Local VS Code

Run the same editor-first loop on your machine. The Dev Container matches Codespaces; a native install needs only uv and just for the core flow.

Set up

git clone https://github.com/DSA-Woodshed/dsa-study-packet.git
cd dsa-study-packet

Choose one lane:

Dev Container

Open the folder in VS Code and choose Dev Containers: Reopen in Container. The container provides uv, just, and watchexec.

Native tools

Install Python 3.14+, uv, and just, then run:

just setup
just doctor

watchexec is optional and only needed for just practice-watch. Nix users can run direnv allow for the pinned toolchain. Do not run .devcontainer/setup.sh directly on a local machine.

Start a rep

Choose an intent, a time budget, and an activity through the guided session, or start one rep directly. Basic practice needs no private service:

just session
just practice-start comments
just practice-start comments arrays two_sum

Optional reacto, clarp, and umpire labels support the same loop.

To study one exact pair without starting a rep, open its committed snapshot:

just practice-study linked_lists lru_cache

When ready, start a candidate pair with source or test focus.

just practice-start comments linked_lists lru_cache
just practice-start-tests linked_lists lru_cache

Your source and test file open under .challenges/workspace/. Write ordinary source comments or docstrings in your own words, save, then run just practice-next. The comments belong in the source file and need no prefixes, minimum count, or gate deletion. Implement the solution and add focused tests. If the tabs do not open, the command prints their paths; just practice-open tries again.

Continue and close

just practice-next       # state and one next action
just practice-test       # reference tests plus your tests
just practice-watch      # rerun on workspace changes
just practice-repl       # interactive exploration
just practice-open       # reopen both files
just practice-finish "one fix"

The committed solution under src/algo/ remains unchanged, and your workspace stays gitignored. Run just doctor when the toolchain looks wrong.

Candidate tests are not sandboxed. The runner bounds test time and cleans the pytest process group. Do not launch background daemons. Receipts detect ordinary staleness and incomplete runs; they are workflow evidence, not a tamper-resistant boundary.

Optional agent providers and their settings belong on the personal contribution fork. Restore only the tools you choose; they route through the same canonical session commands. A terminal session works without an agent.