docent-doctor

docent-doctor — bootstrap or verify the docent environment

You are responsible for getting docent ready to render a film, or for confirming it still is. Two paths to walk depending on whether docent is already on PATH:

Path A — first run (docent not on PATH)

The user just installed the skills via apm install and has never used docent before. You need to clone the engine, bootstrap the cascade, and put the docent CLI on PATH.

Detect this case by running command -v docent. If it returns nothing, take Path A.

Execute these steps in order. Walk through each with the user so they see what's happening; do not paste the whole block silently.

1. Pick the engine location. Default:

   DOCENT_HOME="${XDG_DATA_HOME:-$HOME/.local/share}/docent/engine"
   

   Tell the user this is where the engine will live — about 1 GB after bun install + uv sync + Kokoro weights. They can override by exporting DOCENT_HOME before re-running.

2. Verify bun is on PATH. If command -v bun returns nothing, stop and tell the user:

   docent requires bun. Install it first:
     curl -fsSL https://bun.sh/install | bash
     exec $SHELL -l
   Then re-invoke /docent-doctor.
   

   Bun is the only prerequisite docent itself can't bootstrap (it is the runtime that runs the bootstrap).

3. Clone the engine (or fast-forward an existing clone):

   mkdir -p "$(dirname "$DOCENT_HOME")"
   if [ -d "$DOCENT_HOME/.git" ]; then
     git -C "$DOCENT_HOME" pull --ff-only
   else
     git clone https://github.com/benelser/docent "$DOCENT_HOME"
   fi
   

4. Bootstrap the cascade. This is the long step — uv sync pulls torch + transformers + kokoro, Kokoro downloads ~300 MB of voice weights, and bun install materializes Remotion. Allow 3-8 minutes on a warm machine, longer on a cold one.

   cd "$DOCENT_HOME" && bun packages/agent/scripts/doctor.ts --install --yes
   

   That command installs uv, ffmpeg, gh, apm if missing; writes ~/.local/bin/docent (the shim that lets you call docent <cmd> from any cwd); and records DOCENT_HOME at ~/.config/docent/home. Show the user the live output — every step is labeled.