Dual-Channel Document Reading (Dual-Read)

Why: single-channel reading misses things. Dual-channel = the main agent reads + a subagent reads independently + cross-compare. Measured (single environment, 5 long PDFs; numbers vary with document and environment): after the main agent's own read felt complete, the subagent still surfaced 4-5 key omissions (rule details, numeric limits, full clauses — the "you don't know what you missed until you see it" kind).

Flow (strict order)

1. The main agent reads first

Run the extraction script or Read directly, and form a preliminary conclusion (document type + key-facts list). Cross-compare against your own answers — do not wait blank-minded for the subagent to feed you.

2. Extract to plain text (mandatory for PDF/Office)

• PDF: use fitz (PyMuPDF) to extract the full text page by page to a .txt, with page separators like ===== page N =====; one txt per PDF.
• If your environment's path-safety policy blocks open(variable_path): safe template = the script only print()s to stdout and you redirect with the shell (python extract.py 1 > pdf_txt_a.txt).
• Why: a subagent's Read cannot read binary PDFs; plain text is the robust route (convert first, then dispatch).

3. One subagent per file, dispatched in parallel

Send multiple Agent calls in one message (parallel, never serial).

Role choice:

• researcher: plain-text close reading, summaries, fact extraction (txt is the robust input; Bash availability depends on your role config)
• general-purpose / worker-coder: when the subagent must run scripts itself on PDF/Office/images

The subagent prompt must be self-contained (it starts from zero, cannot see the main session):

1. The txt's absolute path + how long it is and how it's segmented
2. Context: what we already know (existing knowledge base / product library / known conclusions) so the subagent doesn't treat existing content as news
3. The specific questions to answer (the more specific the better — e.g. "does the cooling-off period state a specific number of days? give the page and the sentence")
4. Output format (list-shaped, every item with a source)
5. Traceability rule: every fact carries a page/line number; if it's not in the text, write "not found in text" — never fill in from imagination

Long files (>50K chars): explicitly instruct "do not read linearly; Grep for keywords and read only around hits", and provide the keyword list (both languages).

4. Tri-classified comparison (the main agent arbitrates)

• match: both reads agree → high confidence, adopt directly
• addition: the subagent found what the main agent missed → spot-check 1-2 items against the original before adopting
• conflict: the two disagree → the main agent returns to the original text and arbitrates on the actual sentences — no fence-sitting, no splitting the difference

5. Output

• Verdict table: one row per file (match/addition/conflict + reason + page source)
• Explicitly list "additions found by the subagent" and "conflicts arbitrated" — that's the proof of the dual channel's value

When you can skip dual-read

• Files <10K chars with no key-fact extraction (browsing, existence checks)
• Code diffs / config changes (that's the reviewer flow, not this)
• The user says "just take a quick look"

When dual-read is mandatory

• Key-fact extraction: numbers, limits, rules, clauses, dates
• The user has questioned reading accuracy before, or said "don't miss anything / read carefully / double-check"
• The content will go into a knowledge base / product library / external material (one wrong entry = compliance risk)

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Shares the same evidence tri-classification (match / addition / conflict) with web-research-fanout in this pack.