데이터·AI 스킬

Build causal inference analyses to estimate intervention effects using templates drawn from Causal Inference: The Mixtape. Adapt code across Python, R, and Stata, with checks for method assumptions and guidance on language-specific packages and limitations.

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• Implement a difference-in-differences analysis
• Set up an event study and check parallel trends
• Build instrumental variable or discontinuity models

Find STATA code examples for empirical accounting research from published replication files. Adapt regression, matching, event study, and table export syntax to your variables, with citations to the papers behind each example.

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• Implement fixed effects regressions in STATA
• Find published DiD and matching code examples
• Export regression results as formatted tables

Estimate how an intervention affects outcomes using Bayesian causal analysis. The workflow confirms causal assumptions with the user, selects a suitable method, checks whether findings withstand challenges, and reports effects with uncertainty and limitations.

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• Estimating the effect of a policy or intervention
• Mapping causal relationships before analysis
• Selecting a method for observational data

Plan how to introduce AI into economic research, teaching, or policy analysis. Identify suitable tasks, choose tools with privacy and licensing in mind, and design reproducible data workflows with output checks and ongoing model monitoring.

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• Plan AI adoption for an economics research team
• Design data workflows that protect personal information
• Choose AI tools for teaching or policy analysis

Build Python code for economic models and policy evaluation, with guidance on choosing libraries, estimating effects, plotting results, and checking robustness. Covers macroeconomic simulation and causal inference, the analysis of cause and effect.

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• Implementing macroeconomic models in Python
• Estimating policy effects with difference-in-differences
• Checking assumptions and robustness in causal analysis

Write and debug Python regressions with PyFixest using a reference for model formulas, estimation functions, and standard errors. It also covers extracting results, comparing models, and producing regression tables and plots, including models that account for group differences.

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• Write fixed-effects regression code
• Check clustered standard error settings
• Compare models in tables and plots

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.

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

Fetch public social media data through the ScrapeCreators API, including profiles, posts, comments, transcripts, ads, and trends. Select the appropriate data endpoint and retrieve JSON, a structured data format, for research or further analysis.

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• Collect public profiles and posts across social platforms
• Retrieve video comments and transcripts
• Search ad libraries for company ads

Check CSV, Excel, TSV, and JSON tables for missing values, duplicates, unusual values, and inconsistent formats. Receive scores across 12 quality dimensions, an overall grade, and practical suggestions for fixing the detected issues.

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• Checking missing values and duplicate rows before analysis
• Reviewing date formats and mixed data types
• Verifying unique IDs and relationships between columns

Generate PNG or SVG charts from data for reports, monitoring alerts, and dashboards. Choose from line, bar, pie, candlestick, and heatmap charts, then adjust labels, colors, and annotations using Node.js without a browser or external API calls.

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• Creating chart images for reports
• Comparing multiple time-series trends
• Charting prices alongside trading volume

Explore SQLite and PostgreSQL databases through read-only queries. List tables and row counts, inspect columns and constraints, preview records, and generate Mermaid ER diagrams, which describe how tables relate to one another.

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• Getting an overview of an unfamiliar database
• Inspecting columns, constraints, and indexes
• Previewing records and running read-only queries

Run linear or logistic regression on CSV or Excel tables to examine relationships between variables. Get coefficients, measures of model fit, significance tests, and checks for overlapping predictors, with explanations in plain language.

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• Examining relationships between sales and numeric factors
• Analyzing factors associated with customer churn
• Checking coefficient significance and model fit

Turn JSON data into browser-ready infographics with metric cards, grouped bar charts, process diagrams, or mixed dashboards. Outputs include all styling and SVG graphics in one HTML file, with preset palettes and icons and no external dependencies.

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• Display team KPIs as metric cards
• Compare monthly or quarterly figures
• Illustrate business processes as flow diagrams

Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.

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• Consolidating separate agent memory systems
• Migrating SQLite and Markdown data to AgentDB
• Finding stored information with similar meaning

Train, deploy, and run predictions with neural networks using Flow Nexus and E2B sandboxes, isolated computing environments. Use existing model templates, distribute training across multiple environments, and check training progress and model performance.

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• Train a model to classify images
• Analyze text sentiment with an existing template
• Train an LSTM model for time series forecasting

Implement agents that use recorded task outcomes to choose future approaches. ReasoningBank supports matching past patterns, comparing strategies, retaining experience in AgentDB, and tracking how strategy success and learning change over time.

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• Choose task strategies using past outcomes
• Compare approaches to code review or bug fixing
• Match current situations to learned patterns

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

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• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

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• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors

Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.

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• Adding newly available router models to chat-ui
• Writing concise descriptions of model capabilities
• Updating production and development model entries together

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

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• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.

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• Train agents from historical experience
• Build decision-making for games and navigation
• Combine experience from multiple agents for training

Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.

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• Build agent memory that reuses past experiences
• Record and assess sequences of agent actions
• Consolidate similar experiences into reusable patterns

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

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• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Help text-only models work with images by converting visible text, layout, meaning, and visual clues into structured JSON. The skill also guides configuring image analysis providers and reporting unclear details from the returned evidence.

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• Reading text from an image with a text-only model
• Checking image layout and visual clues
• Locating pasted images for analysis