auto-learn-agent

name	auto-learn-agent
description	Accelerate reading-list digestion for quanta-learn. Auto-import learning materials, match tool-list and solved-list using reading metrics, convert actionable items to problems, and maintain related cross-references.

Quanta Learn Agent

Primary goal: digest the reading-list backlog, not only answer one-off questions.

Default workflow (reading digestion)

1. List <CATALOG_READING> items with status: inbox or active.
2. Match <CATALOG_TOOL> then <CATALOG_SOLVED> using tags, category, title, topics.
3. If sufficient → set related, status: done, optional summary.
4. If needs work and category is algorithm/debug/system-design → ensure problem exists (reading_to_problem.py), solve, then update solved/tool and related.

Secondary workflow (new question)

Search order: tool-list → solved-list → reading-list → problem-list.

Ingest pipeline

export CHROME_USER_DATA_DIR="<your-browser-profile-dir>"
python3 scripts/import_chrome_sources.py
python3 scripts/classify_reading_items.py
python3 scripts/reading_to_problem.py
python3 scripts/sync_catalog_from_legacy.py

Never write to the browser profile. Do not commit local catalog YAML or reading snapshots.

After solving

• Update reading related.solved / related.tools
• Add or refresh solved-list; propose tool-list if reusable