「chat」の検索結果

公众号创作者对标账号匹配工具，基于3层加权匹配体系（核心基础40%+运营变现35%+数据特征25%）推荐对标账号和头部账号。当用户需要公众号账号推荐、公众号对标、起号参考、账号投放选择时使用。触发词：公众号对标、相似账号、对标推荐、起号参考、账号匹配。

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。

基于热门数据生成公众号爆款标题或对用户标题进行多维度评分的专业工具。当用户需要生成公众号标题、查询爆款内容、分析热门趋势、获取创作灵感、参考爆款规律时使用。触发词：公众号标题、爆款标题、标题生成、标题评分、公众号爆款。

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。

一键解析视频号视频的真实下载地址。粘贴视频号分享链接，即可获取视频标题、封面图和高清无水印下载地址。触发词：当用户提到'解析视频号'、'视频号下载地址'、'视频号下载'、'视频号无水印下载'时使用

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。

公众号文案创作工具。基于红狐数据公众号爆款雷达每日收录的热门文章，按关键词检索爆款、分析流量规律、生成可发布的完整文章。当用户写公众号文章、生成公众号文案、查找爆款文章、分析爆款规律时使用。触发词：公众号文案、公众号爆款、爆款文章、公众号写作、公众号文章生成。

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。

中国互联网品牌感知监测统一工具。聚合全网热榜追踪（hub）、小红书/抖音/公众号近30天深度讨论研究（cn30）、豆包/Kimi/DeepSeek AI 搜索品牌可见度分析（geo）三个互补模块，帮助品牌方/市场运营/内容创作者从「话题热度—真实口碑—AI 回答」三层视角全面感知品牌。当用户需要研究中国社交媒体热点、分析品牌舆情、对比竞品口碑、分析品牌在 AI 搜索中的表现、做 GEO 优化、追踪全网热点时使用。触发词：品牌感知、舆情监测、社媒研究、热榜聚合、AI 搜索可见度、GEO 分析、跨平台舆情、热点追踪、品牌口碑、品牌竞品对比、社媒热榜。

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。

AI公众号信息源 — Daily scan of AI WeChat public accounts, finding the hottest articles by read count, auto-clustering into topics, and generating a styled HTML report with cover images, metrics, search, and subscription support. Use when the user wants AI daily reports, 公众号爆款, AI content curation, 每日AI热点, or 公众号日报.

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。

文旅公众号信息源 — 搜索文旅相关公众号热门文章，按阅读量筛选最火内容，生成精美 HTML 报告。当用户需要文旅公众号、旅游攻略、景区热度、文旅资讯、文旅爆款内容时使用。触发词：文旅公众号、文旅信息源、文旅资讯、旅游公众号、景区文章、文旅爆款。

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。

Send Telegram messages, files, and alerts via bot API; read replies; ask questions with inline buttons and wait for the answer (approve-from-phone). Supports multiple bots and named chat targets. Use when the user wants to send a Telegram message or alert, get notified on Telegram when a task finishes or needs input, ask for approval via Telegram, or wire Telegram notifications into hooks, cron jobs, or CI. Triggers: 'telegram', 'send me a telegram', 'alert me on telegram', 'ask me on telegram', 'notify me when done'.

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。

Send and receive WhatsApp messages via the unofficial linked-device client pywhats (pip install pywhats) — pair with QR, send text/images, group chat, read receipts, presence/typing, and a long-running JSON event stream. Use when the user wants to script WhatsApp as a linked companion device (like WhatsApp Web), pair a device, send a WhatsApp from Python, or listen for inbound messages. Triggers: 'whatsapp', 'send whatsapp', 'pair whatsapp', 'pywhats', 'linked device', 'whatsapp web client'. NOT the official WhatsApp Business/Cloud API.

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。

Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.

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。

Trigger for: writing, revising, converting, or descoping a federal Statement of Work, Performance Work Statement, SOW, PWS, or SOO; develop executable requirements; define contract scope; create measurable performance standards; or prepare requirements before an IGCE. Produce a contract-file-ready .docx plus separate chat-only staffing and Section B handoffs. Never place FTEs, SOC codes, staffing estimates, IGCE content, CLINs, or pricing schedules inside the SOW/PWS body. Do NOT use for the IGCE, market research report, acquisition plan, QASP, or clause-selection matrix.

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。

Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image, gpt image, openai image, chatgpt image

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。

Trigger for: finding federal opportunities; capture and bid screening; competitor or incumbent intelligence; recompete pipelines; teaming partner research; agency, customer, or market intelligence; federal labor-rate or pricing context; or refreshing prior GovCon research. Always begin with the complete workflow menu and obtain the user's selection before research, preflight, file generation, web search, or MCP calls. Produce sourced chat findings or an optional validated GovCon Growth Brief without making a bid decision from public data alone.

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。

Trigger for: OT project description, OTA scope, Research OT under 10 U.S.C. 4021, Prototype OT under 10 U.S.C. 4022, follow-on production scope under 10 U.S.C. 4022(f), milestone-based project scope, prototype objective, phase or go/no-go structure, BAA white-paper conversion, SOO conversion, transition planning, or scope reduction before OT cost analysis. Produce a milestone-based .docx plus a separate chat-only handoff to OT Cost Analysis. Use TRL only when the project supports it. Never place cost estimates, funding profiles, contribution calculations, prices, or skill-routing content in the document. Do NOT use for a FAR SOW/PWS, OT cost analysis, IGCE, agreement terms and conditions, or an Agreements Officer's authority, participant-status, successful-completion, or follow-on determination.

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 a complete mathematical-modeling competition paper with an atomic checklist covering abstract readability, page structure, typography, headings, pagination, figures, tables, equations, anonymity, metadata, visual balance, and page allocation. Return the review in chat or Markdown by default, and provide the bundled single-sheet checklist when the user requests a reusable form. Use when the user provides a PDF or Word paper and asks for a full-paper format and writing-compliance audit; do not replace technical model correctness review.

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。

SOP for building LLM applications on Dify — visual workflow + chatflow + agent + RAG knowledge base + plugin marketplace + observability, self-hostable. Use when shipping LLM apps fast with a "no-code to pro-code" gradient, especially when non-engineers need to co-author the flow.

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。

Enhancement-overlay decision protocol for STREAMING the output of long-running LLM / agent runs from the *backend*, not just wiring a typing animation in the UI. Activates when a coder agent must stream final tokens to a chat client, surface intermediate agent steps (which tool, which node, partial reasoning), emit custom tool-progress events, choose a transport (SSE vs WebSocket), or decide what to do when the client disconnects mid-stream. The langchain / langgraph skills mention stream modes but stop at "you can stream"; this skill encodes *what to stream, over what transport, and how to fail safely*.

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。

Multi-turn consultation with Codex CLI for second opinions, brainstorming, or collaborative problem-solving. Use when the user asks to "consult codex", "ask codex", "get codex's opinion", "brainstorm with codex", "discuss with codex", or "chat with codex".

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。

Builds AI agents on the Convex agent component: threads, messages, tools that call queries and mutations, streaming, RAG with vector search, and workflows for multi step jobs. Use when adding a chat assistant, tool calling agent, or retrieval feature to a Convex app.

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。

Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.

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。

Implement real-time features using Convex reactive queries that automatically update when data changes, enabling live collaboration, instant updates, and reactive UIs without manual polling. Use when building live dashboards, implementing collaborative editing, creating chat applications, showing real-time notifications, building activity feeds, implementing presence indicators, creating reactive search, or any feature requiring instant data synchronization across clients.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".

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。