Claude Skills by lancegui

Use throughout the EXECUTION of any analysis — while running, debugging, modeling, or cleaning data — to decide which decisions you may make on your own and which you must STOP and bring to the user first. Forces a human-in-the-loop checkpoint before any consequential analytical choice — changing the research design, estimand, or identification strategy; deviating from the framed question or pre-analysis plan; dropping/filtering/winsorizing data or changing the sample; choosing between materi...

Use when WRITING or EDITING analysis code, notebooks, or data pipelines in R, Julia, or Python — keeping the code minimal, surgical, and legible rather than over-engineered. Use whenever you're about to add a class/framework to a one-off script, refactor a working pipeline you were only asked to tweak, build configurability nobody requested, or rewrite someone's analysis while making an unrelated change — even if the user just says "add a column", "tweak this notebook", or "clean up this scri...

Use when reviewing a data analysis, notebook, script, model, or result — your own before you ship it, a colleague's before it's published, or one handed to you to "check" or "sanity-check" — in R, Julia, Python, or Stata. Hunts specifically for the silent-failure classes that pass code review but produce wrong answers — unchecked joins, leakage, fished specifications, unreconciled totals, undefined metrics, identification gaps, and structural-model failures (non-identified parameters, no reco...

Use whenever an analysis phase, decision log, artifact registry, run record, or subagent handoff needs to be created, updated, resumed, or compacted. Replaces the old habit of growing and rereading one long plan file with a small docs/analysis/ YAML index and task-specific records. Use at phase boundaries, before/after compaction, when recording approved deviations, when registering outputs, and when briefing fresh subagents from durable state.

Use whenever an analysis makes or implies a CAUSAL claim — "the effect of", "X caused Y", "the policy raised", "the treatment increased", "because we did X, Y changed" — or whenever you're running difference-in-differences, event studies, instrumental variables, regression discontinuity, matching, synthetic control, or panel fixed-effects models. Forces the identification strategy and its assumptions to be stated and tested BEFORE estimating, and treats the design-specific diagnostics (parall...

Use when computing, transforming, cleaning, joining, merging, aggregating, reshaping, or modeling ANY result from data — before you trust a number, a table, a model metric, or a chart. Establishes data contracts and invariants up front, validates assumptions before building on them, asserts join cardinality before every merge, and freezes validated results as regression baselines. Use this whenever you load a dataset, write a transform or cleaning step, do a join or group-by, fit a model, or ...

Use when building, cleaning, or assembling the DATASET an analysis runs on in R, Julia, or Python — the moment someone says "clean the data", "the data is messy", "build/assemble the panel", "construct the dataset", "merge these files into one dataset", "prep the data", "dedupe these records", "reconcile the totals", or "recode these variables". Owns the data-ingest-and-cleaning PHASE — decomposing raw sources → clean → join → dedup → recode → reconcile into a phased checklist and decisions l...

Use when the deliverable is a DESCRIPTION of what's in the data rather than an effect, a counterfactual, or a prediction — stylized facts, trends ("what's the trend in X", "how has Y changed over time", "plot the growth"), summary-statistics/Table-1 tables, distributions, descriptive maps/choropleths ("map where X is concentrated"), and the motivating-facts section of an empirical paper. The descriptive layer BENEATH the modeling fork: often the whole deliverable; otherwise the stylized fact ...

Use once an analysis brief or pre-analysis plan is APPROVED and it's time to actually run the analysis — build the dataset, estimate the primary specification, run the robustness suite, placebo tests, and alternative designs, then assemble the results. Drives execution through the plan step by step, validating each step (data-contracts) and stopping for the user at consequential decisions (analysis-checkpoints), and, for the INDEPENDENT pieces (robustness specs, competing designs, subsample c...

Use when making a FIGURE / CHART / PLOT for a paper, deck, or presentation — in ANY language (R/ggplot2, Python/matplotlib, Julia/Makie). Governs the visual style: 16.5pt text throughout (axes, legend, title), no y-label (the title carries the y-axis meaning; paper figures get no headline title), concise axis labels, clean theme with colorblind-safe Paul-Tol palette, B&W-legible (shape/linetype redundancy), and a mandatory visual self-check (no clipped text, no overlapping labels). Covers DiD...

Use BEFORE running a confirmatory analysis, an experiment readout, an A/B test, a policy evaluation, or any study whose credibility depends on not having fished for the result — especially causal/econometric work. Locks the hypotheses, estimand, primary specification, sample and exclusions, and the robustness suite while the outcomes are still unseen, so the analysis is confirmatory rather than a specification search. Use whenever the user says "evaluate whether X worked", "test this hypothes...

Use whenever the GOAL is a prediction that drives an action — predict, score, rank, flag, classify, forecast, or detect anomalies on units ("which pharmacy is likely diverting opioids", "which claim to audit", "who's likely to churn", "rank by risk"). Route by GOAL, not algorithm: a prediction deliverable is this skill even when a random forest does the work; a causal effect stays in causal-identification even when ML does the heavy lifting (double/debiased ML, causal forests, ML propensity s...

Use when finishing a piece of analysis and about to commit or push, when a directory has accreted files and it's unclear what is a deliverable versus scratch, when setting up or reorganizing a research repository, or when asked to clean up the directory, organize the repo, standardize naming, or make a project reproducible. Also fires throughout the work — when creating a new script, dataset, table, or figure, place and name it per the project's structure; and at phase boundaries (plan agreed...

Use BEFORE starting any data analysis, metric, model, causal study, or any deliverable built FROM data — "what's the trend", "is X driving Y", "how many users", "did the policy work", "build me a dashboard metric", "plot/map/visualize this", "make a figure/chart/map/dashboard/table of …", "build an interactive map of these facilities", or a dataset handed over to "look into". Fires for figures, maps, charts, dashboards, and summary tables built from a dataset, not only metrics — the from-data...

Use ONLY when the user directly asks to verify, check, reconcile, or reproduce a result ("verify this", "check these numbers", "does this tie out?", "is this right?", "reproduce it from a clean kernel", "confirm the figures match the text") — never auto-fire it before reporting, shipping, or calling an analysis done. Reconciles totals to source, reproduces the result from a clean session with a fixed seed, confirms the user-approved robustness ran (never initiating new checks itself), and con...

Use whenever an analysis estimates the PRIMITIVES of an economic model — preferences/utility, costs, information/consideration, search, or conduct — or needs a COUNTERFACTUAL the data doesn't contain (a merger, a new product, a tax, a removed friction, welfare/surplus, equilibrium re-pricing). Fires for structural demand estimation (logit, random-coefficients/BLP), supply-side markup-and-cost recovery, dynamic discrete choice (Rust/CCP), entry and dynamic games, auctions, limited consideratio...

Use when starting any data analysis, econometrics, or causal-inference task in R, Julia, or Python — establishes the Causal Powers discipline and routes to the right skill (question-framing, descriptive-evidence, pre-analysis-plan, analysis-state-management, data-contracts, data-preparation, analysis-craft, figure-craft, analysis-checkpoints, executing-analysis-plans, wrong-number-debugging, result-verification, causal-identification, structural-estimation, predictive-modeling, analysis-revie...

Use the moment a computed result looks wrong, surprising, suspicious, or "off" — a total that doesn't reconcile, revenue that tripled after a join, a mean that moved, a coefficient with the wrong sign, a count that's too high or too low, a metric that disagrees with another team's. Bisects the data pipeline to find the exact step where the number went bad, tracing provenance backward instead of guessing-and-patching. Use whenever the user says "this number looks wrong", "why is this so high",...