Agent skill

PDF Extract Progressive Tools

by HKUDS in HKUDS/OpenSpace

Progressive tool-chain PDF extraction with explicit readfile, runshell, and executecodesandbox sequencing

MITAuto-check passedDocuments & Office

Install PDF Extract Progressive Tools

skills CLI
$ npx skills add HKUDS/OpenSpace --skill pdf-extract-progressive-tools -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HKUDS/OpenSpace pdf-extract-progressive-tools --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HKUDS/OpenSpace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/benchmarks/gdpval/skills/pdf-download-extract-fallback-enhanced-899f5b .claude/skills/pdf-extract-progressive-tools && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pdf-extract-progressive-tools
GitHub stars
7.8k
Token cost
~2.9k tokens
SKILL.md length
748 words
Files
2
Skills in repo
199
Repo updated
First seen
Licence
MIT

At a glance

Progressive tool-chain PDF extraction with explicit readfile, runshell, and executecodesandbox sequencing

  • Works in 5 steps: Attempt read_file First → Escalate to run_shell with pdftotext → Final Fallback to execute_code_sandbox… → …
  • Tasks that involve PDF
  • SKILL.md covers Critical Insight from…, Entry Point: Determine Your…, Overview and Step-by-Step Instructions, plus 6 more sections
  • Calls apt-get and pdftotext

What it does

PDF Extract Progressive Tools is an agent skill from HKUDS/OpenSpace. Progressive tool-chain PDF extraction with explicit readfile, runshell, and executecodesandbox sequencing

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file.

It sits in Documents & Office, covering PDF. The repository describes itself as: "OpenSpace: The Skill Management Layer for AI Agents" -- https://open-space.cloud/. The licence is MIT.

When your agent uses it

  • Tasks that involve PDF

Example prompts

  • “/pdf-extract-progressive-tools”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Attempt read_file First
  2. Escalate to run_shell with pdftotext
  3. Final Fallback to execute_code_sandbox with PyMuPDF
  4. Quality Verification
  5. Graceful Degradation to Domain Knowledge

What it can do on your machine

Read from SKILL.md and the folder at commit 3827781. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • apt-get
    • pdftotext

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

PDF Extract Progressive Tools loads about 2.9k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 748 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~35
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUDS/OpenSpace at commit 3827781, republished under its MIT licence (© HKUDS). 748 words, ~2,897 tokens.

Download SKILL.mdSave it as .claude/skills/pdf-extract-progressive-tools/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
pdf-extract-progressive-tools
description
Progressive tool-chain PDF extraction with explicit read_file, run_shell, and execute_code_sandbox sequencing

PDF Text Extraction with Progressive Tool Fallback

This skill provides a robust workflow for extracting text from PDF documents using a sequenced approach with agent tools, with explicit fallback mechanisms based on observed tool behavior.

Critical Insight from Execution Data

read_file often returns binary/image data for PDFs, not extracted text. When this occurs, immediately escalate to run_shell with pdftotext before attempting Python-based extraction.

Entry Point: Determine Your Starting Point

Before beginning, identify your scenario:

ScenarioStart HereSkip
PDF already on local diskStep 1 (read_file attempt)Download steps
PDF at a web URLDownload first, then Step 1None
PDF content already extractedStep 4 (Quality verification)Steps 1-3

Overview

PDF extraction failures cascade when tool sequencing is unclear. This workflow ensures maximum success rate through explicit tool progression:

  1. read_file - Quick attempt, but may return binary data
  2. run_shell + pdftotext - Reliable extraction when read_file fails
  3. execute_code_sandbox + PyMuPDF - Final fallback for complex PDFs

Step-by-Step Instructions

Step 1: Attempt read_file First

Always try the simplest approach first:

Tool: read_file
Path: document.pdf

Expected outcome: Extracted text content

Critical check: Examine the returned content:

  • ✅ Text visible: Proceed to Step 4 (Quality verification)
  • ⚠️ Binary/image data detected: Immediately proceed to Step 2
  • ❌ File not found: Verify path or download first

