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project-organization

Organizes empirical research repositories into paper-centric folder structures with standardized naming and git checkpoints.

2|Updated Jun 8, 2026
Search Tags:#git#data management#reproducibility#econometrics#repository organization#research pipeline#file naming
One-click install
npx skills add https://github.com/lancegui/causal-powers --skill project-organization-lancegui
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: project-organization
Source: https://github.com/lancegui/causal-powers/tree/main/skills/project-organization
Command: npx skills add https://github.com/lancegui/causal-powers --skill project-organization-lancegui

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Research repositories accumulate scripts, datasets, tables, and scratch files until no one can tell deliverables from diagnostics, making results irreproducible for collaborators and future you. ## Core Features & Use Cases - Paper-centric structure: Enforces a stage-first layout separating raw, intermediate, and output data from code, results, sandbox, and per-category archives. - Naming and git conventions: Standardizes snake_case filenames, bans dates and version suffixes, and defines what to track versus gitignore, including shrinking oversized data to parquet. - Phase-boundary checkpoints: Commits local checkpoints at milestones like a clean dataset or validated result, and promotes durable lessons to docs/LESSONS.md. - Use Case: Before pushing a week of difference-in-differences analysis, inventory every artifact, move superseded runs to archive/, rename outputs to traceable conventions, and commit a labeled checkpoint. ## Quick Start Ask the assistant to clean up and organize the research repository before committing, standardizing file names and archiving scratch files.

Frequently Asked Questions about project-organization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I organize an empirical economics research repository?▼

Organize around the paper and its pipeline: separate code, data (raw/intermediate/output), results, and scratch into stage-first folders with subject subfolders. Add a README and a master script so the whole pipeline reproduces with one command.

What folder structure should a data analysis project use?▼

Use paper/ for the manuscript, data/ split into raw, intermediate, and output, code/ ordered by pipeline stage, results/ for tables and figures, and sandbox/ for throwaway exploration. Keep an archive/ in each category for superseded but retained work.

Should I commit large datasets to a git research repository?▼

Track shareable data by default so replicators get what they need. Gitignore only sensitive data and files past GitHub's roughly 100 MB limit, and convert oversized files to parquet or compressed tsv first so they can stay tracked.

How should I name analysis scripts and output files?▼

Use lowercase snake_case with no spaces, dates, or version suffixes. Prefix scripts with run order like 01_clean_mortality.R, and name outputs by content and producer, such as tab_main_did_mortality.tex.

When should I commit checkpoints during a long analysis?▼

Commit locally at each phase boundary: when the plan is agreed, a clean dataset is built, a result is validated, or a bug is fixed. Local commits are private restore points; pushing remains an explicit user decision.

What should I do with old or superseded analysis runs?▼

Move superseded specs and retired results into the category's archive/ folder rather than deleting them. Archive is deliberately retained provenance, distinct from disposable sandbox scratch and regenerable diagnostics.

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