Claude Code My Workflow

My Claude Code Setup
Actively maintained. A summary of how I use Claude Code for academic work — slides, papers, data analysis, and more — packaged so you can fork it for your own research. See CHANGELOG.md for the latest changes.

Live site: psantanna.com/claude-code-my-workflow

A ready-to-fork foundation for AI-assisted academic work. You describe what you want — lecture slides, a research paper, a data analysis, a replication package — and Claude plans the approach, runs specialized agents, fixes issues, verifies quality, and presents results. Like a contractor who handles the entire job. Extracted from a production PhD course and extended by a growing community.

Quick Start (5–10 minutes, plus ~30 min for first-time installs)
Before you start: Claude Code, git and Python 3 are the minimum. Python 3 runs the hooks, the gate suite (./scripts/backtest.sh — 10 checkers) and the quality scorer, and is pre-installed on macOS/Linux. To run the included HelloWorld demos end-to-end you also need XeLaTeX (Beamer sample) and Quarto (Quarto sample). R and the GitHub CLI are recommended. Full list in Prerequisites below. Fastest path: clone first, then run ./scripts/validate-setup.sh — it reports exactly what's missing with install links.
Only need Python/R/markdown? You don't need XeLaTeX or Quarto. The agents, rules, skills, and orchestration patterns work for any text/code artifact. Skip the HelloWorld demos and head straight to /data-analysis, /review-paper, /lit-review, or /review-r.
Session 2 onwards: MEMORY.md (committed) collects generic [LEARN] entries that help all forkers; machine-specific notes accumulate in Claude Code's native auto memory (~/.claude/projects//memory/, machine-local, never committed). See .claude/rules/meta-governance.md for the distinction.

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