PDF Extract with Shell-First Tool Cascade

This skill provides an optimized workflow for extracting text content from PDF documents (local files or downloaded URLs) using a prioritized tool cascade that favors shell-based extraction before falling back to Python libraries.

Why Shell-First?

Analysis of execution patterns shows:

• read_file

  on PDFs sometimes returns binary/image data instead of text

• run_shell

  with

  pdftotext

  has higher success rate and fewer sandbox errors

• execute_code_sandbox

  can fail with "unknown error" in constrained environments
• Shell tools are more reliable for PDF text extraction when available

Entry Point: Determine Your Starting Point

Before beginning, identify your scenario:

Complete Workflow

Step 0: Download PDF (URL Only)

If your PDF is at a web URL, download it first using browser user-agent:

curl -L -A "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" -o target.pdf "URL_HERE"

Key flags:

• -L

  : Follow redirects

• -A

  : Set user-agent header to mimic a real browser

• -o

  : Specify output filename

If you already have the PDF locally, skip to Step 1.

Step 1: Try read_file (Primary Attempt)

First, attempt to extract text using the

read_file

read_file(filetype="pdf", file_path="target.pdf")

Evaluate the response:

read_file

Critical: If

read_file

read_file

Step 2: Use run_shell with pdftotext (Preferred Fallback)

When

read_file

run_shell

pdftotext

run_shell(command="pdftotext target.pdf output.txt")

Then read the extracted text:

read_file(filetype="txt", file_path="output.txt")

If pdftotext is not found, install it first:

run_shell(command="apt-get update && apt-get install -y poppler-utils")
# Or for macOS:
run_shell(command="brew install poppler")

Then retry:

run_shell(command="pdftotext target.pdf output.txt")

Verify extraction quality:

• Check that

  output.txt

  exists and has content
• Sample the text to ensure it's readable (not garbled)
• If extraction looks corrupted, proceed to Step 3

Step 3: Use execute_code_sandbox with PyMuPDF (Last Resort)

If

pdftotext

execute_code_sandbox

import fitz  # PyMuPDF

doc = fitz.open("target.pdf")
text = ""
for page in doc:
    text += page.get_text()
doc.close()

with open("output.txt", "w") as f:
    f.write(text)
print(f"Extracted {len(text)} characters from {len(doc)} pages")

Execute via:

execute_code_sandbox(code="<python code above>")

Then read the result:

read_file(filetype="txt", file_path="output.txt")

Note:

execute_code_sandbox

Step 4: Graceful Degradation to Domain Knowledge

If all extraction methods fail:

1. Document the specific failure mode for each tool attempted
2. Extract any partial content that was successfully retrieved
3. Supplement missing content from established domain knowledge
4. Clearly mark which portions are from source vs. generated from knowledge
5. Provide citations for any claimed requirements or specifications

Example degradation documentation:

EXTRACTION FAILURE REPORT:
- Source: [URL or file path]
- read_file: Returned binary/image data (no text extraction)
- run_shell/pdftotext: [Tool not available / produced garbled output / succeeded]
- execute_code_sandbox/PyMuPDF: [Failed with unknown error / succeeded]

NOTE: Content below combines partial extraction with established domain 
knowledge for [topic]. Verify against official sources when available.

Tool Selection Decision Tree

                    PDF to Extract
                          │
                          ▼
                  ┌───────────────┐
                  │  read_file    │
                  │  (primary)    │
                  └───────┬───────┘
                          │
            ┌─────────────┼─────────────┐
            │             │             │
     Returns text   Returns binary   Error/timeout
        (✓)         / image data         │
            │             │             │
            ▼             ▼             ▼
       SUCCESS    ┌───────────────┐
                  │ run_shell     │
                  │ pdftotext     │
                  └───────┬───────┘
                          │
                  ┌───────┼───────┐
                  │       │       │
             Succeeds  Not      Garbled
                (✓)   avail.    output
                  │       │       │
                  ▼       ▼       ▼
             SUCCESS ┌───────────────┐
                     │ execute_code  │
                     │ _sandbox      │
                     │ PyMuPDF       │
                     └───────┬───────┘
                             │
                     ┌───────┼───────┐
                     │       │       │
                Succeeds   Fails   Error
                   (✓)      │       │
                     │      ▼       │
                     ▼   Domain     │
                 SUCCESS  Knowledge │
                             │      │
                             └──────┘
                              FAILURE
                              DOCUMENTED

