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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

Analyze one Excel worksheet by cleaning column names, extracting numbers, and handling gaps from merged cells. Create category counts, percentages, cross-tabulations comparing groups, high-resolution charts, and CSV exports, with Parquet storage for the data.

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• Clean worksheets containing merged cells
• Summarize category counts and percentages
• Compare groups with cross-tabulations

Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.

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• Summarize Excel records using custom categories
• Compare category distributions across groups
• Score and grade text using adjustable rules

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.

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• 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 trends and group performance in Excel data, clean percentage values, and calculate projections using growth rates assigned by performance tier. Produce comparison charts and a summary of current totals, projected totals, and growth.

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• Track changes in business metrics over time
• Compare performance across departments or categories
• Estimate growth using rates assigned by performance tier

Extract numbers from text values containing a specified unit, remove unusable rows, and visualize the distribution. Create a histogram with mean and median markers, plus a dashboard showing frequencies, proportions, and cumulative percentages.

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• Extract numbers from spreadsheet values containing a unit
• Check a distribution against its mean and median
• Compare value frequencies and proportions

Design amino acid sequences from a protein backbone using SolubleMPNN, a model trained on soluble protein structures. Generate candidates with a solubility bias for redesigning aggregation-prone proteins or preparing an E. coli expression screen.

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• Redesign protein candidates that aggregate
• Adapt membrane-adjacent folds for soluble expression
• Generate candidates for an E. coli expression screen

Design protein sequences while accounting for bound small molecules, nucleic acids, or metals. Use LigandMPNN to redesign binding pockets or metal-coordinating sites, and export sequence files alongside PDB structures with the designed sequences applied.

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• Redesign a binding pocket around a small molecule or cofactor
• Design residues around a metal-coordinating site
• Export designed sequences as FASTA and PDB files

Design amino acid sequences from a protein backbone stored in a PDB file using ProteinMPNN. Generate multiple candidates, redesign selected chains while preserving specified residues, and vary sampling settings to prepare sequences for downstream structure prediction.

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• Designing sequences for RFdiffusion backbones
• Redesigning a chain while keeping interface residues fixed
• Generating candidates across multiple sampling temperatures

Simulate quantum systems in Python with QuTiP, including open systems that interact with their surroundings. Calculate time evolution, energy loss, and entanglement, then visualize states and results for physics research or educational experiments.

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• Simulate the decay of a harmonic oscillator
• Track changes in two-qubit entanglement
• Study interactions between an atom and a cavity

Explore design options in Python when goals such as cost and performance compete. This skill guides optimization, constraint handling, and visualization of trade-offs, then helps compare candidate solutions using stated preferences.

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• Comparing designs with competing cost and performance goals
• Finding solutions that meet design constraints
• Comparing optimization algorithms on benchmark problems

This skill covers Bayesian estimation and inference in quantitative social science. Use when the user is specifying priors, running MCMC, diagnosing chain convergence, or reporting posterior summaries — including hierarchical models, Bayesian structural models, and small-sample settings where priors regularize. Triggers on "Bayesian estimation", "Bayesian inference", "MCMC", "Markov chain Monte Carlo", "Stan", "PyMC", "NumPyro", "prior", "posterior", "credible interval", "Bayesian structural", "Bayesian BLP", "Bayesian DSGE", "hierarchical model", "random effects Bayesian", "posterior predictive check", "Bayes factor", "prior predictive check", "NUTS", "HMC", "Hamiltonian Monte Carlo", "R-hat", "rhat", "effective sample size", "ESS", "Bayesian calibration", "posterior distribution", "prior elicitation", "weakly informative prior", "brms", "rstanarm", "cmdstanpy", "pymc", "arviz".

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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.

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

Clean survey datasets using their codebooks, which explain variables and response codes. Check missing values and outliers, harmonize variables across years, and prepare combined data with a cleaning report, variable definitions, and recorded transformation history.

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• Checking newly downloaded survey data
• Harmonizing variables across survey years
• Preparing panel data for research

Prepare survey and administrative datasets for analysis with Python, R, or Stata. Generate cleaning code to handle missing values, duplicates, outliers, and derived variables, with documented transformations and integrity checks that support reproducible research.

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• Clean missing values, duplicates, and outliers in survey data
• Merge multiple data sources for analysis
• Prepare panel datasets that track units over time

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

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Create figures for machine learning papers from text or experiment data. Uses Gemini for architecture diagrams and matplotlib/seaborn for numerical charts, choosing suitable layouts and chart types while preparing consistent styling, export files, and reusable generation scripts.

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• Turning a methods section into an architecture diagram
• Charting model comparisons and ablation results
• Plotting training curves and confusion matrices

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

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World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

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Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

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Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

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Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files).

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World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

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Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib

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