데이터·AI 스킬

Helps startup leaders make strategic data decisions: assess permission to train AI on customer data, choose data architecture, evaluate customer data as a business asset, and sequence hiring around the decisions the business needs to make.

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• Assessing customer data for AI training
• Choosing data architecture and what to build or buy
• Evaluating data assets before fundraising or M&A

Evaluate a Claude Managed Agent against a written rubric, a set of checkable criteria, and guide improvements within a fixed attempt limit. After a version passes, prepare separate evaluation cases to check performance beyond the examples used for revision.

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• Defining checkable criteria for agent outputs
• Improving an agent within a fixed attempt limit
• Testing a passing version on unseen cases

Coordinate AI agents acting as company executives through shared question and response formats. The skill defines limits on repeated calls, independent analysis, peer checks, conflict reporting, and decision records to support cross-functional reviews and board meetings.

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• Coordinating financial, technical, and hiring analysis
• Running board meetings with independent role perspectives
• Making conflicting findings and assumptions explicit

Scope a Claude Managed Agent through six questions about its job, trigger, inputs, actions, success criteria, and recurrence. Produce a validated build sheet with configuration components, deferred features, and an evaluation plan, without requiring an API key.

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• Turn an agent idea into a concrete build plan
• Define success criteria before implementation
• Separate the initial scope from later integrations

Create and refine Jupyter notebooks for experiments, exploratory analysis, or tutorials. Bundled templates help organize runnable code and short explanations into clear steps, while validation guidance supports notebooks that others can read and rerun.

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• Starting a notebook for an experiment
• Turning scripts or notes into a structured notebook
• Creating a step-by-step teaching notebook

Creates charts from numerical data using the chart tool and Chart.js v4. Supports bars, lines, pies, scatter plots, and other formats to show comparisons, trends, proportions, and relationships, with options for dataset colors and mixed charts.

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• Comparing values across categories with a bar chart
• Showing monthly changes with a line chart
• Displaying proportions with a pie or doughnut chart

Turn everyday questions into SQL queries, the commands used to retrieve and summarize database records. Uses your database structure and platform to produce queries with explanations, performance notes, and optional validation scripts.

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• Extract users who meet specific activity conditions
• Build revenue reports by region and customer tier
• Create queries from database diagrams

Analyze reviews, surveys, and other user feedback by user group. Summarize satisfaction scores, recurring complaints, positive themes, and the goals users want to achieve, then recommend product improvements based on frequency, severity, and business impact.

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• Finding recurring themes across large volumes of user feedback
• Comparing satisfaction across user groups
• Prioritizing product improvements from customer complaints

Analyze A/B test results to decide whether to launch a change, extend testing, stop, or investigate further. Check sample size, statistical significance, uncertainty ranges, and effects on other important metrics, then summarize the recommendation and next steps.

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• Checking whether an A/B test shows a meaningful difference
• Reviewing sample size and test duration
• Deciding whether to launch a tested change

Analyze users grouped by signup period or another shared characteristic to compare retention and feature adoption over time. Produce charts, highlight unusual changes, and recommend follow-up research or experiments to investigate the patterns.

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• Comparing retention across signup months
• Tracking adoption of a new feature by user group
• Finding periods with unusual user drop-off

Design a product metrics dashboard with clearly defined measures, data sources, charts, targets, and alert thresholds. The skill produces a Markdown specification that also covers dashboard layout, review schedules, and responsibilities for responding to alerts.

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• Defining product KPIs and their calculation rules
• Reviewing whether existing metrics support decisions
• Planning dashboard charts and layout

Generate text and images through a reverse-engineered Gemini Web API, an interface reconstructed from the web service. Use reference images for descriptions or variations, and continue conversations across requests with saved sessions.

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• Generating images from written prompts
• Describing the contents of reference images
• Creating variations from an existing image

Retune a collection of skills for a new model using archived runs and controlled comparisons. Establish normal measurement variation, register success criteria before editing, and make targeted changes until measured behavior meets the criteria or a specific limitation is reported.

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• Investigating skill regressions after switching models
• Measuring the effect of instruction changes
• Separating behavior improvements from normal run variation

Manage the AI models offered through OpenWork, including model additions, removals, aliases, and pricing discounts. Validate configuration consistency and trigger the GitHub workflow that refreshes models from models.dev, then report its merge or failure result.

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• Adding or removing models offered through OpenWork
• Changing the charged price fraction for specific models
• Checking consistency between model lists and aliases

Design AI agents and chatbots in n8n, choosing suitable nodes and connecting models, tools, conversation memory, and structured JSON output. Includes guidance on document retrieval and human approval before tool execution.

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• Building a chatbot with conversation memory
• Connecting external tools to an AI agent
• Returning data in a defined JSON format

Develop applications with the Claude API using examples from Anthropic's official cookbooks. Explore document summarization, image analysis, document retrieval, external tool calls, and prompt caching through practical Python code and implementation guidance.

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• Learning basic Claude API integration
• Building an assistant that searches documents
• Extracting text and information from images

Build Python applications that collect live cryptocurrency prices, trades, and order books with Cryptofeed. Covers consistent data formats across exchanges, price comparisons, and writing incoming data to storage systems for monitoring and analysis.

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• Monitoring prices across cryptocurrency exchanges
• Analyzing order book depth
• Analyzing executed trade flows

Read market details and public order prices directly from Polymarket. Match outcomes such as Yes or No to their token identifiers, then use the best buying and selling prices to calculate a midpoint.

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• Look up a Polymarket market by its identifier
• Match Yes or No outcomes to token identifiers
• Calculate a midpoint from public order prices

Convert queries, schema definitions, and stored procedures from database dialects such as Oracle and Teradata into BigQuery GoogleSQL. Coordinate single-query or batch translation, configure conversion rules, and inspect errors and behavioral differences in the results.

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• Translate existing database queries into BigQuery GoogleSQL
• Convert multiple SQL files and schema metadata in batches
• Configure schema redirection and object renaming

Create, deploy, and troubleshoot custom functions in BigQuery for calculations, data processing, and API calls. Choose among SQL, JavaScript, Python, and externally hosted functions, with guidance on dependencies, permissions, and validation.

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• Create reusable data normalization and aggregation functions
• Use Python packages for custom calculations in queries
• Call translation and other APIs from BigQuery

Design hybrid search on AlloyDB by combining meaning-based search, keywords, and SQL filters. Turn requirements into architecture diagrams, database setup scripts, deployment guidance, and validation plans for search applications on Google Cloud.

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• Designing product search with meaning-based matching and attribute filters
• Planning result reranking and AI quality checks
• Preparing AlloyDB and Cloud Run deployment guidance

Design an enterprise search system that answers questions using retrieved company content, an approach called RAG. Gather requirements and produce architecture diagrams, deployment guidance, and validation plans using GKE, AlloyDB, and open models.

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• Designing conversational search for internal documents
• Planning RAG with a vector-enabled SQL database
• Hosting open models on GKE

Design Google Cloud systems where multiple AI agents process live audio, video, or text and respond in real time. Guides requirements discovery, architecture diagrams, infrastructure code, deployment instructions, and validation for technical guidance or safety monitoring workloads.

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• Design real-time technical guidance using live audio and video
• Plan hazard detection and visual inspection from video streams
• Create cloud architecture diagrams and deployment instructions

Design a Google Cloud system for data analysis or machine learning with AI agents. Turn requirements into product choices, an architecture diagram, infrastructure configuration, deployment instructions, and validation steps, with user reviews between phases.

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• Design a data analysis system with multiple AI agents
• Choose Google Cloud products for analytical requirements
• Prepare an architecture diagram and deployment guide