「assistant」の検索結果

Mentor for embedded and IoT hardware projects. Helps select MCUs, dev boards, and toolchains, decides where sensor readings end up (phone, PC, dashboard, or alert), and gives time/cost estimates and a phased build plan from breadboard MVP to production PCB. Use when the user mentions embedded, IoT, microcontroller, ESP32, STM32, Arduino, Raspberry Pi Pico, firmware, PCB, KiCad, EasyEDA, PlatformIO, MQTT, Home Assistant, ESPHome, Grafana, an IoT dashboard, seeing sensor data on a phone, or asks for hardware tool recommendations, project planning, or cost/time estimates for an electronics project.

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。

Configure and verify the OpenWork Slack assistant for an approved private-alpha organization, using its existing Slack app and individual-account connector. Use when a user wants @openwork in Slack or needs help installing or linking it.

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 n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.

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。

Guides developers through downloading, configuring, and installing the official open-source Google Ads MCP Server. Use this skill when a user wants to connect their AI assistant (such as Gemini, Claude Code, or Cursor) to their Google Ads account to query campaigns or retrieve reporting metrics using natural language.

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。

数学形式化与 proof-assistant 验证。用户要求 Lean 4/Mathlib、formal proof、kernel check、无 sorry 编译，或需要把自然语言定理切成可形式化定义和引理时使用。缺少 Lean 工具链时必须 fail-closed。

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。

Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.

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。

Ingest supplied source material into an Obsidian vault with provenance and claim tracking: pasted text, files staged in the selected vault's inbox or .raw archive, or explicitly approved URLs. Use for a single source or bounded batch, not for saving an assistant answer. Triggers: ingest, ingest this file, ingest this URL, process this source, read and file this source, batch ingest, ingest these sources.

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。

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.

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。

Reference for using Plannotator (its `plannotator` tool when you have one, otherwise the CLI): plan review, code review, annotating files, URLs, folders, and running local apps, annotating the last assistant message, browsing archived plan decisions, exporting or sharing Guided Reviews, and the Plannotator Inbox (messages and questions the person answers later, without blocking you). Invoke when asked to use Plannotator for anything not covered by a more specific plannotator-* skill, and before you leave the person a decision, a question or finished work while they are away from the chat (if another agent started you, report to it 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。

Open Plannotator on the latest rendered assistant message and use the returned annotations to revise that message or continue.

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。

帮助用户了解 Octop、解答产品与使用问题，并配置当前实例。当用户提出以下类型的问题时使用此 skill： Octop 是什么、能做什么、有哪些功能或亮点；官网、帮助文档、安装、快速开始、Docker； 专家、人格、知识库、连接器、通道、定时任务、ACP、远程桌面等概念或求助； 配置或切换 LLM 模型与 Provider；添加或管理 IM 通道（飞书、企业微信、QQ 等）； 启用或禁用 Agent Skill；管理定时任务；备份与升级；询问「octop 怎么用」「怎么配置」、 「怎么接入 xxx」「怎么换模型」「怎么加通道」「CLI 怎么用」「有没有文档」等。 即使用户只是问「Octop 是什么」或「怎么配置 octop」，也应触发此 skill。

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。

综合办公文员与内容创作者在需要整理会议纪要或创作社媒文案时，使用本技能将录音一键转写并智能改写为邮件、笔记或社媒文案，支持多段录音无缝拼接，秒出高质量结构化文本，让语音记录与内容产出效率翻倍！

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。

其他高等院校教师与数据科学家在追踪前沿学术时，当需要深度解析arXiv论文，用此技能可一键下载PDF并自动生成词频分析、双人播客、交互式网页、PPT演示与总结图，将数小时的文献阅读转化为多媒体内容，效率提升十倍！

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。

Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments). Use when preparing your own codebase to be audited by someone else, getting a repository review-ready before an external security review, deciding what to fix before auditors start, or asking what assessors need from a project. For understanding unfamiliar code you are about to audit, use audit-context-building 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。

This skill should be used when the user asks about Mixpanel product analytics, event data, funnel analysis, retention curves, cohort analysis, segmentation queries, user behavior, conversion rates, churn, DAU/MAU, ARPU, revenue metrics, feature adoption, A/B test results, user paths, flow analysis, or any request to query, explore, visualize, or analyze Mixpanel data using Python. Also use when the user asks to read, write, or manage Mixpanel "business context" — the markdown documentation that grounds AI assistants in an organization's structure and goals.

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。

Guide for creating AI subagents with isolated context for complex multi-step workflows. Use when users want to create a subagent, specialized agent, verifier, debugger, or orchestrator that requires isolated context and deep specialization. Works with any agent that supports subagent delegation. Triggers on "create subagent", "new agent", "specialized assistant", "create verifier". Do NOT use for Cursor-specific subagents (use cursor-subagent-creator 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。

Expert in Confluence operations using Atlassian MCP. Use when the user says "search Confluence", "create a Confluence page", "update a page", "find documentation in Confluence", "list spaces", or "add a comment to a page". Do NOT use for Jira issues, general web search, or local file creation.

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 n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.

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 code review assistant that analyzes code quality, security, and 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。

Interactive troubleshooting assistant for Claude Code issues

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。

Professional AI programming assistant with structured workflow (Research -> Ideate -> Plan -> Execute -> Optimize -> Review) for developers

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, refine, and optimize high-quality YAML prompts for AI assistants. Use when working with prompt templates, system prompts, agent prompts, or any prompt engineering tasks. Provides structure guidelines, template patterns, and quality standards for YAML-based prompts.

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。

Black-box security audit of a DEPLOYED AI agent (not the MCP server behind it) — tool-call hijacking, cross-session memory poisoning, confused-deputy via connected tools, agent-to-agent IDOR, excessive agency / unconfirmed destructive actions, and privilege compromise where the agent holds broader perms than the user. Use when the target is a live assistant/agent product with tool access (bookings, email, payments, file/RAG, browsing) rather than a raw LLM chat box or an MCP server you can read.

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。

LLM application red-teaming — prompt injection (direct + indirect), jailbreak, system-prompt leak, data exfiltration, guardrail bypass, multi-turn crescendo, cross-lingual + cipher + invisible-unicode token smuggling, excessive agency / tool abuse, insecure output handling. Canonical OWASP LLM Top 10 + ASI01-ASI10 mapping. Use when a target exposes a chat/completions/assistant/copilot endpoint, an AI feature that consumes user text or documents, or any /v1/chat, /api/chat, /mcp surface.

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。