데이터·AI 스킬

Automate Alpha Vantage operations through Rube MCP and Composio. Discover tools for a specific task, inspect their required inputs, verify an active connection, and execute the workflow using the current tool definitions.

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• Finding tools for an Alpha Vantage task
• Checking required inputs before execution
• Running a workflow with an active connection

Automate AI ML API tasks through Rube MCP, a connection for accessing external tools. The skill guides tool discovery, connection checks, and execution using current input requirements, with guidance for bulk operations and paginated results.

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• Finding available AI ML API tools
• Checking the service connection before execution
• Running tools with their current input requirements

Run Apify Actors, programs that collect web data, through the Composio integration. Retrieve structured results, create reusable tasks with preset inputs, and inspect execution logs for both quick collection jobs and longer runs.

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• Collect restaurant data with a Google Places scraper
• Retrieve selected dataset fields in JSON format
• Create reusable tasks for recurring data collection

Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.

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• Add sentiment analysis to a JavaScript app
• Build image classification in a browser
• Implement audio transcription

Add support for a new language model in MNN, from downloading and analyzing it to mapping model fields, comparing intermediate results, and exporting files. Follow staged tests, with additional paths for image, audio, and unusual architectures.

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• Adding a new text model to MNN
• Exporting a model from HuggingFace or ModelScope to MNN
• Checking intermediate outputs against the original model

Turn documentation, GitHub repositories, PDFs, videos, and local code into AI skills with Skill Seekers. It identifies source types, guides collection and packaging, and supports updates and export to databases for similarity search.

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• Create a skill from a documentation site
• Turn repository content or PDFs into reusable skills
• Update skills from their source documentation

Turn a rough idea or an existing prompt into a ready-to-paste instruction for a specific AI tool. The skill clarifies the task, output format, constraints, and success criteria for writing, image generation, or coding agents.

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• Turning a rough idea into an AI prompt
• Improving a prompt for a specific AI tool
• Defining scope and completion criteria for coding agents

Read existing CARLA sensors to view live camera or lidar output, save recordings, or inspect a single reading. Display multiple sensors together and check ROS 2 topic names, delivery settings, and whether publishing is enabled.

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• View a vehicle camera feed live
• Save camera images or lidar data for a fixed duration
• Inspect a GNSS sensor reading

Read and display CARLA object bounding boxes, then project them onto a camera image as 2D rectangles. Export an annotated PNG and box coordinates in JSON for object detection annotation work.

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• Adding vehicle boxes to a camera image
• Exporting box coordinates for detection annotations
• Checking vehicle dimensions

Guides fine-tuning and evaluation of OpenVLA-OFT robot models that turn images and language into actions. Covers LIBERO simulation, ALOHA deployment, LoRA adaptation that updates a small set of parameters, and troubleshooting action scaling or configuration mismatches.

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• Evaluate pretrained models in LIBERO
• Fine-tune robot action models with LoRA
• Deploy server-client inference for ALOHA

Guides building AI applications that search documents before answering questions, an approach called RAG. Covers importing files and web data, creating searchable indexes, adding conversational interfaces, and evaluating whether answers match the retrieved material.

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• Build a question-answering system for internal documents
• Create a chatbot that remembers conversation context
• Search information across files, web pages, and databases

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

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• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Build image conversations with LLaVA, a model that connects image recognition with language. This skill guides setup, image descriptions, visual questions, and follow-up conversations, with examples for Python, command-line use, and a Gradio interface.

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• Build a chatbot that answers questions about images
• Write detailed descriptions of photos
• Ask follow-up questions about the same image

Combine specialized language models without retraining using mergekit. The skill explains how to choose merging methods, configure each model’s contribution, compare resulting variants, and prepare a merged model for evaluation and deployment.

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• Combine math, coding, and conversation capabilities
• Compare model variants with different merge weights
• Choose a method for merging several models

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Build similarity search with FAISS to find nearby matches in large collections of numeric vectors. The skill guides index selection, memory reduction, GPU use, and saving indexes while balancing search speed and accuracy.

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• Find similar items in large vector datasets
• Choose an index for search speed and accuracy
• Reduce memory use for vector search

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

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• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.

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• Build a chatbot that remembers conversation history
• Create document question answering with source references
• Build an agent that uses custom tools

Evaluate NVIDIA Cosmos Policy for robot manipulation in LIBERO and RoboCasa simulations. Follow GPU environment setup, configure rendering without a display, run small checks before larger benchmarks, inspect success rates, and profile inference latency.

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• Running a small LIBERO evaluation
• Evaluating kitchen tasks in RoboCasa
• Setting up simulation on a GPU cluster without a display

Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.

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• Extracting article details into JSON
• Building agents that call tools
• Reusing shared prompts across conversations

Guides LLM compression through pruning, which removes selected model weights. Covers Wanda and SparseGPT methods without retraining, accuracy comparisons, and structured weight patterns that can accelerate inference on compatible hardware.

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• Trying model compression without retraining
• Comparing accuracy before and after pruning
• Choosing between Wanda and SparseGPT

Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.

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• Improve an existing agent against benchmark tasks
• Build evaluations for domain-specific tasks
• Refine skills and memory from failure feedback

Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.

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• Extracting company or profile details into JSON
• Classifying text using a fixed set of labels
• Generating text that matches a specified pattern

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

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• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs