deep-read

name	deep-read
description	Deeply understand ONE large document or book — build a structured understanding (sections, claims, methodology, caveats, argument-structure) of a single source, depth-on-one-source vs deep-research's breadth-across-many. Use when the user wants to thoroughly comprehend one long document, paper, or book, run inside any coding agent host using the host's own tools (zero API-key setup).
version	0.1.0

deep-read

Deeply understand ONE large document, paper, or book. Where deep-research goes broad (many sources, adversarially fact-checked), deep-read goes deep on a single source — it builds a structured understanding of that one document: its sections, claims (each grounded in a verbatim quote), methodology, caveats, and argument structure.

This skill reuses exactly two of deep-research's extract primitives — schemas.py (EXTRACT_SCHEMA) and prompts.py (fetch_prompt) — as byte-identical copies (kept in sync with the deep-research SSOT by the MD5 CI check). It carries neither rank.py nor dedup.py: deep-read has no quorum step and its cross-chunk claim merge rolls its own claim-text dedup, so copying them would be dead code. It adds a stdlib chunker (chunker.py) plus the deep-read-specific schemas + cross-chunk synthesis + report renderer (deepread.py).

Executor model — who does what

You (the agent running this skill) are the executor. You supply the LLM reasoning and the read capability:

• LLM reasoning — you read each chunk, classify its section role, extract its claims with supporting quotes, and synthesize the merged understanding, emitting JSON that conforms to a bundled schema.
• Read capability — your host's own tools (Read, WebFetch, …) to turn the source into text, plus the parallel fan-out for per-chunk work.

The bundled scripts/*.py supply only deterministic logic — markdown chunking, prompt text, JSON schemas, claim-dedup, cross-chunk merge, and markdown rendering. They make no network calls and read no API keys. They are stdlib-only and run with plain python.

No API key is required. This skill borrows the host agent's own LLM + read tools (your existing subscription) — there is no key to set, no separate program to install, no per-call API cost.

No adversarial verify. Unlike deep-research's 3-vote quorum, deep-read does not cross-examine claims against other sources — there is only one source, so quorum has nothing to vote against. Grounding here is per-claim verbatim quotes: every extracted claim carries the exact sentence from the source that supports it, so a reader can check it against the original. That is the single-source analogue of deep-research's quorum.

Run all python scripts/… commands from the skill's own scripts/ directory (paths below are relative to it).

Portable fan-out convention

The per-chunk extraction (Step 3) does the same work across N independent chunks. Do this in parallel by dispatching N subagents, per code-toolkit:dispatching-parallel-agents: one fresh subagent per chunk, dispatched in a single assistant message with multiple agent calls so the harness runs them concurrently.

Describe and dispatch this work abstractly as "dispatch N subagents" — do not hard-code the Claude Code Workflow tool. Stated abstractly, the fan-out maps onto whatever concurrent-subagent primitive the host agent provides (Claude Code, Codex, Cursor, …); binding to one harness's workflow primitive would break agent-portability. Each per-chunk subagent is independent (disjoint chunk, no shared files) — exactly the case the fan-out convention is for.

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Step 1 — Ingest (content-agnostic)

deep-read processes text. Get the source as text by whatever your host can read — the format is the host's problem, not deep-read's. If the host can read it to text, deep-read can process it.

• Local .md / .txt / PDF → host Read tool (it reads PDFs to text).
• A URL → host WebFetch, or obsidian:defuddle for clean markdown with the page chrome stripped.
• An EPUB → tsundoku:book-extract to convert it into chapter-split markdown (one file per chapter).

These are optional skill-composition pointers, not dependencies of deep-read. deep-read itself only needs the text; how you obtained it does not matter. Use whichever your host supports for the source at hand.

Step 2 — Chunk

Split the document into ordered chunks so each can be reasoned over independently.

echo '<the document markdown>' | python scripts/chunker.py

stdin: markdown text → stdout: a JSON array of chunks, each {heading, text, ordinal}:

• heading — the #-line that starts the chunk ("" for any preamble before the first heading).
• text — the chunk body up to the next section-level heading. The chunker picks the section level automatically (the shallowest heading level that repeats, else the shallowest present), so a lone # title + several ## sections splits into one chunk per ## section (each ## swallows its nested ### subsections) — not one giant chunk under the title.
• ordinal — 0-based index in document order.

A heading-less document collapses to one chunk. If the source is already chapter-split files (e.g. tsundoku:book-extract output), skip the chunker and treat each file as one chunk — heading = the chapter title, ordinal = the file's order.

Step 3 — Per-chunk extract (fan out)

For each chunk, read it and pull out its claims. This is per-chunk independent work — fan out one subagent per chunk (see the fan-out convention above) so the chunks are read concurrently.

Each subagent reasons over its single chunk and emits a chunk extraction conforming to the CHUNK_EXTRACT_SCHEMA shape — a module-level dict in scripts/deepread.py (read it directly; it has no print subcommand, mirroring how cite-check exposes EXTRACT_CITED_CLAIMS).

Shape (CHUNK_EXTRACT_SCHEMA): {section, claims: [{claim, quote, importance}], methodology?, caveats?, openQuestions?} —

• section — this chunk's structural role (e.g. "Methods", "Conclusion"); usually its heading.
• claims — each a concrete statement plus a verbatim quote from the chunk that supports it, rated importance ∈ high | medium | low.
• methodology / caveats / openQuestions — optional section-local notes folded into the merged understanding.

This per-chunk shape is richer than deep-research's schemas.py extract (it adds the section role + methodology/caveats). You may reuse the prompts.py fetch framing and the schemas.py extract claim fields as a base, but the binding shape is CHUNK_EXTRACT_SCHEMA:

python scripts/prompts.py fetch --source '<chunk-as-source JSON>' \
  --label deep-read --question "What does this chunk establish?"
python scripts/schemas.py extract

Step 4 — Hierarchical synthesis

Collect every chunk extraction into one JSON array, then merge them into a single understanding.

echo '[<chunk extractions>]' | python scripts/deepread.py merge

stdin: the array of chunk extractions → stdout: a merged understanding conforming to READ_SCHEMA (also a module dict in scripts/deepread.py). The merge dedups claims across chunks (near-duplicate claims collapse to the first occurrence), assembles sections in chunk order with each claim tagged by its source section, and concatenates methodology/caveats/openQuestions. Shape: {sourceQuality, sections[], claims[] (each {claim, quote, importance, section}), methodology, caveats, openQuestions, argumentStructure}.

Step 5 — Render the report

echo '<merged understanding JSON>' | python scripts/deepread.py report

stdin: the merged understanding → stdout: a markdown READ report — a sections outline, a claims table (claim · section · importance · quote), then methodology / caveats / open-questions blocks. Hand this back to the user as the structured understanding.

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Boundary — what deep-read is NOT

• tsundoku:book-distill turns a book into executable skills and explicitly skips comprehension — it mines for reusable procedures. deep-read instead produces a structured understanding of the source.
• obsidian:wiki-ingest ingests vault notes into a knowledge wiki. deep-read works on one arbitrary document and emits an understanding report, not vault pages.

Use deep-read when the goal is to understand one long source deeply.

Script-invocation quick reference

Step	Command	stdin → stdout
2	chunker.py	markdown → chunk array {heading, text, ordinal}
3	deepread.py (CHUNK_EXTRACT_SCHEMA shape)	— → per-chunk schema (in source)
3	prompts.py fetch --source S --label deep-read --question Q	— → extract prompt (framing reuse)
3	schemas.py extract	— → extract schema (claim-field reuse)
4	deepread.py merge	chunk extractions → merged understanding (READ_SCHEMA)
5	deepread.py report	merged understanding → markdown READ report