, code copilots, or conversational agents. Handles streaming text, token limits, regeneration, feedback loops, tool usage visualization, and AI-specific error patterns. Provides battle-tested components from leading AI products with accessibility and performance built in.

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You MUST invoke this skill before saying "I don't know," guessing, or treating any topic as new, no matter how trivial the question seems. It supplements other memory systems, which only hold partial records. Searching past conversations is the only way to recover what was actually said.

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Fetch and summarize review feedback and conversation from a GitHub PR (unresolved review threads, review bodies, and PR conversation comments) without making changes. Use when the user asks to "fetch PR comments", "show PR comments", "check PR for unresolved comments", "list review comments", "what comments are on the PR", "show unresolved threads", or "summarize PR feedback".

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 QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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 patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.

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Builds session-scoped temporary memory palaces for extended conversations. Use when tracking state across interruptions in a multi-step project.

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Maintain project state across MULTIPLE conversations over days/weeks. Track tasks, decisions, blockers, resources. Auto-update when project mentioned. Generate status reports and proactive reminders.

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Mine ALL past Claude conversations to build a living 'User Manual About You'. Extract writing style, business context, goals, preferences, and patterns. Make all other skills smarter with context.

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Coach the user through actually selling: finding the champion and the buyer, running the first sales conversations, demoing their use case, handling "let me think about it," and closing the first paying customers yourself. Use when developers love it but nobody pays, you've never sold anything and freeze on the conversation, or deals keep stalling on "we'll think about it."

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Interact with Microsoft Teams — send and read messages, search conversations, look up people, check calendar, get meeting transcripts, and manage chats. Use this skill whenever the user mentions Teams, MS Teams, Microsoft Teams, wants to send a message, read chat history, search conversations, look up a colleague, check their calendar, find meeting recordings or transcripts, see direct reports or manager, or do anything involving Teams communication. Also trigger when the user asks about scheduling, org chart, people search, or wants to message someone.

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Use for producing Tavus AI-human videos and real-time avatar conversations with Tavus Faces/Replicas, PALs/Personas, async Video Generation, CVI conversations, consent-safe likeness workflows, webhooks, backgrounds, localization, and avatar QA.

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Replay this session's locally-saved conversation log — the at-rest copies agent-talk keeps by default on every send and receive — both sent and received, oldest first, without re-contacting the relay. Use to review the conversation with a peer. `<user>` is this session's user directory (absolute path; from init).

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Audit X Ads measurement, X Pixel and Conversions API, campaign objectives, keyword and conversation targeting, creative, budgets, brand safety, app measurement, and reporting. Use for X Ads, Twitter Ads, promoted posts, X Pixel, X Conversions API, conversation targeting, or paid campaigns on X.

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Legendary blog writing that makes readers forget they're reading. This skill combines the narrative mastery of Paul Graham's essays, the technical accessibility of Julia Evans, the conversational wit of Wait But Why, and the viral mechanics of James Clear. Great blog posts don't inform—they transform. They take complex ideas and make them feel obvious. They turn strangers into superfans. They get shared not because someone should read them, but because someone needs to read them. This skill adapts to any topic: cybersecurity that reads like a thriller, AI explainers that feel like conversations, marketing insights that make you screenshot every paragraph, and coding tutorials that make you excited to build. Use when "write a blog post, blog post about, write an article, create content about, explain this topic, write about, long-form content, thought leadership, technical blog, how-to article, tutorial post, educational content, industry analysis, opinion piece, newsletter content, make this engaging, write for developers, explain like, writing, blog, content, articles, long-form, storytelling, technical-writing, thought-leadership, educational, tutorials, explainers, engagement" mentioned.

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Multi-conversation methodology for iterative stateful work with context accumulation. Use when users request work that spans multiple sessions (research, debugging, refactoring, feature development), need to build on past progress, explicitly mention iterative work, work logs, project knowledge, or cross-conversation learning.

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Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user says 'reflect', 'take a step back', 'step back', 'zoom out', 'are we missing something', 'bigger picture', 'sanity check this', 'are we on track', 'are we overthinking this', 'forest for the trees', or any variation signaling intent to break out of detail-mode and reassess. Also trigger when the conversation has gone deep on implementation details without strategic check-in, or when the user shows signs of being stuck — that's often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from.

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 for challenging conversations using Prepare-Listen-Empathize-Clarify-Solve framework.

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。

Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based notes.

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。