데이터·AI 스킬

Analyze company earnings and revenue forecasts using Yahoo Finance data. Compare analyst consensus, meaning their combined expectations, across periods, track upward and downward revisions, assess forecast ranges, and review how past estimates compared with actual results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracking changes in earnings forecasts
• Comparing optimistic and pessimistic estimates
• Comparing growth forecasts with industry benchmarks

Find open-source AI tools in a bundled collection of over 2,500 repositories. Search from everyday requests, filter by category or programming language, and compare relevant options with a guide organized around practical uses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find tools for chatbots that answer using retrieved documents
• Compare frameworks for building AI agents
• Find AI tutorials for beginners

Find open-source ChatGPT and large language model projects in a bundled collection of over 2,500 entries. Filter by category or programming language, then compare relevant results with a guide organized around practical uses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find tools for chatbots that use retrieved documents
• Explore AI agent libraries
• Filter projects by category or programming language

Find open-source ChatGPT and large language model tools from a project’s curated list. This local Codex entry point passes requests to awesome-chatgpt-search to find projects such as chatbots, agents, prompts, and browser extensions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find open-source tools for AI development
• Look for RAG frameworks or agent projects
• Explore chatbots and browser extensions

Turn daily market observations, unusual activity, and news reactions into structured clues for investigating trading opportunities. Export hints.yaml for later analysis, with optional ideas from a language model added through an external command or a prepared file.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize daily market observations into reusable clues
• Explore AI ideas grounded in unusual market activity
• Prepare hints.yaml for concept synthesis or automated detection

Assess crypto market conditions with six measures covering Bitcoin trends, altcoin participation, leverage, and momentum. Generate a 0–100 score and a market posture report using free public data or an offline snapshot, without API keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking whether crypto market conditions favor risk-taking
• Reviewing altcoin participation and Bitcoin market share
• Checking whether perpetual futures funding is overheated

Analyze dated strategy returns to separate baseline exposure from performance beyond that baseline. Test stability across time and alternative comparisons, then produce JSON and Markdown reports showing how strong or fragile the evidence is.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test whether backtest returns exceed simple baseline exposure
• Check independent performance in live or out-of-sample returns
• Explain losses through baseline and strategy-specific behavior

Analyze historical stock prices to measure how long declines from local peaks to troughs lasted and how deep they were. Generate interactive HTML histograms and statistical reports to compare correction durations across sectors and company sizes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing historical correction lengths across sectors
• Examining decline durations by company size
• Estimating holding periods for pullback strategies

Group raw detection records into reusable concepts before designing strategies. The skill produces a YAML file with shared conditions, supporting evidence counts, hypotheses, signals that would invalidate them, and readiness flags for strategy drafting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organizing large sets of detection records
• Reviewing hypotheses before drafting strategies
• Merging concepts with overlapping conditions

Review a limited packet of user evidence to decide which preferences or collaboration habits deserve memory actions. Produces structured decisions to create, update, forget, or skip memories while excluding one-off tasks and sensitive information.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Decide which user preferences are worth remembering
• Review explicit requests to update or forget memories
• Exclude one-off tasks and activity summaries from memory

Assess whether user preferences and collaboration habits are worth remembering from a bounded Comet review packet. Return structured decisions to create, update, forget, or skip memories, excluding temporary task records, sensitive information, and unsupported assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess a user's explicit request to remember or forget something
• Identify reusable preferences and collaboration habits
• Separate project-specific preferences from global ones

Document and organize datasets in AWS catalogs by adding descriptions, owners, glossary terms, and custom metadata. The skill identifies the catalog platform first and preserves existing records when applying supported documentation and publishing changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add table descriptions, owners, and column comments
• Create a business glossary and classify datasets
• Prioritize assets missing documentation

Create managed analytics tables with Amazon S3 Tables using Iceberg, a table format for data lakes. Set up columns, data types, partitions, Glue catalog integration, and access permissions, then verify the table through Athena.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a table for analytics data
• Define columns, data types, and partitions
• Connect tables to the Glue catalog

Measure and improve an AgentCore agent’s answer quality, speed, and costs. Set up evaluators, continuous monitoring, automated release checks, and CloudWatch logs and traces that show how requests move through the agent.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measure answer quality with evaluators
• Monitor agent quality continuously in production
• Add pass or fail quality checks to CI/CD

Find AWS-curated prompts for AI coding agents covering prototype development, security checks, cost monitoring, and more. Also identifies separately installable agents for tasks such as quota management, database selection, and checking Bedrock model availability.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find a prompt for building an MVP
• Find a prompt for a Bedrock chatbot using document retrieval
• Find prompts for security checks or cost monitoring

Publish Amazon Redshift system logs to S3 Tables using Apache Iceberg, a table format supported by multiple query engines. Verify publishing and analyze historical query or connection activity through Athena or Redshift without loading the source cluster.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Enable system log publishing for Provisioned or Serverless Redshift
• Check log ingestion status and destination
• Analyze historical query performance in Athena

Analyze SageMaker Catalog metadata with SQL to count registered assets, track growth, check missing descriptions or ownership details, and compare daily snapshots to see how the catalog has changed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Track catalog asset counts over time
• Find assets without descriptions
• Audit asset ownership

Store embeddings, numerical representations of content, in Amazon S3 Vectors and search for similar items. The skill guides bucket and index setup, embedding generation, batch storage, and metadata filtering for retrieval augmented generation and infrequent queries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Store embeddings for retrieval augmented generation
• Search for similar content with metadata filters
• Create vector buckets and indexes

Export RDS or Aurora database snapshots to S3 as Apache Parquet, a format suited to data analysis. The workflow covers snapshot preparation, permissions, encryption, progress monitoring, and verification for analytics, backup, or migration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing database data for analysis with Athena or Glue
• Exporting data for Redshift Spectrum
• Exporting snapshots for backup or data migration

Run quantum computing experiments on AWS, from choosing quantum hardware or simulators to building programs and submitting jobs. The skill also helps check device availability, compare prices, and enforce spending limits for quantum hardware tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose quantum hardware or a simulator
• Build and run quantum circuits
• Run experiments across multiple parameter values

Plan and carry out a Teradata migration to Amazon Redshift, from source inventory and SQL conversion to data transfer, validation, performance comparison, and reporting. The agent uses reference guidance to generate scripts tailored to the environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess the complexity of a Teradata migration
• Convert Teradata SQL and BTEQ for Redshift
• Transfer data through S3 into Redshift

Build generative AI applications on Amazon Bedrock, from calling models to answering questions using retrieved documents and deploying agents. Includes guidance for content filters, model migration, prompt caching, access errors, usage limits, and cost tracking.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build document-based question answering
• Deploy agents or migrate them to AgentCore Harness
• Investigate access errors and throttling

Find AWS datasets using a table name, business keyword, column name, or S3 path. Search Glue Data Catalog, S3, S3 Tables, and Redshift to return matching table references, data formats, and storage locations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locate a table by name or business keyword
• Find tables containing a specific column
• Look up a catalog table from an S3 path

Import files and database records into an AWS data lake for analysis. Support one-time loads, recurring pipelines, and table migrations, with target format selection, row-count checks, missing-value checks, sample inspection, and scheduling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Import local or S3 files into data lake tables
• Build recurring imports from databases
• Move Snowflake or BigQuery data to AWS