Complete Automated Script

#!/bin/bash
# pdf-extract-cascade.sh
# Implements the full tool cascade for PDF extraction

INPUT="$1"
OUTPUT_PDF="target.pdf"
OUTPUT_TXT="output.txt"

# Step 0: Handle URL vs local file
if [[ "$INPUT" =~ ^https?:// ]]; then
    echo "Downloading PDF from URL..."
    curl -L -A "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" -o "$OUTPUT_PDF" "$INPUT"
else
    if [ ! -f "$INPUT" ]; then
        echo "ERROR: Local file not found: $INPUT"
        exit 1
    fi
    OUTPUT_PDF="$INPUT"
fi

# Step 1: Verify file type
echo "Verifying file type..."
if ! file "$OUTPUT_PDF" | grep -q "PDF document"; then
    echo "WARNING: File is not a valid PDF"
    file "$OUTPUT_PDF"
fi

# Step 2: Try pdftotext (shell-first approach)
echo "Attempting pdftotext extraction..."
if command -v pdftotext &> /dev/null; then
    if pdftotext "$OUTPUT_PDF" "$OUTPUT_TXT" 2>/dev/null; then
        if [ -s "$OUTPUT_TXT" ]; then
            echo "SUCCESS: Extraction completed with pdftotext"
            wc -l "$OUTPUT_TXT"
            exit 0
        fi
    fi
fi

# Step 3: Fallback to PyMuPDF
echo "Falling back to PyMuPDF..."
python3 << 'PYTHON_SCRIPT'
import fitz
import sys

try:
    doc = fitz.open("target.pdf")
    text = ""
    for page in doc:
        text += page.get_text()
    doc.close()
    with open("output.txt", "w") as f:
        f.write(text)
    print(f"SUCCESS: Extracted {len(text)} characters from {len(doc)} pages")
    sys.exit(0)
except Exception as e:
    print(f"PyMuPDF failed: {e}")
    sys.exit(1)
PYTHON_SCRIPT

# Step 4: Handle complete failure
if [ $? -ne 0 ]; then
    echo "FAILURE: All extraction methods failed"
    echo "ACTION: Generate content from domain knowledge"
    echo "Document each tool's failure mode for future reference"
    exit 1
fi

Best Practices

1. Never retry read_file on binary response: If

   read_file

   returns image/binary data, immediately switch to

   run_shell

2. Prefer run_shell over execute_code_sandbox: Shell tools have higher reliability and fewer sandbox-related errors
3. Verify before trusting: Always check extracted text is readable, not just that the command succeeded
4. Document failures: Record which tools failed and how, to inform future extraction attempts
5. Preserve originals: Keep the source PDF for debugging and re-extraction if needed
6. Check tool availability early: Test for

   pdftotext

   before starting complex workflows

Common Failure Modes and Responses

apt-get install poppler-utils

page.get_text("text")

When to Use This Skill

• Extracting text from local PDF files where

  read_file

  may return binary data
• Processing PDFs in automated pipelines requiring high reliability
• Situations where

  execute_code_sandbox

  has shown instability
• Working with PDFs from sources that may deliver rendered images instead of parseable text
• Any workflow where shell tool availability can be assumed or easily installed

Migration Notes from pdf-download-extract-fallback

This skill (

pdf-extract-shell-first

1. Explicit tool sequencing: Clearly prioritizes

   read_file

   →

   run_shell

   →

   execute_code_sandbox

2. No retry on binary read_file: Instructs immediate fallback when binary data detected
3. Shell-first philosophy: Emphasizes

   pdftotext

   via

   run_shell

   as preferred over Python
4. Reduced download focus: Assumes PDF is available or downloads in pre-step; focuses on extraction cascade
5. Decision tree visualization: Provides clear flowchart for tool selection