sequential-read-preread

name	sequential-read-preread
description	Analyse source material and split into semantic chunks for sequential reading
user-invocable	false

Preread Phase — Semantic Chunking

You are performing the preread phase of a sequential reading session. Your job is to analyse the source material, split it into semantic chunks, and prepare the session for reading.

Inputs

You will be given:

• SESSION_ID — the session identifier
• SOURCE_FILE — path to the source text file
• BASE_DIR — path to the sequential_read skill directory

Procedure

1. Read the Source File

Read the entire source file using the read tool. Understand its structure, length, and type (novel, essay, article, poetry, non-fiction, etc.).

2. Decide Chunking Strategy

Choose an appropriate chunking approach based on the material:

Material Type	Default Approach
Novel with chapters	One chunk per chapter (combine very short chapters)
Novel without chapters	Scene breaks or ~300-400 line segments at paragraph boundaries
Essay/article	Section by section, or argument-by-argument
Non-fiction with chapters	One chunk per chapter
Poetry	Stanza groups or poem-by-poem in a collection
Short story	2-5 chunks based on narrative movement

Key constraints:

• Chunks must contain the complete original text — no abridging, no summarising
• Chunks must be in order
• Each chunk should be a meaningful unit of reading (not mid-sentence or mid-paragraph)
• Consider reading rhythm: cliffhanger endings might pair better with the next section

3. Determine if Structural Fallback is Needed

If the source file is too large to fit entirely in your context (roughly >200,000 characters), use the structural chunker as a fallback:

python3 {BASE_DIR}/scripts/chunk_manager.py structural-chunk {SESSION_ID} {SOURCE_FILE}

This will automatically split on chapter markers, scene breaks, and paragraph boundaries (targeting 300-400 lines per chunk) and save all chunks to the session.

Skip to step 5 if using the structural fallback.

4. Save Chunks (Semantic Approach)

For each chunk you identify:

1. Write the chunk text to a temp file:

   # Write chunk text to temp file (use the read tool to get the text, write tool to save it)
   

2. Save via chunk_manager:

   python3 {BASE_DIR}/scripts/chunk_manager.py save {SESSION_ID} {N} \
     --text-file /tmp/chunk_N.md \
     --meta '{"tone":"<tone>","intensity":"<low|medium|high>","themes":["<theme1>","<theme2>"],"adjacent_relationship":"<relationship to previous/next chunk>"}'
   

   Metadata fields:

   • tone: descriptive string (e.g., "melancholic", "argumentative", "playful", "tense")
   • intensity: low / medium / high — how emotionally or intellectually demanding
   • themes: array of 1-4 thematic tags
   • adjacent_relationship: how this chunk connects to neighbours (e.g., "resolution of previous chapter's cliffhanger", "new argument thread", "continuation of scene")

5. Update Session Status

python3 {BASE_DIR}/scripts/session_manager.py update {SESSION_ID} --set status=chunked

6. Initialise Reading State

python3 {BASE_DIR}/scripts/state_manager.py init {SESSION_ID}

7. Write Chunking Notes

Write a brief chunking_notes.md file to the session directory explaining:

• How many chunks you created and why
• What chunking approach you chose and the rationale
• Any notable structural features of the source (unusual formatting, embedded documents, etc.)

Save it to: {WORKSPACE}/memory/sequential_read/{SESSION_ID}/chunking_notes.md

Done

After completing all steps, the preread phase is complete. The reading phase can begin.