「nct」の検索結果

Select, define, create, inspect, update, attach, detach, and verify reusable Vapi tools through the public API. Use for native call-control tools, supported provider integrations, API Request tools, custom function tools, MCP tools, tool messages, credentials, or configuration-preserving assistant attachment changes.

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This skill should be used when the user asks to "write a test", "run tests", "add a test for", "test this function", "check test coverage", "run the test suite", or when writing pytest tests for StreamFlow features or bug fixes.

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。

Generates 3-5 conceptually distinct approaches (Novel, Surprising, Diverse, Conventional) and holds for selection before implementing. Use before committing to an approach when a task has more than one non-obvious solution; --codex delegates the brainstorm to a fresh subagent.

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。

Generates 3-5 conceptually distinct approaches labeled Novel, Surprising, Diverse, or Conventional, and holds for the user's selection before implementing. The --codex mode runs the brainstorm on GPT-6 Astra via codex exec, then has Codex implement the selected approach. Use before committing to an approach when a creative, architectural, or analytical task has more than one non-obvious solution; use --codex when another model family should widen the range.

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When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.

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。

Manage Alibaba Cloud CDN using the @alicloud/cdn20180510 TypeScript SDK. Use when working with CDN domain acceleration, domain configuration, SSL certificates, cache refresh/prefetch, real-time monitoring, traffic analysis, log management, usage/billing, IP tools, Function Compute triggers, delivery tasks, and resource tagging. Covers all 168 APIs of the CDN 20180510 version.

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Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

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Trace every user-facing button/touchpoint through its full state change sequence to find bugs where functions individually work but cancel each other out, produce wrong final state, or leave the UI in an inconsistent state. Use when: systematic debugging found no bugs but users report broken buttons, or after any major refactor touching shared state stores.

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 articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.

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 distinctive frontend UI with SvelteKit/Bun/Hono. Triggers on: web pages, dashboards, landing pages, apps, artifacts, styling, theming, fonts, animations, charts, dark/light modes.

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 .NET types for performance. Seal classes, use readonly structs, prefer static pure functions, avoid premature enumeration, and choose the right collection types.

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Write modern, high-performance C# code using records, pattern matching, value objects, async/await, Span<T>/Memory<T>, and best-practice API design patterns. Emphasizes functional-style programming with C# 12+ features.

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Semantic code understanding with IDE-like symbol operations. Use when: (1) Large codebase analysis (>50 files), (2) Symbol-level operations (find, rename, refactor), (3) Cross-file reference tracking, (4) Project memory and session persistence, (5) Multi-language semantic navigation. Triggers: "find symbol", "rename function", "find references", "symbol overview", "project memory". IMPORTANT: Prioritize Serena's symbolic tools over file-based grep/read for code exploration.

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Builds a product messaging library from your positioning, your website and customer evidence: a description, the status quo and alternatives, pain points with their knock-on consequences, capabilities, functional, emotional and social benefits, branded features, the cost of doing nothing, common objections with responses, and core messaging blocks (tagline, elevator pitch, value props, proof, per-audience lines). Every claim sourced. Triggers: "product messaging", "messaging library", "messaging framework", "value props", "capabilities and benefits", "what are our differentiators in words". Run after /positioning. Not for choosing the position itself (use /positioning).

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Оценка архитектурного решения по 7 характеристикам ЭМОГССБ (v3.1 — фильтр допуска, атрибут-сценарии, совет затронутых сторон; профиль без агрегатного балла, conjunctive screening). Используй когда пользователь предлагает архитектурное решение, новый инструмент или системное изменение.

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Knowledge Extraction — captures and routes knowledge at work boundaries. Use when you discover a pattern, make a decision, find a distinction, or complete a sub-task.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.

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Detect AI/LLM-generated text patterns in research writing. Use when: (1) Reviewing manuscript drafts before submission, (2) Pre-commit validation of documentation, (3) Quality assurance checks on research artifacts, (4) Ensuring natural academic writing style, (5) Tracking writing authenticity over time. Analyzes grammar perfection, sentence uniformity, paragraph structure, word frequency (AI-typical words like 'delve', 'leverage', 'robust'), punctuation patterns, and transition word overuse.

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Build functional software prototypes and MVPs without deep technical skills by using AI agents as "developers in your pocket." Use this skill when you need to validate a new product concept, build custom back-office tools (e.g., a real estate data manager), or create high-fidelity demos to unblock engineering roadmaps.

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Write clear, plain-spoken code comments and documentation that lives alongside the code. Use when writing or reviewing code that needs inline documentation like file headers, function docs, architectural decisions, or explanatory comments. Works well for both human readers and AI coding assistants who see one file at a time.

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Create, use, and manage Agent Skills for Claude. Use when working with Skills, creating custom capabilities, or understanding how Skills extend Claude's functionality. Covers Skill architecture, file structure, and best practices.

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Build and query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.

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Use after an Anabasis v2 comparison, checkpoint or system change, and before any sentence claims that a harness, a model or a patch improved something. States what one reading may be said to show: the identities it was made under, the counts kept apart, whether it was repeated, which part of a harness it belongs to, whether a mechanism was live in the run, and whether the measurement is valid as distinct from whether it was exploited. Ends in one fixed report.

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。