Binary data indicators:

  • Content starts with %PDF- header without text extraction
  • Content appears as garbled characters or base64
  • Content contains PNG/JPEG markers within PDF wrapper
  • File size seems reasonable but no readable text
Step 2: Escalate to run_shell with pdftotext

When read_file returns binary data, do NOT attempt execute_code_sandbox yet. Use run_shell immediately:

Tool: run_shell
Command: pdftotext document.pdf document.txt

If pdftotext is not available:

Tool: run_shell
Command: apt-get update && apt-get install -y poppler-utils && pdftotext document.pdf document.txt

Then read the extracted text:

Tool: read_file
Path: document.txt

Expected outcome: Clean text extraction

If this fails:

  • Check if file is password-protected
  • Check if file is corrupted (run file document.pdf)
  • Proceed to Step 3
Step 3: Final Fallback to execute_code_sandbox with PyMuPDF

Only attempt this if Steps 1-2 fail:

Tool: execute_code_sandbox
Language: python
Code: |
  import fitz  # PyMuPDF
  
  try:
      doc = fitz.open("document.pdf")
      text = ""
      for page in doc:
          text += page.get_text()
      doc.close()
      
      with open("document_pymupdf.txt", "w") as f:
          f.write(text)
      
      print("SUCCESS: Extracted {} characters".format(len(text)))
  except Exception as e:
      print(f"FAILED: {e}")

Then read the result:

Tool: read_file
Path: document_pymupdf.txt
Step 4: Quality Verification

Regardless of which method succeeded, verify extraction quality:

  1. Check text length: Should be proportional to PDF pages (~500-2000 chars per page)
  2. Check readability: Text should form coherent sentences
  3. Check for truncation: Look for cut-off words or missing sections
  4. Compare methods: If multiple methods worked, compare outputs

If quality is poor:

  • Try alternative extraction tools (pdfplumber, camelot-py for tables)
  • Consider OCR for scanned documents
  • Document limitations clearly
Step 5: 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 note:

NOTE: Source document [path/URL] was inaccessible due to [specific tool failures].
Content below combines partial extraction with established domain knowledge 
for [topic]. All claims verified against [alternative sources] where possible.

Tool Failure Log:
- read_file: Returned binary data (no text extraction)
- run_shell/pdftotext: Command not available in environment
- execute_code_sandbox/PyMuPDF: Sandbox execution failed with [error]
Show full SKILL.md (301 more words)Show less

Complete Tool Orchestration Script

python
# pdf-extract-orchestrator.py
# Implements the progressive tool fallback pattern

def extract_pdf_text(pdf_path):
    """
    Progressive PDF extraction following tool precedence:
    1. read_file (quick check)
    2. run_shell + pdftotext (primary extraction)
    3. execute_code_sandbox + PyMuPDF (final fallback)
    """
    extraction_log = []
    
    # Step 1: Try read_file
    print("Step 1: Attempting read_file...")
    try:
        content = read_file(pdf_path)
        if is_binary_or_image_data(content):
            extraction_log.append("read_file: Returned binary data")
            # Proceed to Step 2
        else:
            extraction_log.append("read_file: Success")
            return content, extraction_log
    except Exception as e:
        extraction_log.append(f"read_file: Failed - {e}")
    
    # Step 2: Try run_shell with pdftotext
    print("Step 2: Attempting run_shell + pdftotext...")
    try:
        run_shell(f"pdftotext {pdf_path} output.txt")
        content = read_file("output.txt")
        if content and len(content) > 100:
            extraction_log.append("run_shell/pdftotext: Success")
            return content, extraction_log
        else:
            extraction_log.append("run_shell/pdftotext: Empty extraction")
    except Exception as e:
        extraction_log.append(f"run_shell/pdftotext: Failed - {e}")
    
