4,180 件 ・ 関連度順
概要と使いどころ
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。
Modify a Unity `UnityEngine.Object`'s serializable fields/properties. Three modification surfaces are available (`objectDiff`, `pathPatches`, `jsonPatch`) — see the skill body. Use 'object-get-data' first to inspect the object structure.
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。
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。
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。
Modify GameObject fields and properties in opened Prefab or in a Scene. You can modify multiple GameObjects at once. Just provide the same number of GameObject references and SerializedMember objects. Three modification surfaces are available per GameObject (gameObjectDiffs, pathPatchesPerGameObject, jsonPatchesPerGameObject) — see the skill body for details.
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。
Modify a specific Component on a GameObject in opened Prefab or in a Scene. Allows direct modification of component fields and properties without wrapping in GameObject structure. Use 'gameobject-component-get' first to inspect the component structure before modifying. Three modification surfaces are available (componentDiff, pathPatches, jsonPatch) — see the skill body for details.
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 multi-AI code review with inline PR comments — use for thorough quality and security 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。
Working with tika-metadata-schema, the build-gated registry of Tika metadata keys — regeneration after Property changes, gate tests, naming conventions, post-rename sweeps. Use when adding or renaming metadata keys or when the schema gate fails.
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 to run a multi-persona expert advisory review on a labelled pull request in microsoft/apm. panel-mode (full | lean | delta) selects a surface-gated roster plus a CEO synthesizer; omitted or unknown mode is lean. Do not spawn inactive stubs. The orchestrator is the sole writer to the PR: ONE recommendation comment, no verdict labels, no merge gating. The panel is advisory -- it surfaces findings, prioritizes follow-ups, and renders a ship-recommendation that the maintainer and author weigh.
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。
Swift Concurrency review and remediation for Swift 6.2+. Use when asked to review Swift Concurrency usage, improve concurrency compliance, or fix Swift concurrency compiler errors in a feature or file. Concrete actions include adding Sendable conformance, applying @MainActor annotations, resolving actor isolation warnings, fixing data race diagnostics, and migrating completion handlers to async/await.
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。
Routes problems to appropriate mathematical frameworks using expert heuristics
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。
Query and operate connected Google Search Console properties through NotFair MCP. Use for live GSC query or page performance, clicks, impressions, CTR, position, traffic changes, indexing status, URL inspection, sitemap reads or approved sitemap submission/removal, and Search Console MCP setup. Route full-site SEO audits to seo-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。
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
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 guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
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。
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
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 ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production 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。
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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。
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
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。
Routes problems to appropriate mathematical frameworks using expert heuristics
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。
Query ChEMBL bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.
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。
Scan Apple's SwiftUI documentation for deprecated APIs and update the SwiftUI Expert Skill with modern replacements. Use when asked to "update latest APIs", "refresh deprecated SwiftUI APIs", "check for new SwiftUI deprecations", "scan for API changes", or after a new iOS/Xcode release. Requires the Sosumi MCP to be available.
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。