2,304 件 ・ 関連度順
概要と使いどころ
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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 face.evoLVe for high-performance face recognition workflows across alignment, data preparation, PyTorch training, feature verification, and Paddle deployment.
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, polish, and troubleshoot figures4papers-style publication-ready matplotlib figures, including grouped bars, trends, radar charts, heatmaps, concept diagrams, manifolds, and high-DPI/vector export 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。
Use DeepMind dm_control for MuJoCo simulation, Control Suite environments, PyMJCF models, Composer tasks, rendering, and high-level locomotion/manipulation 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。
Use bm25s for sparse lexical retrieval: tokenize text, build and query BM25 indexes, persist or memory-map them, run the high-level or bm25 CLI workflows, and diagnose optional acceleration and integrations.
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。
Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.
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 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.
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。
Deeply research technical architecture, source code, mechanisms, SDKs, frameworks, project comparisons, and system-design options across repositories, history, official docs, issues, discussions, and high-quality community signals. Use when the user asks for deep research, source-level understanding, architecture comparison, mechanism satisfaction, or evidence for designing a system. Always map the question and obtain confirmation before beginning substantive research; skip for simple factual lookups or straightforward implementation 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。
Coach workers, employees, and individual contributors to turn meetings, transcripts, minutes, and work notes into better execution: infer leader expectations, diagnose previous work, identify next actions, judge delivery readiness, and prepare a high-bandwidth update. Use after meetings, during work, before reviews, or across repeated low-value discussions. Perform semantic review from source evidence.
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。
Coach leaders and managers to prepare, run, and review high-bandwidth meetings that advance real work and decisions. Use for meeting retrospectives, employee work reviews, decision meetings, agenda and question design, direct feedback, meeting-quality diagnosis, or repeated low-value discussions. Preserve direct standards without taking over the worker's work or degrading the truth signal.
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 high-quality academic Beamer slides from a thesis/paper (PDF, Word, or LaTeX source). Compatible with Claude Code and Codex. Supports thesis defense, proposal presentations, and conference talks. Built-in layout library with 13 professional page types, 5 color schemes, and interactive editing loop. Use when: (1) User asks to create thesis defense slides/PPT, (2) User wants to generate academic presentation from a paper, (3) User mentions beamer, 答辩PPT, 学术报告, 开题PPT, 论文幻灯片, or academic slides, (4) User has a PDF/docx/tex thesis and wants presentation output.
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 2+ user reference images into a high-fidelity animated Codex companion using PocketMen's own local stack. Prefer the open-weight Neural Local Studio (FLUX.2 klein 4B; optional Qwen-Image-Edit-2511 Identity-Max) on compatible hardware; otherwise fall back to the deterministic local renderer. Never require hatch-pet or OPENAI_API_KEY for the normal 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。
Production retopology for animation-ready, low-poly, quad-dominant meshes with proper edge loops and efficient polygon flow. Use after sculpting or high-poly modeling before rigging and UV work in Blender via MCP.
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。
Photorealistic AAA art direction for Blender including photogrammetry cleanup, real-world scale, high-poly workflows, PBR accuracy, and scan optimization. Use for cinematic and AAA game realism.
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。
Blender sculpting for organic and hard surface detail using Dyntopo, multiresolution, brushes, alpha workflow, wrinkles, and damage. Use when high-frequency surface detail is needed before retopology in Blender via MCP.
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。
Noir art direction for Blender with high-contrast lighting, wet streets, silhouettes, and monochrome or limited-color grading.
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。
Fantasy world-building art direction for Blender covering medieval, high fantasy, dark fantasy, and urban fantasy asset language.
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。
Comic book and graphic-novel art direction for Blender with ink-line emphasis, halftone cues, and high-contrast framing.
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。
Art direction for racing and sports games in Blender with track landmarks, stadium kits, and high-speed readability.
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 guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking 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。
Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS.
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。
Advanced JavaScript animation library skill for creating complex, high-performance web animations.
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。
Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.
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 "[Competitor] alternative" and comparison pages for developer tools. Build honest, high-converting comparison content that ranks for competitive search terms.
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。