데이터·AI 스킬

Retrieve stock prices, exchange rates, cryptocurrency prices, company financial statements, and economic indicators through the Alpha Vantage API. It also covers historical data, news sentiment, and technical indicators, which summarize price and trading patterns.

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• Retrieve historical stock prices and trading volumes
• Collect company financial statements and earnings data
• Check foreign exchange and cryptocurrency prices

Work with DNA, RNA, and protein sequences in Python. Read and convert biological files, compare sequences, search for similar sequences with BLAST, retrieve NCBI records, analyze protein structures, and build or visualize evolutionary trees.

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• Convert GenBank files to FASTA
• Calculate sequence length and GC content
• Search with BLAST and retrieve matching sequences

Learn how to organize the instructions, conversation history, documents, and tool results an AI agent receives. Explains context limits and loading information as needed, helping you review agent designs and investigate behavior caused by poorly managed context.

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• Learning how context windows work
• Reviewing instructions and document loading in agent designs
• Investigating unexpected behavior in long conversations

Add language model features to a PinMe TypeScript Worker through its OpenRouter proxy. The skill guides code for listing models, chat responses, streaming text as it arrives, web search, and handling API errors.

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• Add AI chat to a PinMe app
• Display generated responses as they arrive
• Let a model search the web

Improve a Browserbase Agent's system prompt by running a fixed task, examining messages and logs, and changing one instruction at a time. Compare quality, duration, and message counts, then repeat runs to check consistency.

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• Refining prompts for a Browserbase Agent demo or proof of concept
• Diagnosing inconsistent Agent runs from recorded evidence
• Comparing prompt revisions by quality, duration, and message count

Create and edit Proteina-Complexa target definitions for protein binders, ligand binders, and enzyme scaffolding. Organize chain and residue selections in YAML configuration files, check saved settings, and verify structure file paths when a prepared installation is available.

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• Registering a protein target with contact residues
• Adding ligand codes and SMILES to target definitions
• Checking existing target settings and structure paths

Compare Proteina-Complexa design settings across all combinations of selected parameter values. Prepare sweep configurations, estimate GPU time, coordinate runs, and produce ranked CSV results that show success rates and trade-offs between quality and runtime.

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• Comparing combinations of search settings
• Estimating configuration counts and GPU time
• Ranking supplied results by success rate

Generate synthetic printed circuit board inspection images through a CDF workflow. Choose simulation, realistic clean images, defect generation, or the full pipeline, then validate settings, check GPU readiness, monitor execution, and trace defects to their source images.

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• Generate solder bridge defects from a board scene
• Create realistic clean inspection images
• Add defects to prepared clean images

Create clothing variations from cropped person images and build an augmented dataset with person attribute search. Prepare inputs, configure and monitor the Kubernetes workflow, retrieve outputs, and check for missing attributes or skipped images.

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• Create clothing variations from cropped person images
• Prepare data for identifying the same person across images
• Configure clothing attribute distributions and variation counts

Build validated simulation jobs from a board USD, a 3D scene file, for clean board images, pin variations, component placement defects, or solder defects. Return the compiled job payload for a separate executor to render.

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• Prepare jobs for clean reference images of circuit boards
• Build through-hole pin geometry variation jobs
• Configure component placement and solder defect datasets

Run the CDF Video Data Augmentation workflow to create Cosmos video variants and automatically label source and generated videos. Prepare and validate run settings, check available resources, monitor execution, and verify the separate training exports.

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• Generate video variants with different weather or times of day
• Automatically label source and generated videos
• Validate run settings and GPU availability

Find driving clips similar to an example video and generate rain, snow, or daylight variations. Prepare run settings, monitor the Airflow workflow, and inspect retrieved clips, generated videos, and validation results.

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• Find driving clips similar to an example video
• Generate rain, snow, or daylight variations of retrieved clips
• Prepare run settings without submitting a workflow

Evaluate existing protein structure files with Proteina-Complexa, including outputs from BindCraft, AlphaProteo, and RFdiffusion. Select a structure prediction backend, calculate interface and structural agreement metrics, and summarize individual designs, pass rates, and leading candidates in CSV outputs.

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• Evaluating an existing directory of PDB designs
• Assessing BindCraft, AlphaProteo, or RFdiffusion outputs
• Comparing ligand binder and motif design metrics

Generate videos of unusual events, such as falls or fires, from seed images. Run a workflow that adds object detection, tracking, captions, and other labels, then builds an anomaly dataset and retrieves the results.

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• Create fall or climbing clips from seed images
• Generate fire and smoke videos for surveillance datasets
• Build anomaly datasets with automatic labels

Helps prepare video and image datasets with Cosmos Curator through splitting, filtering, deduplication, captioning, and embeddings, numerical representations for matching. Covers pipeline configuration, execution checks, Cosmos Dataset Search ingestion and search, and compatible handoffs to TAO data mining.

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• Splitting and filtering videos into training datasets
• Deduplicating data and packaging it into archives
• Adding captions and embeddings to images

Create and validate Cosmos Data Factory YAML configurations for changing video scenes, editing images, or generating videos. Run generation through remote model APIs, configure captions and quality checks, and retry candidates within a defined limit.

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• Changing weather or lighting in source videos
• Editing selected attributes in still images
• Generating videos from an image or text

Guides CDF Auto-Labeling workflows for annotating data such as videos. Routes tasks to specialized references, covering shipped examples, stage selection, prompt and configuration changes, pipeline migration, container retrieval, and output validation.

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• Starting automatic labeling for warehouse-safety videos
• Adapting a shipped workflow to a new domain
• Writing annotation prompts and question banks

Generate protein structures and sequences with specified conditions using NVIDIA's hosted API or a local NIM service. Supports binder and antibody design, reward-guided search, and inspection of generated candidates, outputs, and validation results.

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• Generate protein candidates for a specified binding target
• Design antibodies or structures around a retained motif
• Explore candidates with reward-guided search

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.

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

Clarify an AI agent’s purpose, boundaries, and success criteria by reviewing its code and documents, then asking focused questions about intent. The resulting draft is handed to ethos-write to create an ethos.md record.

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• Define the purpose and scope of a new agent
• Document the intended behavior of an existing agent
• Clarify priorities when desired outcomes conflict

Add optional NVIDIA Parabricks GPU steps to an existing genomics workflow while retaining its CPU path. Use runtime switches to compare outputs, execution time, and cost tradeoffs before enabling GPU processing for production.

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• Identify genomics steps suitable for GPU acceleration
• Add switchable CPU and GPU paths to an existing workflow
• Compare CPU and GPU outputs and runtime

Prepare NVIDIA Parabricks commands for individual genomics tools, translate options from CPU tools, and check runtime readiness. Get guidance matched to the target release, including reference compatibility, command flags, and unresolved requirements before execution.

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• Choosing a Parabricks tool for genomic data
• Translating STAR options into command drafts
• Checking GPU and container readiness

Guides the definition and publication of business terms, relationships, and measures in Auto Ontology. Covers SQL validation, model import and export, and persisted-change checks to verify that saved definitions reflect the intended business meaning.

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• Define or revise business terms and relationships
• Check measure formulas, units, and aggregation levels
• Back up models and verify imports

Ask Auto Ontology questions in plain language, then check the generated SQL, returned data, and written answer. Supports MCP or authenticated REST access to verify that reported counts and other findings match the intended question.

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• Checking a count against its SQL and returned data
• Confirming business terms before asking a question
• Reviewing incomplete or truncated query results