financial-literacy-rag

name	financial-literacy-rag
description	Persistent retrieval over Atlas's lawful financial literature corpus: investing canon, Canadian tax sources, official filings, research papers, and CC-licensed books. Grounds stock picks, tax strategy, and financial literacy explanations in cited source chunks.
triggers	["RAG","literature","financial literacy","source-grounded","bibliography","canon","investing books","tax sources","embedded literature","retrieval","citations","research corpus"]
tier	core
dependencies	["financial-planning","accounting-advisor","portfolio-rebalancing"]

Financial Literacy RAG

Atlas uses this skill when a response, stock pick, tax strategy, or teaching moment should be grounded in the embedded literature corpus rather than memory alone.

Routing

Need	Module	Command
Embed Atlas docs	research/rag_pipeline.py	python -m research.rag_pipeline ingest-default
Embed a licensed local file	research/rag_pipeline.py	python -m research.rag_pipeline ingest-path <file> --permission-status cc_licensed ...
Embed licensed inbox	research/rag_pipeline.py	python -m research.rag_pipeline ingest-inbox --tier cc_licensed --topic investing_canon
Embed an official/open URL	research/rag_pipeline.py	python -m research.rag_pipeline ingest-url <url> --permission-status open_access ...
Retrieve context	research/rag_pipeline.py	retrieve_literature_context(query, top_k=6)
Stock-pick grounding	research/stock_picker.py	Automatic inside StockPickerAgent.pick() and deep_dive()

Source Rules

1. Full-text embedding is allowed only for public_domain, official_public, open_access, cc_licensed, or owned_or_licensed sources.
2. Copyrighted books/course texts without CC's lawful copy stay permission_required or metadata_only.
3. Source metadata must include title, tier, topic, year, permission status, publisher, and URL/path where possible.
4. The literature layer supplies frameworks, not live prices. Stock picks still obey research/_data_integrity.py.

CC-Licensed Works

If CC provides a lawful copy of a copyrighted book or course text, place it in research/literature_inbox/ (git-ignored), then ingest it as:

python -m research.rag_pipeline ingest-path "path/to/book.pdf" \
  --title "Book Title" \
  --tier tier_1 \
  --topic value_investing \
  --year 1949 \
  --permission-status cc_licensed \
  --license-note "CC-provided lawful access; internal Atlas research use."

Do not fetch unofficial mirror PDFs. Use local files supplied by CC, official publisher portals, or public-domain/open-access sources.