Load this when the user ran /graphify add <url> or passed --watch. Neither is part of the default build.
Fetch a URL and add it to the corpus, then update the graph.
$(cat graphify-out/.graphify_python) -c "
import sys
from graphify.ingest import ingest
from pathlib import Path
try:
out = ingest('URL', Path('./raw'), author='AUTHOR', contributor='CONTRIBUTOR')
print(f'Saved to {out}')
except ValueError as e:
print(f'error: {e}', file=sys.stderr)
sys.exit(1)
except RuntimeError as e:
print(f'error: {e}', file=sys.stderr)
sys.exit(1)
"
Replace URL with the actual URL, AUTHOR with the user's name if provided, CONTRIBUTOR likewise. If the command exits with an error, tell the user what went wrong - do not silently continue. After a successful save, automatically run the --update pipeline on ./raw to merge the new file into the existing graph.
Supported URL types (auto-detected):
.txt on next run (requires pip install 'graphifyy[video]').md with tweet text and author.md.pdfStart a background watcher that monitors a folder and auto-updates the graph when files change.
$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
graph.json and GRAPH_REPORT.md are updated automatically.graphify-out/needs_update flag and prints a notification to run /graphify --update (LLM semantic re-extraction required).Debounce (default 3s): waits until file activity stops before triggering, so a wave of parallel agent writes doesn't trigger a rebuild per file.
Press Ctrl+C to stop.
For agentic workflows: run --watch in a background terminal. Code changes from agent waves are picked up automatically between waves. If agents are also writing docs or notes, you'll need a manual /graphify --update after those waves.