1,239 件 ・ 関連度順
概要と使いどころ
Generate AntV Infographic syntax outputs. Use when asked to turn user content into the Infographic DSL (template selection, data structuring, theme), or to output `infographic <template>` plain syntax.
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。
Use only for MSBuild failure investigation, build-performance analysis, or review of existing build XML/extension contracts. A plan to convert a working legacy/non-SDK project to SDK style or migrate packages.config is a project-system migration, NOT a build-file review: do not activate, even to check scope. Framework/API upgrades are also excluded. Activate for advisory Import/Exists hook and NuGet packed-import questions even without a checkout or failed build; .csproj/.fsproj/.vbproj/.props/.targets conditions; custom-target inputs/outputs; .binlog capture/replay; failing dotnet build/restore/pack/publish or MSBuild-based test builds; slow evaluation, compilation, copying, or no-change builds; and controlled timing comparisons. Exclude SDK installation alone, C# refactoring, runtime profiling, assertions after successful builds, and non-MSBuild builds.
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。
Detects MSBuild projects with conflicting OutputPath or IntermediateOutputPath. USE FOR: builds failing with 'Cannot create a file when that file already exists', 'The process cannot access the file because it is being used by another process', intermittent build failures that succeed on retry, or missing/overwritten outputs in multi-project or multi-targeting builds where bin/obj (or project.assets.json) collide. Common causes: shared OutputPath, missing AppendTargetFrameworkToOutputPath, extra global properties (e.g. PublishReadyToRun), or SetTargetFramework on a ProjectReference to a single-targeting project. DO NOT USE FOR: file access errors unrelated to MSBuild (OS-level locking), single-project single-TFM builds, non-MSBuild build systems.
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。
Own MSBuild import and hook discovery. USE FOR: CustomBefore/CustomAfter hooks, ordered wildcard and NuGet auto-imports, control properties, build/buildTransitive packed layout, package ID and file-name matching, per-TFM forwarders, and tracing why assets or hooks are missing, broken, or replaced. An import guard remains in scope when it is one defect in a broader hook/import flow. DO NOT USE for a focused safety verdict on whether one specific Import needs Exists or is an intentionally unguarded package contract; use msbuild-antipatterns. NEVER INVOKE when imports and hook placement already work and the request is only target Inputs/Outputs, incremental skipping, or FileWrites clean tracking; use incremental-build or target-authoring. Exclude props-versus-targets placement and non-MSBuild systems.
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。
Maintain model-routing audit golden fixtures and expected outputs.
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。
Polishes academic English paragraph by paragraph, reviewing grammar, word choice, voice, coherence, and sentence structure. Outputs revision suggestions alongside polished text. Triggered by phrases like 'polish this paragraph,' 'check the grammar,' 'rewrite in academic English,' or keywords like manuscript editing, SCI polishing, and journal submission editing.
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 compliance checklists for business scenarios involving GDPR, PIPL, or advertising/data laws. Outputs a structured checklist with check items, legal basis, risk levels, and actionable recommendations. Triggered by requests like "run a compliance check," "GDPR/PIPL compliance," "pre-launch review," "privacy impact assessment (PIA/DPIA)," or asking if a feature is compliant.
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。
Software project effort estimation assistant. Outputs three-point estimates (optimistic/most-likely/pessimistic values with confidence intervals), T-shirt sizes, or Function Point Analysis (FPA) counts. Triggered when users ask 'how long will this feature take,' need to assess project workload, perform PERT estimation, T-shirt sizing, FPA, sprint planning, or quote-based effort breakdowns.
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。
Industrial AI literature research with mandatory intake questions, venue-aware source prioritization, structured report outputs, and survey draft generation. Use when the user needs up-to-date research on predictive maintenance, intelligent scheduling, industrial anomaly detection, smart manufacturing, cyber-physical systems, edge AI for automation, or crossover robotics-for-industry topics. Also trigger for adjacent terms: "digital twin", "industrial IoT", "Industry 4.0", "manufacturing AI", "factory automation", "process optimization", or "survey draft" in industrial contexts.
