threejs
vmain
SkillSkillsMP
io.github.nexu-io/open-design/threejs
Three.js skills for creating 3D elements and interactive experiences in the browser — scenes, materials, controls, and post-processing.
source
“3D Models” 共 207 个结果
vmain
io.github.nexu-io/open-design/threejs
Three.js skills for creating 3D elements and interactive experiences in the browser — scenes, materials, controls, and post-processing.
vmain
io.github.nexu-io/open-design/open-design-homepage
A pixel-faithful, self-contained mirror of the live open-design.ai homepage — an interactive React Three Fiber / Next.js hero with a real-time 3D wordmark, sticker collage, variable fonts, and scroll-driven motion. First-party showcase of the visual ceiling for interactive web marketing surfaces.
vmain
io.github.nexu-io/open-design/webgl-depth-gallery
A scroll-reactive 3D image gallery in Three.js: Z-stacked images crossfade over per-image mood backgrounds with velocity breath, a glowing cursor trail and an editorial CMYK/RGB/HEX/PMS color card.
vmain
io.github.affaan-m/ECC/remotion-video-creation
Best practices for Remotion - Video creation in React. 29 domain-specific rules covering 3D, animations, audio, captions, charts, transitions, and more. Use when building video in React with Remotion — animations, audio, captions, charts, or transitions.
vmain
io.github.microsoft/ai-agents-for-beginners/local-ai-agents
Bumuo ng mga local-first AI agents na tumatakbo nang buong-buo sa isang developer workstation gamit ang Microsoft Foundry Local at Qwen function-calling models. Saklaw nito ang Small Language Models (SLMs), ang OpenAI-compatible na lokal na endpoint, sandboxed local tools, lokal na RAG gamit ang Chroma, lokal na MCP servers, hybrid cloud/local routing, at ang privacy/cost/offline trade-offs. Batay sa Lesson 17 ng AI Agents for Beginners. GAMITIN PARA SA: pagpapatakbo ng agent nang lokal, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, lokal na RAG, Chroma vector database, lokal na MCP server, privacy-preserving agent, hybrid local at cloud agent, small language model agent, engineering assistant sa aking makina. HUWAG GAMITIN PARA SA: pag-deploy ng mga agents sa cloud nang malakihan (gamitin ang deploying-scalable-agents / Lesson 16), paggawa ng iyong unang agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
vmain
io.github.microsoft/ai-agents-for-beginners/local-ai-agents
Build local-first AI agents wey dey run fully for developer workstation wit Microsoft Foundry Local and Qwen function-calling models. E cover Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG wit Chroma, local MCP servers, hybrid cloud/local routing, and di privacy/cost/offline trade-offs. E based on Lesson 17 of AI Agents for Beginners. USE FOR: run agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant for my machine. DO NOT USE FOR: deploying agents to di cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
vmain
io.github.microsoft/ai-agents-for-beginners/local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the privacy/cost/offline trade-offs. Based on Lesson 17 of AI Agents for Beginners. USE FOR: run an agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant on my machine. DO NOT USE FOR: deploying agents to the cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
vmain
io.github.microsoft/ai-agents-for-beginners/local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the privacy/cost/offline trade-offs. Based on Lesson 17 of AI Agents for Beginners. USE FOR: run an agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant on my machine. DO NOT USE FOR: deploying agents to the cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
vmain
io.github.camunda/camunda/frontend-integration-test
Use when writing, modifying, or debugging Playwright tests in the orchestration cluster webapp — integration tests, visual regression, or accessibility tests; MSW mocking, Page Object Models, or axe-core. Test directory: webapp/client/apps/orchestration-cluster-webapp/test/.
vmain
io.github.Klotzkette/claude-fuer-deutsches-recht/pdf-bericht-erzeugen
3D-Review-Ergebnis als PDF-Bericht erzeugen: Zusammenfassung, Tabellen, Risikoampeln. Normen: §§ 174 ff. InsO. Prüfraster: Vollständigkeit Berichtinhalte, Layout, Signaturfeld. Output: PDF-Bericht 3D-Tabellenreview. Abgrenzung: nicht Excel-Export.
vmain
io.github.aristoteleo/PantheonOS/database_access
Skills for querying and downloading data from genomic, transcriptomic, 3D-genome, and cancer-genomics databases. Covers programmatic access to public repositories, gene annotation, sequence retrieval, processed functional-genomics tracks, Hi-C / Micro-C contact matrices, TCGA-style cohorts, and large-scale single-cell data.
vmain
io.github.affaan-m/ECC/remotion-video-creation
Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。
vmain
io.github.affaan-m/ECC/remotion-video-creation
Remotion 最佳实践 - 在 React 中创建视频。29 条领域特定规则，涵盖 3D、动画、音频、字幕、图表、过渡等。
vmain
io.github.midudev/autoskills/python-executor
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
vmain
io.github.openakita/openakita/python-executor
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
vmain
io.github.calesthio/OpenMontage/manimce-best-practices
Trigger when: (1) User mentions "manim" or "Manim Community" or "ManimCE", (2) Code contains `from manim import *`, (3) User runs `manim` CLI commands, (4) Working with Scene, MathTex, Create(), or ManimCE-specific classes. Best practices for Manim Community Edition - the community-maintained Python animation engine. Covers Scene structure, animations, LaTeX/MathTex, 3D with ThreeDScene, camera control, styling, and CLI usage. NOT for ManimGL/3b1b version (which uses `manimlib` imports and `manimgl` CLI).
vmain
io.github.vercel-labs/json-render/react-three-fiber
React Three Fiber 3D renderer for json-render. Use when working with @json-render/react-three-fiber, building 3D scenes from JSON specs, rendering meshes/lights/models/environments, or integrating Three.js with json-render catalogs.
v0.3.12
io.clawhub.rainer-liao/pexoai-agent
AI video generation skill with auto model selection across Seedance 2, Kling 3.0, HappyHorse, and 10+ models. Produces finished multi-shot videos (5–120s) fr...
v1.0.17
io.clawhub.cellcog/image-generation-cellcog
AI image generation and photo editing powered by CellCog. Text-to-image, image-to-image, consistent characters, product photography, reference-based generation, style transfer, sets of images, social media visuals, brand assets, stickers, comics, GIFs. Professional image creation with multiple AI models.
v1.0.0
io.clawhub.karatla/opencode-controller
Control and operate Opencode via slash commands. Use this skill to manage sessions, select models, switch agents (plan/build), and coordinate coding through Opencode.
v1.0.2
io.clawhub.blueberrywoodsym/x-ai
Chat with Grok models via xAI API. Supports Grok-3, Grok-3-mini, vision, and more.
v1.0.11
io.clawhub.shaivpidadi/free-ride
Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates opencla...
v0.8.0
io.clawhub.robbyczgw-cla/agent-chronicle
AI-powered diary generation for agents - creates rich, reflective journal entries (400-600 words) with Quote Hall of Fame, Curiosity Backlog, Decision Archaeology, Relationship Evolution, mood analytics, weekly digests, "On This Day" resurfacing, and scheduled auto-generation. Works best with Claude models (Haiku, Sonnet, Opus).
v1.0.0
io.clawhub.steipete/swiftui-view-refactor
Refactor and review SwiftUI view files for consistent structure, dependency injection, and Observation usage. Use when asked to clean up a SwiftUI view’s layout/ordering, handle view models safely (non-optional when possible), or standardize how dependencies and @Observable state are initialized and passed.