데이터·AI 스킬

Fit regression and time series models in Python, then examine coefficients, uncertainty, and predictions. Check model assumptions, investigate unusual observations, and compare alternatives using detailed statistical summaries and diagnostic tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Interpret regression coefficients and confidence intervals
• Model binary outcomes or event counts
• Forecast time series with uncertainty intervals

Check whether Kiln’s base models still support fine-tuning, the process of adapting a model with additional training. Compare configured identifiers and Fireworks’ allowlist with provider information, then report unsupported entries and recommend specific updates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit support for fine-tuning base models in Kiln
• Check whether configured fine-tuning identifiers remain valid
• Find stale entries in the Fireworks allowlist

Audit Kiln’s model list against provider APIs and catalogs to identify removed models and upcoming retirements. Get findings organized by provider, then mark affected entries as deprecated after reviewing and approving the proposed changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit Kiln’s model list for retired entries
• Identify models approaching their retirement dates
• Check model availability for a single provider

Maintain Kiln’s list of large language models by discovering new releases, testing provider access, and marking unavailable entries as deprecated. Prepare pull requests, report unresolved issues, and check whether the updated list needs publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run the scheduled model sweep manually
• Add new models and test provider access
• Mark unavailable model entries as deprecated

Use a transaction hash, its identifying code, to retrieve records from TRES Finance. The skill draws asset movements and network fees, then explains the action, participants, amounts, and net result in plain English.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understand a transaction from its hash
• Visualize assets sent and received in a token swap
• Check network fees separately from other asset movements

Retrieve economic data from official statistics and established research sources after confirming price basis, seasonal adjustment, frequency, and currency. Return structured tables with source details and access dates, plus loading guidance for the user’s statistical software.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Retrieving GDP or employment data from official sources
• Confirming consistent specifications for country comparisons
• Finding replication data for published research

Write and review R analysis scripts for a sewage research project using its established conventions. Estimate requested regression models, add specifications, and save LaTeX tables, figures, and model objects for use in the research paper.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate models of sewage spills and property prices
• Add specifications to an existing regression analysis
• Create descriptive plots and maps

Analyze ML and AI experiment data with statistical tests, effect sizes, confidence intervals, and figures. The skill validates comparisons and produces an analysis report, statistical appendix, and figure catalog while identifying missing evidence and limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare model performance across repeated experiment runs
• Measure the effects of removing or changing model components
• Generate comparison charts and training curves from real data

Choose quantitative or qualitative methods that fit a research question and dataset. Compare alternatives, check assumptions, generate analysis code, interpret results, and examine sensitivity, meaning how conclusions change under different analytical choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing methods for a research question
• Generating analysis code in R or Python
• Planning theme identification in interview data

Learn from top Kaggle solutions across text, images, time series, tabular data, and multimodal tasks that combine data types. Explore technical analyses, model settings, reusable code templates, and practical lessons to inform machine learning projects.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Study top solutions for a Kaggle competition
• Find proven methods for a machine learning task
• Explore reusable machine learning code templates

Prepare research papers for RAG, a method that supplies retrieved documents to AI. Download PDFs, extract and split text, and build a local searchable database, with parallel processing for document batches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a RAG document collection from selected research papers
• Download and extract text from batches of paper PDFs
• Create a local vector database without paid embedding APIs

Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore distributions, missing values, and relationships
• Run regressions on cross-sectional or panel data
• Export publication-ready regression tables and figures

Build research workflows that regenerate results from raw data with a single command. Organize project files, configure workflow tools, manage software environments and data versions, and prepare replication packages for journal submission.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up folders for a new research project
• Automating analysis and figure generation
• Debugging results that differ across computers

Analyze U.S. electoral district plans in R with redistverse. Guides data preparation, simulations under population and geographic constraints, diagnostic checks, and comparisons of district shape, partisan fairness, and administrative splits through maps and summary tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing census, election, and geographic data
• Simulating district plans under population constraints
• Comparing enacted plans with simulated alternatives

Build and estimate economic models that represent underlying choices and incentives. Get guidance on demand, dynamic choice, and auction models, select estimation methods, and diagnose numerical problems such as failed convergence or sensitivity to starting values.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose an estimation method for a structural economic model
• Implement BLP demand estimation
• Estimate dynamic choice and auction models

Choose and implement machine learning methods for estimating causal effects with many control variables or effects that differ across groups. Guidance covers DML, causal forests, and related methods, including diagnostics and limits on interpretation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimating average effects with many control variables
• Studying how treatment effects differ across groups
• Implementing cross-fitting for causal estimation

Analyze strategic interactions in markets, including firm entry, pricing, auctions, and bargaining. Guides equilibrium selection and computation, explains how to handle multiple possible outcomes, and helps assess whether data can identify the model's parameters.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing and computing an equilibrium
• Planning an analysis of firm entry or price competition
• Handling multiple equilibria in empirical models

Estimate policy effects by constructing a weighted comparison from untreated regions or other units. Guides donor selection, weight fitting, placebo tests, and reporting, with examples in R, Python, and Stata for studies with few treated units.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluating a policy introduced in one region
• Selecting and weighting comparison units
• Testing results with fake treatment dates or units

Estimate effects by comparing observations just above and below an eligibility threshold using regression discontinuity design. Covers plots, comparison range selection, sharp and fuzzy designs, validity checks, and sensitivity analysis for reporting research findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate a policy effect near an eligibility threshold
• Analyze treatment with imperfect compliance
• Check manipulation and covariate balance at a cutoff

Create summary tables, treatment and control comparisons, and correlation matrices using Python, R, or Stata. Summarize averages, variation, sample sizes, and missing data to understand a dataset before regression or prepare tables for a research paper.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore a dataset before regression
• Prepare a paper's summary statistics table
• Compare treatment and control groups

Search Kaggle datasets, competitions, notebooks, and models through its official command-line tool. Download public data into a chosen directory and preserve audit records and file hashes, identifiers used to check downloaded files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding Kaggle datasets and inspecting metadata
• Downloading public data into a chosen directory
• Discovering competitions, notebooks, and models

Generate a Stata .do file that compares every subset of optional control variables across parallel instances. The script ranks combinations by the focal variable’s absolute t-statistic, reports the top ten, and fits the highest-ranked model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing all subsets of optional control variables
• Keeping mandatory controls in every regression
• Preparing parallel regression runs in Stata

Guide economic time series analysis from initial plots to forecasting. Use Python, R, and Stata examples to check whether statistical properties remain stable over time, choose models, diagnose errors, and examine relationships between multiple series.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking trends and stationarity in economic data
• Building ARIMA forecasts and evaluating accuracy
• Examining relationships between multiple time series

Write, debug, and understand Stata code for data cleaning, statistical analysis, causal inference, and charts. The reference covers command syntax, common pitfalls, Mata programming, and community packages for estimation and reporting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Write and debug Stata .do files
• Clean and merge analysis datasets
• Implement regression and causal inference methods