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Keep chat-ui model settings aligned with the HuggingFace router. Add descriptions for newly available models, check reasoning support, enable artifact panels based on model size, and remove settings for models no longer served.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add newly released models to chat-ui settings
• Remove settings for models no longer served
• Check reasoning support and artifact eligibility

Plan NVIDIA NuRec workflows that turn sensor recordings into renderable 3D scenes. This routing skill selects the relevant specialist skills for dataset downloads, conversion, training, rendering, and frame cleanup, and puts the steps in order.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan a reconstruction workflow from sensor recordings
• Find the steps to render an existing USDZ scene
• Choose a workflow for cleaning up rendered frames

Build blockchain applications with ChainGPT APIs and SDKs. The skill guides chatbot integration, NFT image creation and minting, smart contract generation and auditing, and crypto news feeds, with starter templates and usage cost estimates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a blockchain chatbot
• Create an NFT image generation and minting service
• Add smart contract auditing to CI/CD

Search GitHub, Stack Overflow, Hacker News, and HuggingFace for code examples, projects, technical answers, discussions, models, datasets, and Spaces. Filter supported searches and receive structured JSON results containing titles, links, and snippets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find code examples and open-source projects
• Search technical answers and GitHub Issues
• Explore developer news and discussions

Use Evo 2 to score DNA sequences, turn genomic regions into numerical representations for classification, and generate sequences from a supplied prefix. Compare reference and variant sequences or assess coding and regulatory regions across species.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing scores before and after a DNA variant
• Preparing numerical features for genomic region classification
• Generating DNA from a supplied prefix

Predict structures of protein, nucleic acid, and small-molecule complexes with ESMFold2, using single sequences or aligned related sequences. The skill also covers ESMC models for numerical sequence representations, mutation scoring, and predictions of contacts between residues.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting complex structures from single sequences
• Assessing designed binders with ESMFold2-Fast
• Predicting structures using aligned related sequences

Predict patterns of gene expression and other genomic measurements from DNA sequences with Borzoi. Compare reference and altered sequences to estimate regulatory effects, generate predicted coverage tracks, and prioritize variants in regions that do not encode proteins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing predicted effects before and after a DNA variant
• Generating predicted coverage tracks for a genomic region
• Prioritizing variants in non-coding regions

Predict structures of complexes containing proteins, DNA, RNA, and ligands with OpenFold3, an AlphaFold3 reproduction with publicly available model weights. Prepare JSON inputs, generate candidate structures, and inspect confidence scores and molecular clashes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting structures of protein–ligand complexes
• Predicting complexes containing DNA or RNA
• Comparing confidence scores across candidate structures

Add AI models to Kiln or find available models missing from its list. The skill checks provider model identifiers and capabilities, updates model settings, runs tests against real APIs, and drafts a Discord announcement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a newly released model to Kiln
• Finding available models missing from Kiln
• Checking provider support for recently added models

Support authorized external security reconnaissance, the discovery of exposed systems and information. The skill provides search queries, probe references, secret detection patterns, and scoring guidance to help identify assets and organize findings with evidence and confidence levels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Discovering public subdomains, APIs, and cloud storage
• Investigating exposed API keys and other secrets
• Identifying SSO services and Microsoft 365 exposure

Forecast sales, sensor readings, or other time series without training a custom model. Using Google's TimesFM, this skill produces forecasts and uncertainty ranges from CSV files or arrays, supports batch processing, and checks local computing resources before use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Forecasting monthly sales from a CSV file
• Forecasting demand across multiple stores
• Plotting sensor forecasts with uncertainty ranges

Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding newly available router models to chat-ui
• Writing concise descriptions of model capabilities
• Updating production and development model entries together

Draft huggingface_hub release notes from saved pull request JSON files. The skill groups changes, explains major updates, adds examples from documentation changes, and includes author credits and verified documentation links in a Markdown document.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting release notes from saved pull request data
• Explaining major features and breaking changes
• Adding command or API examples from documentation changes

Check a new internal Marin developer’s local environment and access to development services. Distinguish setup problems from missing permissions, repair authorized local issues, and provide a checklist of verified capabilities and remaining access requests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a new Marin developer
• Validating a completed development setup
• Diagnosing missing service access

Register Hugging Face datasets for Marin by inspecting their structure without downloading the full dataset. Identify text fields, configurations, and data splits, then add a dataset module and validate the mapping with a sample.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Registering a Hugging Face dataset for Marin
• Inspecting text fields without a full dataset download
• Checking configurations and data splits

Upload model weights to Hugging Face Hub with an OpenMDW 1.1 license and a required research warning. Check the published README and license file after uploading or updating to confirm that the required information is present.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Publishing model weights to Hugging Face Hub
• Checking documentation after a model update
• Adding OpenMDW 1.1 license files and metadata

Forecast sales, sensor readings, and other single-variable time series with Google's TimesFM, without training a custom model. Accept CSV files, DataFrames, or arrays and obtain forecast values with prediction intervals that express uncertainty.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Forecast monthly sales from a CSV file
• Forecast demand across multiple stores
• Plot sensor forecasts with prediction intervals

Use pre-trained models with Hugging Face Transformers for text generation, translation, image recognition, and speech tasks. Load models, prepare inputs, run predictions, and fine-tune them, meaning further training with your own data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prototyping text classification, summarization, or translation
• Building image recognition or speech recognition workflows
• Adjusting text generation settings

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare code generation models on shared benchmarks
• Measure code generation performance across languages
• Evaluate instruction-tuned coding models

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Use LaminDB to organize biological datasets, search their metadata, and track how analyses produce results. Standardize annotations with biological vocabularies, validate data structures, and record versions and computational history for reproducible research.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Standardizing cell type annotations in single-cell data
• Searching datasets across multiple experiments
• Tracing analysis results back to their inputs and code

Create synthetic defect images for detector training using clean images and masks that mark defect regions. Guide mask placement, Cosmos fine-tuning, image generation, evaluation, quality refinement, and automatic assignment of provisional labels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating synthetic PCB defect images
• Placing defect masks on clean images
• Fine-tuning Cosmos for a defect dataset

Prepare a public CodonFM v1 environment and download supported Encodon checkpoints, the files containing trained model weights. Covers container or host setup, GPU access checks, and verification of downloaded weights and configuration files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a CodonFM development container
• Set up CodonFM directly on the host
• Download public Encodon model weights