2,838 件 ・ 関連度順
概要と使いどころ
Use when the user has a vague research direction and wants to explore feasible specific topics. Outputs a structured analysis with candidate topics, innovation/feasibility scoring, and a pre-survey of 20–30 representative works. Single-stage, no Python runtime.
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。
Fetches AI news from smol.ai RSS and generates structured markdown with intelligent summarization and categorization. Optionally creates beautiful HTML webpages with Apple-style themes and shareable card images. Use when user asks about AI news, daily tech updates, or wants news organized by date or category.
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。
Structured logging with proper levels, context, PII handling, centralized aggregation. Use for application logging, log management integration, distributed tracing, or encountering log bloat, PII exposure, missing context errors.
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。
Adaptive interview-driven spec generation. Use when converting rough plans into comprehensive specifications, needing structured requirements gathering, or transforming ideas into implementation-ready documentation.
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 well-structured git commits from the current working tree. Use when the user says 'commit', 'save my work', 'let's commit this', 'make a commit', or any variation of wanting to commit code to git.
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 a PRD from the client brief and write it as a local markdown file in issues/. Use when the user wants to turn a client request into a structured PRD.
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。
Turn whatever the agent just produced — a conversation, an analysis, an artifact, a markdown file — into a live shareable URL in seconds, published as-is via ReportRoom. Use when the user says "share this", "give me a link", "put this online", "make this a page", "send this to someone". This is the fast, verbatim path — for a restructured, designed report use report-publisher; for a view-tracked proposal use proposal-tracker.
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。
Break down full ICML reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted 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。
Break down full NeurIPS reviewer responses into structured rebuttal units. Use when input contains NeurIPS reviewer fields (Summary, Strengths and Weaknesses, Questions, Limitations) and numeric scores (Rating, Confidence, Quality, Clarity, Significance, Originality). Splits questions and limitations into granular response items while preserving original wording for quoted 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。
Break down full ICLR reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted 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。
Break down full ARR (ACL Rolling Review) reviewer responses into structured rebuttal units. Use when input contains ARR reviewer fields (Paper Summary, Strengths, Weaknesses, Comments/Suggestions) and numeric scores (Confidence, Soundness, Excitement, Overall Assessment, Reproducibility). Splits weaknesses and comments/suggestions into granular response items while preserving original wording for quoted 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。
Write high-quality, SEO-optimized blog posts in multiple formats including how-to guides, listicles, opinion pieces, and case studies, with structured outlines, hooks, and calls-to-action. Use when the user requests blog post writing or provides relevant inputs for this 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。
Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited. Use when the user requests threat modeling or provides relevant inputs for this 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。
Conduct a structured literature review on a given topic by defining a search strategy, applying inclusion and exclusion criteria, extracting key findings, and synthesizing results into a coherent academic review. Use when the user requests literature review or provides relevant inputs for this 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。
Optimize a webpage for search engines by analyzing on-page elements, improving technical performance, and implementing structured data for maximum visibility. Use when the user requests seo optimization or provides relevant inputs for this 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。
Analyze contracts for risks, obligations, key clauses, and generate structured risk reports with severity ratings. Use when the user requests contract review or provides relevant inputs for this 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。
Capture, organize, and retrieve notes efficiently using structured formats, tagging, and file management for meetings, ideas, research, and daily logs. Use when the user requests note taking or provides relevant inputs for this 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。
Design GraphQL APIs with well-structured schemas, efficient resolvers, pagination, and performance patterns like DataLoader and federation. Use when the user requests graphql api design or provides relevant inputs for this 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。
Design structured customer onboarding workflows with phased checklists, email templates, success milestones, and ownership assignments. Use when the user requests onboarding playbook creation or provides relevant inputs for this 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。
Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Use when the user requests report generation or provides relevant inputs for this 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。
Design structured, engaging chatbot conversations with robust intent handling, slot filling, disambiguation, error recovery, and graceful fallback strategies. Use when the user requests chatbot conversation design or provides relevant inputs for this 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。
Design, implement, harden, and verify Model Context Protocol (MCP) servers with precise tool contracts, least-privilege authorization, safe transports, structured errors, and interoperability tests. Use when creating a new MCP server, exposing an API or data source through MCP, reviewing an MCP server design, adding or revising MCP tools, or preparing an MCP server for production.
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 clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this 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。
Design and validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.
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。