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Create 3D objects from text or images, choose catalog assets for repeated scenery, and inspect the results in Blender. The workflow checks geometry, materials, scale, and ground contact while recording asset sources and generation costs.

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• Create a unique 3D object from a text description
• Reconstruct a 3D object from a concept image
• Populate scenery with catalog vegetation and rocks

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.

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• Training and evaluating molecular property prediction models
• Building molecule generation workflows
• Learning representations of protein sequences and structures

Set up OpenMAIC and generate interactive classrooms with multiple AI agents. The skill guides hosted or local use, model configuration, and classroom generation, and also supports customizing the product or building with its SDK, a set of development tools.

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• Trying classroom generation with the Live Demo
• Setting up and checking a local installation
• Generating a classroom from requirements and materials

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

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• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Choose SGLang Diffusion settings for a specific model, GPU, and memory budget. Compare generation speed, memory use, and output quality to select command-line options, including CPU offloading, multiple GPUs, caching, and reduced precision.

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• Choosing generation settings for a specific model and GPU
• Reducing GPU memory use to fit a memory budget
• Comparing performance settings across multiple GPUs

Add a diffusion model or Diffusers generation pipeline to SGLang. Examine reference code, choose a suitable pipeline structure, reuse existing components, and implement model settings and processing stages, with guidance for later multi-GPU support.

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• Adding a new diffusion model to SGLang
• Porting a Diffusers generation pipeline
• Adding a variant by reusing existing model components

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

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• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Create videos from text or images through 9Router using xAI Grok Imagine. Set duration, aspect ratio, and resolution, then track the generation job and download the completed video as an MP4 file.

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• Generate a video from a written scene description
• Create a video from an existing image
• Choose video duration, aspect ratio, and resolution

Plan customer acquisition across paid ads, SEO, and partnerships. Structure channel budgets, calculate customer acquisition cost, define lead handoffs to sales, and set up campaign tracking to understand which activities contribute to new business.

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• Planning acquisition channels and marketing budgets
• Reviewing ad spend and customer acquisition cost
• Defining lead scoring and handoffs to sales

Turn game design documents and character profiles into asset specifications and image generation prompts. Define appearance, dimensions, file formats, and naming rules, then maintain an asset list that tracks progress and identifies shared assets.

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• List the visual assets needed for a game
• Define consistent character and environment asset specifications
• Prepare image generation prompts from an art direction guide

Helps build AI features in Go with Genkit, including text generation, structured data output, streaming, tool calls, and conversational agents. Guides HTTP serving and trace inspection to check model responses and tool execution.

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• Adding text generation or structured output to a Go app
• Building a chatbot that retains conversation state
• Connecting model responses to tool calls

Helps integrate YAML output into json-render, turning incoming text into incremental UI updates. Covers prompt generation, streaming parsing, and targeted edits to existing UI specifications through merge, patch, or diff formats.

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• Render UI updates as YAML arrives
• Generate prompts for YAML output
• Apply partial edits to existing UI specifications

Define the structure and available components for AI-generated JSON screens or videos. Build generation prompts, process incremental updates, connect values to application state, and validate or repair definitions before passing them to a renderer.

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• Define allowed components for AI-generated screens
• Build prompts for generating or editing JSON definitions
• Assemble definitions from streamed AI responses

Set up reinforcement learning, training guided by rewards, for language models with slime. The skill covers Megatron-LM training, SGLang response generation, data preparation, asynchronous execution, and custom workflows for agents that use tools across multiple turns.

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• Prepare GRPO training for GLM or Qwen3 models
• Overlap response generation and model training
• Train agents with multi-turn tool use

Turn image briefs into structured prompts for GPT-Image-2 using reusable JSON templates, organized fields for visual requirements. Specify composition, text, lighting, and constraints for product images, posters, infographics, and other design work.

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• Prepare prompts for product images
• Specify poster titles and layouts
• Structure infographic content

Delegate file creation to shell_agent when direct generation tools fail. Specify the required format and content, then locate generated files by extension or modification time, copy them to the working directory, and check their integrity.

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• Recover from failures in direct file generation
• Delegate PowerPoint creation with specific requirements
• Generate a PDF report containing charts and tables

Check generated PDFs with PyPDF2, a Python library for reading PDF files. Verify that files exist, contain data, open successfully, and match expected page counts, then use failures to guide regeneration and another verification pass.

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• Checking PDFs after generation
• Verifying exact page count requirements
• Validating multiple related PDF outputs

Debug PowerPoint generation with python-pptx, a Python library for presentations. Follow a sequence of installation checks, minimal tests, error logging, and fixes for tables and charts, then inspect the generated slides.

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• Diagnosing errors in PowerPoint generation
• Fixing table and chart code
• Saving detailed error logs

Recover PDF or DOCX generation when execute_code_sandbox fails with unclear errors. Use run_shell to execute Python directly, check required libraries, report specific failures, and verify that the output file was created.

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• Retry PDF generation after an unclear sandbox error
• Generate DOCX files by running Python through the shell
• Check and install missing document libraries

Create Word, PDF, and HTML documents from Markdown, choosing pandoc or ReportLab to suit formatting and layout needs. The workflow covers character handling, output checks, and an alternative generation method when PDF conversion fails.

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• Export a Markdown report to Word, PDF, and HTML
• Create PDFs containing multilingual text and symbols
• Generate PDFs without LaTeX

Generate DOCX, PDF, and HTML from Markdown through separate, observable conversion steps. The workflow replaces problematic characters for PDF output, captures error messages, and switches PDF engines when needed to troubleshoot document generation failures.

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• Export one Markdown document to DOCX, PDF, and HTML
• Investigate unclear document conversion errors
• Replace special characters before PDF conversion

Recover document generation tasks by separating Markdown content creation, format conversion, and output checks. Uses pandoc to produce Word, PDF, or HTML files, making it easier to identify which conversion step failed.

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• Recover document generation after an unclear shell_agent error
• Create Word and PDF files from the same Markdown source
• Convert Markdown into HTML with a custom template

Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.

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• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

Build chat, image generation, and speech transcription features on 0G Compute Network, a distributed GPU marketplace. This skill guides SDK integration, model fine-tuning, response processing for billing, and account funding through code patterns and CLI commands.

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• Integrating chat and image generation into an application
• Preparing model fine-tuning jobs
• Checking balances and funding provider accounts