    # Step 3: Try execute_code_sandbox with PyMuPDF
    print("Step 3: Attempting execute_code_sandbox + PyMuPDF...")
    try:
        code = """
import fitz
doc = fitz.open("""" + pdf_path + """")
text = ""
for page in doc:
    text += page.get_text()
doc.close()
print(text[:1000])  # Preview
"""
        result = execute_code_sandbox(language="python", code=code)
        extraction_log.append("execute_code_sandbox/PyMuPDF: Success")
        return result, extraction_log
    except Exception as e:
        extraction_log.append(f"execute_code_sandbox/PyMuPDF: Failed - {e}")
    
    # Step 4: All methods failed
    extraction_log.append("ALL METHODS FAILED - Escalate to domain knowledge")
    return None, extraction_log

def is_binary_or_image_data(content):
    """Detect if content is binary/image data rather than extracted text"""
    if not content:
        return True
    # Check for PDF header without text extraction
    if content.startswith("%PDF-"):
        return True
    # Check for high ratio of non-printable characters
    non_printable = sum(1 for c in content if ord(c) < 32 and c not in '\n\r\t')
    if len(content) > 0 and non_printable / len(content) > 0.1:
        return True
    return False

Tool Precedence Decision Tree

                    ┌─────────────────┐
                    │  Start: PDF     │
                    │  Available?     │
                    └────────┬────────┘
                             │
                    ┌────────▼────────┐
                    │   Step 1:       │
                    │   read_file     │
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              │              │              │
        ┌─────▼─────┐  ┌─────▼─────┐  ┌─────▼─────┐
        │  Text     │  │  Binary   │  │  Error/   │
        │  Returned │  │  Data     │  │  Not Found│
        └─────┬─────┘  └─────┬─────┘  └─────┬─────┘
              │              │              │
              │         ┌────▼─────┐  ┌────▼─────┐
              │         │ Step 2:  │  │ Download │
              │         │ run_shell│  │ or Fix   │
              │         │ pdftotext│  │ Path     │
              │         └────┬─────┘  └──────────┘
              │              │
              │         ┌────▼─────┐
              │         │ Success? │
              │         └────┬─────┘
              │              │
        ┌─────▼─────┐  ┌─────▼─────┐
        │  Yes      │  │  No       │
        └─────┬─────┘  └─────┬─────┘
              │              │
              │         ┌────▼─────────┐
              │         │ Step 3:      │
              │         │ execute_     │
              │         │ code_sandbox │
              │         │ PyMuPDF      │
              │         └──────────────┘
              │
        ┌─────▼──────────────────┐
        │  Step 4: Quality Check │
        │  Step 5: Document      │
        │  Limitations           │
        └────────────────────────┘

Best Practices

  1. Check read_file output immediately: Don't assume it extracted text - verify before proceeding
  2. Escalate quickly on binary data: Don't waste iterations trying read_file multiple times
  3. Prefer run_shell over execute_code_sandbox: Shell tools are more reliable for PDF extraction when available
  4. Log each tool attempt: Document which method succeeded for future reference
  5. Preserve extraction artifacts: Keep intermediate files for debugging
  6. Verify extraction quality: Check text length and readability before accepting results
  7. Document tool failures: When falling back to domain knowledge, specify which tools failed and why

Common Failure Modes by Tool

ToolSymptomCauseSolution
read_fileBinary PDF dataTool doesn't extract PDF textEscalate to run_shell immediately
read_filePNG/JPEG dataPDF contains embedded imagesUse OCR tools or request text version
run_shellpdftotext not foundTool not installedInstall poppler-utils first
run_shellEmpty outputPassword-protected PDFRequest accessible version
execute_code_sandboxUnknown errorSandbox execution issueTry run_shell alternative or document limitation
execute_code_sandboxImport errorPyMuPDF not installedInclude pip install in script

When to Use This Skill

  • PDFs from web downloads: After downloading, apply this extraction workflow
  • PDFs already local: Start at Step 1 with existing file path
  • Automated document processing: Where reliability matters more than speed
  • Regulatory/compliance documents: Where source verification is critical
  • Situations with tool uncertainty: When environment capabilities are unknown

