Skill.md files: teach Claude once, not every chat.

• SKILL.md

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name: env-snapshot
description: Captures tool versions, packages, and kernel info as a reproducibility record in notes/. Use when documenting the environment.
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• SKILL.md

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name: execute
description: Executes all registered notebooks, strips noisy cell metadata, and syncs Jupytext pairs. Use when asked to re-run notebooks or refresh outputs.
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• SKILL.md

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name: figures-gallery
description: Generates an HTML gallery of all project figures with captions and source notebooks. Use when reviewing figures.
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• SKILL.md

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name: freeze-check
description: Checks whether registered notebooks have current, stale, or missing outputs. Use before rendering or to verify freshness.
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• SKILL.md

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name: handoff
description: Writes a session handoff report to handoffs/ with project state, work done, decisions, and next steps. Use at session end or after significant work.
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• SKILL.md

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name: init
description: Fills all [FILL:] placeholders across the template to initialize a new research project. Use when setting up a freshly cloned project.
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• SKILL.md

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name: interpret-results
description: Writes academic prose interpreting regression output. Use when describing estimation results in manuscript-ready language.
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• SKILL.md

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name: literature-note
description: Creates a structured annotation note in references/ with sections for research question, data, findings, and relevance. Use when documenting a paper.
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• SKILL.md

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name: new-analysis
description: Scaffolds a method-specific analysis notebook (DiD, IV, RDD, LASSO, Panel FE) with boilerplate. Use when starting a new econometric analysis.
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• SKILL.md

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name: new-notebook
description: Creates a Jupyter notebook with Jupytext pairing and registers it in _quarto.yml. Use when adding a new notebook.
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• SKILL.md

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name: new-slide-deck
description: Creates a Quarto revealjs slide deck in slides/ with the project style guide. Use when a presentation is needed.
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• SKILL.md

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name: referee-response
description: Drafts a point-by-point response letter to referee comments with suggested edits. Use after a revise-and-resubmit.
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• SKILL.md

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name: regression-table
description: Formats estimation output as a publication-quality regression table with stars, SEs, and fit statistics. Use when creating a results table.
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• SKILL.md

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name: render
description: Runs the clean render pipeline (HTML, PDF, Word) via scripts/render.sh. Use when asked to render, build, or compile the manuscript.
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• SKILL.md

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name: robustness-table
description: Generates robustness check code and formats results as a combined table. Use for sensitivity analysis.
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• SKILL.md

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name: submission-prep
description: Runs pre-submission checks (word count, anonymization, citations, placeholders, cross-refs) and generates a checklist. Use before journal submission.
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• SKILL.md

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name: sync-tex
description: Transfers prose edits from latex/index.tex (Overleaf) back into index.qmd. Use after pulling LaTeX edits from a collaborator.
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• SKILL.md

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name: coding-guidelines
description: Standardized Python & Stata coding practices for empirical research projects
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• SKILL.md

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name: data-doc
description: Document datasets, variables, sources, and merge keys for replication
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• SKILL.md

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name: lit-review
description: Summarize academic papers, extract key findings, and identify research gaps
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• SKILL.md

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name: project-structure
description: Project directory organization and script naming conventions for research
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• SKILL.md

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name: referee-response
description: Structure responses to referee reports for R&R submissions
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• SKILL.md

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name: robustness
description: Checklist of empirical robustness tests for finance/economics papers
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• SKILL.md

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name: stata-c-plugins
description: Develop high-performance C/C++ plugins for Stata using the stplugin.h SDK. Use when the user asks to create a Stata plugin, write C/C++ code for Stata, accelerate a Stata command with C, build cross-platform Stata plugins, or translate/port a Python or R package into Stata. Covers the full lifecycle: SDK setup, data flow, memory safety, .ado wrappers with preserve/merge, cross-platform compilation, performance optimization (pthreads, pre-sorted indices, XorShift RNG), debugging, and distribution via net install. Also includes a translation workflow for porting Python/R packages to Stata — wrapping existing C++ backends when available, or writing C from scratch when not.
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