데이터·AI 스킬

Evaluate AI agents by preparing test cases, scoring responses and tool use, investigating failures, and comparing results after changes. Supports built-in checks and custom scoring rules, including evaluations performed by a language model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create test cases for an AI agent
• Check response quality and tool use
• Investigate failed evaluation cases

Turn analysis results into charts suited to the question, such as trends, comparisons, or distributions. Save a chart image and the script that generated it, with a short explanation of the findings and relevant caveats.

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• Show changes over time
• Compare values across categories
• Visualize relationships between two numeric variables

Answer business questions about counts, trends, comparisons, or rankings using SQL, a language for querying tables. Validate the results, then present a concise answer, a small table, the executed query, and supporting checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Counting records for a defined period
• Checking how a business metric changes over time
• Comparing groups over the same period

Calculate the count, sum, minimum, maximum, and average of supplied numbers or datasets, then present the results in a clear, structured format. Analysis results can also be saved to a file when requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check the count and total of supplied numbers
• Summarize minimum, maximum, and average values
• Get a basic numerical summary of a dataset

Create and refine YAML prompts for AI assistants using structured roles, requirements, and output instructions. Organize reusable templates with variable placeholders, and check indentation, braces, headings, and formatting for consistency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating system prompts for AI assistants
• Building prompt templates with replaceable variables
• Reviewing YAML prompt syntax and formatting

Guides the design of data pipelines, continuous stream processing, and data quality checks. It compares tools such as Airflow, Dagster, and Flink, and provides implementation patterns and checklists for scheduling, retries, validation, and monitoring.

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• Compare tools for data pipeline scheduling
• Design continuous data processing
• Define checks for missing, duplicate, or stale data

Routes AI development questions to focused references on agents, language model security, retrieval-augmented generation, which uses retrieved information in answers, and prompt design and evaluation. It also outlines principles for validation, safety boundaries, version control, and ongoing assessment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find guidance for agent development
• Locate references on prompt injection defenses
• Explore retrieval and reranking for RAG

Extract data from websites or saved HTML using a suitable fetching method for each page. The skill generates and runs scripts for static pages, form-based logins, JavaScript rendering, and sites with Cloudflare or WAF protection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting data from static web pages
• Collecting data after a form-based login
• Fetching pages that require JavaScript rendering

Extract keyword-matched entries from an Excel sheet, clean whitespace and empty values, and count distinct entries by category. Export separate Excel files containing summary counts and detailed lists for review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Counting distinct facility names by category
• Extracting entries that match specific keywords
• Creating summary and detail reports from Excel data

Calculate averages for selected groups in Excel and sum items extracted from specified row ranges after removing duplicates. Save filtered records, summary statistics, and item totals as Excel files with local download links.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate average scores for a selected class
• Sum equipment power values without duplicate items
• Export filtered records and summary statistics

Clean category and numeric fields in structured data, compare groups with counts and cross-tabulations, and produce charts, Excel results, and a Markdown report. Export visuals as high-resolution PNG and SVG files for use in reports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare category distributions across groups
• Clean category labels and fill merged-cell gaps
• Export analysis results to Excel

Clean a target Excel column, convert its values to numbers, and compare the calculated total with a summary sheet. Export the totals, match status, and difference in Excel and CSV files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Standardize numerical formats before calculating a column total
• Check detail totals against a summary sheet
• Export reconciliation results to Excel and CSV

Group Excel figures by department or product line, assign performance tiers, and calculate projections using growth rates set for each tier. Compare current and projected values in charts and summarize overall growth for target setting.

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• Setting sales targets by department
• Estimating production targets by product line
• Comparing projections under tier-specific growth assumptions

Clean Excel data and group records with similar numerical features. Produce a combined view of indicator trends, value distributions, category shares, and grouped data, then export the charts as an image and cleaned records as Excel.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing trends across multiple indicators
• Checking value distributions and category shares
• Grouping records with similar numerical features

Filter rows from Excel or previously converted Parquet files, a format designed for efficient data storage. Summarize matching records by group with counts, percentages, and totals, then export the summary to Excel.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Count records containing a specified risk level by work category
• Calculate group shares among matching records
• Summarize large datasets already converted to Parquet

Prepare large Excel datasets by combining sheets, using Parquet, a format for efficient table storage, and cleaning a selected text column to retain only Chinese characters. Export the cleaned data as an Excel file with a local download link.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Removing symbols and numbers from Chinese text columns
• Preparing large Excel datasets for analysis
• Combining sheets and exporting cleaned data

Combine selected columns from multiple Excel sheets, fill blank category cells with the preceding value, and rank records by a chosen metric. Export the top results to an Excel report with headings, borders, and formatted numbers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Combine metrics stored on separate Excel sheets
• Fill category gaps caused by merged cells
• Extract the top records by a selected metric

Extract selected ranges from Excel workbooks with multiple sheets, check missing values, and clean numeric data. Fit a straight trend line, save charts and CSV results, and convert larger tables to Parquet for subsequent analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking missing values and category counts in a selected sheet
• Extracting numeric pairs from selected rows and columns
• Creating a scatter plot with a straight trend line

Analyze large Excel workbooks by totaling values by category and identifying the highest result. The workflow can convert data to Parquet, a column-based storage format, and export a styled Excel report with highlighted results.

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• Assessing row counts across workbook sheets
• Converting large datasets to Parquet
• Finding the category with the highest total

Count rows across Excel sheets to assess data size, then analyze an existing Parquet file, a format for storing tabular data. Calculate category counts and percentages, and save the results as an Excel summary and a pie chart.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Counting rows across every sheet in an Excel workbook
• Calculating category counts and percentages from Parquet data
• Creating a pie chart of category proportions

Filter Excel rows using conditions across several numeric columns. Count rows across sheets, convert selected fields to numbers, remove duplicate rows and records with missing identifiers, then export the filtered results to a new workbook.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract rows where several numeric columns are zero
• Clean numeric fields before filtering
• Remove duplicate rows and missing identifiers

Combine data from multiple Excel sheets, clean inconsistent labels and merged cells, and summarize counts by category. Create Excel summary tables with percentages and totals, plus labeled bar charts for comparing categories.

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• Summarizing data across multiple Excel sheets
• Cleaning merged cells and inconsistent category labels
• Comparing category counts and percentages

Turn Excel or CSV data into an Excel report with category totals, percentage shares, and a doughnut chart. The workflow identifies likely category and numeric columns, cleans missing values, and highlights the largest share.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare spending shares by category
• Summarize quantities or scores by category
• Create an Excel report with a doughnut chart

Find key figures in tabular data using column names or row keywords, calculate category averages and percentages, and export the results as a structured Excel report and a pie chart.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find figures using keywords in rows
• Calculate averages for a selected category
• Calculate one figure as a percentage of another