Split-PDF: Download, Split, and Deep-Read Academic Papers

CRITICAL RULE: Never read a full PDF. Never. Only read the 4-page split files, and only 3 splits at a time (~12 pages). Reading a full PDF will either crash the session with an unrecoverable "prompt too long" error — destroying all context — or produce shallow, hallucinated output. There are no exceptions.

When This Skill Is Invoked

The user wants you to read, review, or summarize an academic paper. The input is either:

• A file path to a local PDF (e.g., ./articles/smith_2024.pdf)
• A search query or paper title (e.g., "Gentzkow Shapiro Sinkinson 2014 competition newspapers")

Important: You cannot search for a paper you don't know exists. The user MUST provide either a file path or a specific search query. If the user invokes this skill without specifying what paper to read, ask them. Do not guess.

Prefer Paperpile when possible. If the paper is in Paperpile, call paperpile get-pdf-text(citekey=KEY) directly — you get the full structured text without any splitting. Only fall through to this skill's page-split workflow when the PDF is NOT in Paperpile (preprints, referee materials, ad-hoc reading, third-party shared PDFs).

Step 1: Acquire the PDF

If a local file path is provided:

• Verify the file exists
• Use the PDF in place — do not move or copy it. The folder containing the PDF becomes the working directory for splits and extracts.
• Proceed to Step 2

If a search query or paper title is provided:

Determine the download directory:

• Inside a research project (has CLAUDE.md, data/, paper/, etc.): use ./articles/ in the project directory (create if needed).
• Outside a project (e.g., ad-hoc reading from Task Management root): use to-sort/downloads/ in the Task Management folder.

Then:

1. Use WebSearch to find the paper
2. If WebSearch doesn't yield a direct PDF link, try scholarly scholarly-search first. Fallback to Python OpenAlex client:

   import sys
   sys.path.insert(0, ".scripts/openalex")
   from openalex_client import OpenAlexClient
   client = OpenAlexClient(email="user@example.edu")
   results = client.search_works(search="paper title here", per_page=5)
   # Check open_access.oa_url in results for direct PDF links
   

3. Use WebFetch or Bash (curl/wget) to download the PDF
4. Save it to the download directory
5. Proceed to Step 2

CRITICAL: Always preserve the original PDF. The source PDF must NEVER be deleted, moved, or overwritten at any point in this workflow. The split files are derivatives; the original is the permanent artifact. Do not clean up, do not remove, do not tidy.

Step 2: Check for Cached Extract, then Split

First, check for an existing extract. Look for <basename>_text.md in the same folder as the PDF.

If found, ask:

"An extract from a previous deep-read exists (<basename>_text.md). Use it for this request, or re-read the PDF from scratch?"

• Use extract: read <basename>_text.md and use it as the source notes — skip the rest of Steps 2 and 3 entirely.
• Re-read: proceed with splitting below.

This prevents redundant re-reading of papers you have already processed. The _text.md file is a structured plain-text extraction far cheaper to read than re-processing PDF page images.

Second, check for existing splits. Compute the build directory:

import os
folder_path  = os.path.dirname(os.path.abspath(pdf_path))
foldername   = os.path.basename(folder_path)
pdf_basename = os.path.splitext(os.path.basename(pdf_path))[0]
build_dir    = os.path.join(folder_path, foldername + '_build')
split_dir    = os.path.join(build_dir, 'split_' + pdf_basename)

If split_dir already exists and contains .pdf files, ask:

"Splits already exist for <pdf-basename> (N chunks in <foldername>_build/split_<pdf-basename>/). Reuse existing splits, or re-split from scratch?"

• Reuse: skip splitting, proceed to Step 3 using the existing files in split_dir.
• Re-split: delete the existing split folder, then split.

Otherwise, split. Create <foldername>_build/split_<pdf-basename>/ and run:

from PyPDF2 import PdfReader, PdfWriter
import os

def split_pdf(input_path, output_dir, pages_per_chunk=4):
    os.makedirs(output_dir, exist_ok=True)
    reader = PdfReader(input_path)
    total = len(reader.pages)
    prefix = os.path.splitext(os.path.basename(input_path))[0]

    for start in range(0, total, pages_per_chunk):
        end = min(start + pages_per_chunk, total)
        writer = PdfWriter()
        for i in range(start, end):
            writer.add_page(reader.pages[i])

        out_name = f"{prefix}_pp{start+1}-{end}.pdf"
        out_path = os.path.join(output_dir, out_name)
        with open(out_path, "wb") as f:
            writer.write(f)

    print(f"Split {total} pages into {-(-total // pages_per_chunk)} chunks in {output_dir}")

If PyPDF2 is not installed: uv pip install PyPDF2.

Directory convention:

articles/                             # any folder containing a PDF
├── smith_2024.pdf                    # original — NEVER DELETE
├── smith_2024_text.md                # persistent extract — created after deep-read
└── articles_build/                   # <foldername>_build/ — shared build folder
    └── split_smith_2024/             # split_<pdf-basename>/
        ├── smith_2024_pp1-4.pdf
        ├── smith_2024_pp5-8.pdf
        ├── smith_2024_pp9-12.pdf
        ├── notes.md                  # working copy — source for _text.md
        └── ...