Migration from Parent Skill

This skill enhances pdf-download-extract-fallback by:

  1. Explicit tool sequencing: Parent described shell commands; this specifies agent tool order
  2. Binary detection: Parent assumed download success; this checks read_file output quality
  3. Faster escalation: Parent tried pdftotext then PyMuPDF; this escalates immediately on binary data
  4. Agent-focused: Parent was shell-script focused; this is optimized for agent tool calls
  5. Execution insights: Incorporates learnings from failed task 0353ee0c showing read_file limitations

© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in benchmarks/gdpval/skills/pdf-download-extract-fallback-enhanced-899f5b of HKUDS/OpenSpace.

  • SKILL.md
  • .skill_id

Open the folder on GitHubat commit 3827781

Compare with similar skills

PDF Extract Progressive Tools next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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SkillStarsUsed inTokensAuto-checkLicenceRepo updated
PDF Extract Progressive Tools this skillHKUDS/OpenSpace7.8k—~2.9kAutomated safety check: PassMIT
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GenOffice Document CLIgenspark-ai/genoffice9k—~19kAutomated safety check: PassApache-2.0
Harness Book Best Practicewquguru/harness-books3.2k—~4.1kAutomated safety check: PassNone
Bookforge Korean Ebook PDF Makergongnyang/bookforge3151 repos~1.7kAutomated safety check: PassMIT

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Questions about PDF Extract Progressive Tools

What does PDF Extract Progressive Tools do?

Progressive tool-chain PDF extraction with explicit readfile, runshell, and executecodesandbox sequencing. PDF Extract Progressive Tools is an agent skill from HKUDS/OpenSpace.

When should I use PDF Extract Progressive Tools?

PDF Extract Progressive Tools fits situations like: tasks that involve PDF.

How do I install PDF Extract Progressive Tools in Claude Code?

Run `npx skills add HKUDS/OpenSpace --skill pdf-extract-progressive-tools -a claude-code`. Or copy the skill folder (benchmarks/gdpval/skills/pdf-download-extract-fallback-enhanced-899f5b in HKUDS/OpenSpace) into .claude/skills/pdf-extract-progressive-tools in your project. Claude Code loads it when a task matches its description.

How do I install PDF Extract Progressive Tools in Codex?

Run `npx skills add HKUDS/OpenSpace --skill pdf-extract-progressive-tools -a codex`. Or copy the skill folder (benchmarks/gdpval/skills/pdf-download-extract-fallback-enhanced-899f5b in HKUDS/OpenSpace) into .agents/skills/pdf-extract-progressive-tools in your project. Codex loads it when a task matches its description.

Can I use PDF Extract Progressive Tools in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/OpenSpace --skill pdf-extract-progressive-tools -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pdf-extract-progressive-tools, .gemini/skills/pdf-extract-progressive-tools, .github/skills/pdf-extract-progressive-tools and .opencode/skills/pdf-extract-progressive-tools in your project.

What does PDF Extract Progressive Tools need to run?

Going by SKILL.md and its folder, PDF Extract Progressive Tools needs the command-line tools its instructions call (apt-get and pdftotext). Our summary lists: Python 3.

Does PDF Extract Progressive Tools access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is PDF Extract Progressive Tools safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does PDF Extract Progressive Tools use?

PDF Extract Progressive Tools is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does PDF Extract Progressive Tools use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to PDF Extract Progressive Tools?

Skills that share tags, products or a category with PDF Extract Progressive Tools: Markitdown (ImCa0/just-laws, 782 stars), Gzh Design (isjiamu/gzh-design-skill, 3.9k stars), GenOffice Document CLI (genspark-ai/genoffice, 9k stars) and Harness Book Best Practice (wquguru/harness-books, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains PDF Extract Progressive Tools?

HKUDS (a GitHub organization) maintains it in HKUDS/OpenSpace, which has 7,750 GitHub stars. The repository holds 199 skills in this directory. The repository was last updated on August 12, 2026.

Source: HKUDS/OpenSpace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.