1,376 件 ・ 関連度順
概要と使いどころ
Execute Unity tests (`EditMode` or `PlayMode`) and return per-test results. Supports filtering by test assembly, namespace, class, and method. Refreshes the AssetDatabase first; defers execution across domain reloads if scripts changed. Precondition: every open scene must be saved — dirty scenes abort the run.
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 JSON Schema for a C# type name via reflection. Supports primitives, enums, arrays, generic collections, dictionaries, and complex objects. Knobs control inclusion of nested `$defs` and whether type-level / property-level descriptions are emitted.
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。
Call a C# method by reflection — including private methods. Requires a method schema obtained via 'reflection-method-find'. Supports static methods, instance methods (with optional target deserialization), and main-thread / off-thread execution.
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。
Get asset data from the asset file in the Unity project — every serializable field and property. Supports token-saving path-scoped reads via `paths` or `viewQuery`. Use 'assets-find' to find the asset first.
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 a specific GameObject in the opened Prefab (preferred when present) or the active Scene. Optionally include editable data, components preview, bounds, and limited hierarchy. Supports token-saving path-scoped reads via `paths` or `viewQuery`.
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。
Get detailed information about a specific Component on a GameObject — type, enabled state, and (optionally) serialized fields and properties. Supports token-saving path-scoped reads via `paths` or `viewQuery`. Use 'gameobject-find' to list components first.
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。
Get serialized data for a Unity `UnityEngine.Object` — all serializable fields and properties. Supports token-saving path-scoped reads via `paths` or `viewQuery`. Pair with 'object-modify' when you need to write back.
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。
Get detailed data about a shader asset — properties, subshaders, passes, compilation messages, and supported status. Supports token-saving path-scoped reads via `paths` or `viewQuery`. Use 'assets-find' with `t:Shader` or 'assets-shader-list-all' to locate the shader first.
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。
Universal release workflow. Auto-detects version files and changelogs. Supports Node.js, Python, Rust, Claude Plugin, GitHub Releases, annotated tags, historical release backfill, and generic projects. Use when user says "release", "发布", "new version", "bump version", "push", "推送", "release notes", "GitHub Release", or "回填 Release".
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。
Extract text from images and scanned documents using PaddleOCR - supports 100+ languages
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 code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
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 JSON-LD structured data markup for rich results in Google Search. Supports FAQ, HowTo, Article, Product, LocalBusiness, and multi-type schemas. Validates against Google requirements and provides implementation guidance. Use when asked to "add schema markup", "generate structured data", "JSON-LD", "rich snippets", "FAQ schema", "product markup", "add structured data to my page", "how to get rich snippets", or any structured data task.
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。
Explain NotFair's paid-ads skills, installation, platform boundaries, account connections, and current product capabilities. Use for questions about how NotFair works, what it supports, how to install or connect it, plans or limits, or paid-media troubleshooting that is not an account-performance request.
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。
Dynamic instrumentation via Frida - attach to or spawn a process, load a JS hook script, capture send() events into a lifecycle-managed run directory. Supports local, USB-attached, and remote frida-server targets.
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。
Self-improving browser automation via the auto-research loop. Iteratively runs a browsing task, reads the trace, and improves the navigation skill (strategy.md) until it reliably passes. Supports parallel runs across multiple tasks using sub-agents. Use when you want to build or improve browser automation skills for specific website tasks.
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 this skill when the user wants to retrieve a URL without a full browser session: fetch HTML or JSON from static pages, inspect status codes or headers, follow redirects, or get page source for simple scraping. Prefer it over a browser when JavaScript rendering and page interaction are not needed. Supports proxies and redirect control.
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-powered adversarial UI testing via the browse CLI. Analyzes git diffs to test only what changed, or explores the full app to find bugs. Tests functional correctness, accessibility, responsive layout, and UX heuristics. Use when the user asks to test UI changes, QA a pull request, audit accessibility, or run exploratory testing. Supports local browser (localhost) and remote Browserbase (deployed sites).
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。
Automate web browser interactions using natural language via CLI commands. Use when the user asks to browse websites, navigate web pages, extract data from websites, take screenshots, fill forms, click buttons, or interact with web applications. Supports remote Browserbase sessions with Browserbase Identity, Verified browsers, automatic CAPTCHA solving, and residential proxies — ideal for protected websites and JavaScript-heavy pages.
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。
Company discovery and deep research skill. Researches a company's product and ICP, discovers target companies to sell to using Browserbase Search API, deeply researches each using a Plan→Research→Synthesize pattern, and scores ICP fit — compiled into a scored research report and CSV. Supports depth modes (quick/deep/deeper) for balancing scale vs intelligence. Use when the user wants to: (1) find companies to sell to, (2) research potential customers, (3) discover companies matching an ICP, (4) build a target company list, (5) do market research on prospects. Triggers: "find companies to sell to", "company research", "find prospects", "ICP research", "target companies", "who should we sell to", "market research", "lead research", "prospect list".
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 concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
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。
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.
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。
Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
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 scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).
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。