The build directory (<foldername>_build/) keeps split artifacts separate from source material and finished outputs. Multiple PDFs in the same folder share one build directory, each with its own split_<basename>/ subdirectory.

Step 3: Read in Batches of 3 Splits

Read exactly 3 split files at a time (~12 pages). After each batch:

1. Read the 3 split PDFs using the Read tool
2. Update the running notes file (notes.md in the split subdirectory)
3. Pause and tell the user:

"I have finished reading splits [X-Y] and updated the notes. I have [N] more splits remaining. Would you like me to continue with the next 3?"

4. Wait for the user to confirm before reading the next batch.

Do NOT read ahead. Do NOT read all splits at once. The pause-and-confirm protocol is mandatory.

Step 4: Structured Extraction (8 dimensions)

As you read, collect information along these dimensions and write them into notes.md:

1. Research question — What is the paper asking and why does it matter?
2. Audience — Which sub-community of researchers cares about this?
3. Method — How do they answer the question? What is the identification strategy?
4. Data — What data? Where from? Unit of observation? Sample size? Time period?
5. Statistical methods — What econometric/statistical techniques? Key specifications?
6. Findings — Main results? Key coefficient estimates and standard errors?
7. Contributions — What's learned that we didn't know before?
8. Replication feasibility — Public data? Replication archive? Data appendix? URLs?

These extract what a researcher needs to build on or replicate the work.

Step 5: Persist the Extract

After all batches are complete, write the final notes to <basename>_text.md in the same folder as the source PDF:

articles/smith_2024_text.md

Then notify the user:

"Extract saved to smith_2024_text.md alongside the source PDF. Future requests on this paper can reuse it without re-reading."

This file is the persistent, reusable artifact. The notes.md in the build directory is the working copy. Both are kept — never delete either.

Structured Mode (Paperpile)

If the paper IS in Paperpile (user provides a citekey), skip the page-split workflow entirely:

1. paperpile get-item(citekey=KEY) — title, authors, abstract, affiliations
2. paperpile get-pdf-text(citekey=KEY) — full text from Google Drive

Write the 8-dimension extraction directly to <basename>_text.md in the working directory. No splits needed.

This skill's page-split workflow is for PDFs NOT in Paperpile.

Agent Isolation Protocol

When split-pdf is invoked by another skill or workflow (any process that continues working after the PDF has been read), the PDF reading MUST run inside a subagent to prevent context bloat in the parent conversation.

Why: Each PDF page rendered by the Read tool produces image data in the conversation context. A 35-page PDF (9 chunks) can add 10-20MB of image data that accumulates permanently. After reading one or two large PDFs on top of prior work, the conversation hits the API request size limit and becomes unrecoverable.

Pattern: The parent skill handles splitting (Step 2's Python script) in its own context — this is lightweight. Then it launches an Agent to perform all the reading:

Read PDF split files and produce structured extraction notes.

Split directory: <split_dir>
Files (read in this order, 3 at a time): <file_list>
Notes output:    <notes_path>  (working copy in split_dir)
Text output:     <text_path>   (persistent <basename>_text.md)

Process:
1. Read 3 PDF files at a time using the Read tool
2. After each batch, update notes.md with extracted content
3. Extract along the 8 dimensions (research question, audience, method,
   data, statistical methods, findings, contributions, replication feasibility)
4. Write the final structured extraction to <text_path>

Report when done: pages read, figures/tables found, one-sentence content summary.

After the agent returns, the parent reads the output files (plain markdown, not PDF images) and continues its workflow.

Standalone invocations (user calls /split-pdf directly) use the interactive protocol above with reads in the main conversation and the pause-and-confirm protocol.

When NOT to Split

• Papers shorter than ~15 pages: read directly (still use the Read tool, not Bash)
• Policy briefs or non-technical documents: a rough summary is fine
• Triage only: read just the first split (pages 1-4) for abstract and introduction
• Paperpile items: use paperpile get-pdf-text directly

Quick Reference

Step	Action
Acquire	Use local PDF in place, or download to ./articles/ (in-project) / to-sort/downloads/ (ad-hoc)
Check cache	<basename>_text.md or existing splits — offer to reuse
Split	4-page chunks into <foldername>_build/split_<pdf-basename>/
Read	3 splits at a time, pause after each batch
Notes	Update notes.md with 8-dimension extraction
Persist	Save final extract to <basename>_text.md alongside source PDF
Confirm	Ask user before continuing to next batch

Acknowledgments

The in-place PDF handling, persistent _text.md extraction, split reuse, build directory convention, and agent isolation protocol are adapted from Scott Cunningham's MixtapeTools split-pdf skill (April 2026), which itself incorporated improvements from Ben Bentzin (McCombs School of Business, UT Austin). Structured Paperpile mode is the user's addition.