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。
专业Marp演示文稿制作助手。支持完整工作流程：工作空间初始化、内容分析、slides制作、多维度审阅、中文语言规范审阅（中文演示文稿）、PNG转换检查、终稿确定。所有产出物集中管理在项目工作文件夹中。当用户提到"制作slides"、"做PPT"、"演示文稿"、"Marp"、"幻灯片"、"presentation"等关键词时自动启用。Professional Marp presentation assistant with complete workflow: workspace initialization, content analysis, slide creation, multi-dimensional review, Chinese language review (for Chinese presentations), PNG conversion check, and finalization. All outputs organized in project workspace.
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 master's thesis review reports (硕士论文评阅意见) calibrated to a given score. Outputs academic evaluation and shortcomings/suggestions in Chinese. Trigger when user says "master thesis review" / "硕士论文评阅" / "论文评阅" / "评阅意见" / "thesis 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。
Brainstorm a longlist of benefits and costs for a project. Outputs two clean tables (benefits and costs), each with materiality (H/M/L), a cash flow tag (cash in / cash out / non-cash), a quantification method, and a monetisation method. Supports HMT Green Book, EU Better Regulation Guidelines, World Bank, Asian Development Bank, and the Victorian Treasury High Value High Risk (HVHR) 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。
Audit economic analysis outputs (fiscal briefings, macro briefings, market research, longlists, and other quantitative economic documents) against methodology standards, academic literature, and common errors. Runs structured checks across core categories including counterfactual, additionality, discounting, double counting, distributional analysis, Aqua Book RIGOUR, and Flyvbjerg-style strategic misrepresentation detection. Returns a RAG scorecard with issues ranked by severity.
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。
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.
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 truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
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。
Find the exact pages AI assistants cite when answering questions in your category, and identify which ones you can get into. Claude clusters the cited sources, scores each by attainability, and outputs an outreach/content plan. Platform: AI visibility.
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。
实践认识论：把方案、假说或判断放回实践中检验，按「实践 → 认识 → 再实践」螺旋迭代，并在开始前写明循环终止条件。当你提出了假说要验证、一次尝试失败要复盘再来、发现自己只想不做或只做不想时触发；一次性输出、答案可直接查阅、还没形成假说（先调查）时不触发。 English: Practice-cognition loop. Test a hypothesis or plan in practice, learn from the result, and iterate in a rising spiral with an explicit stop condition. Trigger when a plan needs validation, an attempt failed and must be retried, or you notice thinking without doing (or doing without thinking); skip for one-shot outputs, lookup questions, or before any hypothesis exists.
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。
Event prospecting skill. Takes a conference / event speakers URL, extracts the people, filters their companies against the user's ICP, then deep-researches only the speakers at ICP-fit companies. Outputs a person-first HTML report where each card answers "why should the AE talk to this person?" with all public links and a one-click DM opener. Use when the user wants to: (1) find leads at a specific conference, (2) prep for an event, (3) research event speakers, (4) build a target list from a sponsor/exhibitor page, (5) scrape conference speakers and rank by ICP fit. Triggers: "find leads at {event}", "research speakers at", "prospect this conference", "stripe sessions leads", "ai engineer summit prospects", "event prospecting", "scrape conference speakers", "who should I meet at".
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 truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
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。
CLI-first web scraping & content extraction with optional MCP server. Use when you have target URLs and need clean, selector-based outputs (html/md/txt).
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 research/technical reports with strong structure + figure standards. Supports Markdown/HTML/PDF outputs (Quarto optional), executive summary, methods, results, discussion, and reproducibility appendix.
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。
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
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。
Compute well-defined metrics from existing formulas, datasets, or test outputs. Use as an explicit/manual helper when the metric definition is already known, not for choosing the overall analysis owner or dashboard strategy.
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。
Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.
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。