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Pdf Gen Fallback

OrganizationPopular
HKUDS
pdf-gen-fallback

Reliable PDF generation using shell-based Python execution when sandbox fails

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill namepdf-gen-fallback
Stars
7.8K
Forks
923
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Pdf Gen Fallback AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/pdf-gen-fallback
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HKUDS/OpenSpace.git /tmp/OpenSpace
mkdir -p .claude/skills
cp -r /tmp/OpenSpace/benchmarks/gdpval/skills/pdf-gen-fallback .claude/skills/pdf-gen-fallback
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pdf Gen Fallback in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pdf Gen Fallback on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pdf Gen Fallback is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PDF Generation Fallback

This skill provides a reliable workflow for generating PDFs and documents when execute_code_sandbox returns opaque errors. Use direct run_shell with python -c as a fallback, with library availability checks and explicit error handling.

When to Use

  • execute_code_sandbox fails with cryptic error messages during document/PDF generation
  • You need to generate PDFs, reports, or structured documents
  • Standard library execution is unreliable for document creation tasks

Step-by-Step Procedure

Step 1: Check Library Availability

Before attempting generation, verify that required libraries are installed:

bash
run_shell command="python -c \"import reportlab; print('reportlab available')\""
run_shell command="python -c \"import fpdf; print('fpdf available')\""
run_shell command="python -c \"import pypdf; print('pypdf available')\""

Step 2: Attempt execute_code_sandbox First

Try the sandbox approach initially (it's cleaner for iteration):

python
execute_code_sandbox code="""
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

c = canvas.Canvas("output.pdf", pagesize=letter)
c.drawString(100, 750, "Document Content")
c.save()
print("ARTIFACT_PATH:/path/to/output.pdf")
"""

Step 3: Detect Opaque Errors

Watch for these failure indicators:

  • Generic "execution failed" messages
  • Import errors that don't match library availability checks
  • Timeout errors during document rendering
  • Missing artifact paths in output

Step 4: Fall Back to run_shell

If sandbox fails, use direct shell execution:

bash
run_shell command="python -c \"
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas('output.pdf', pagesize=letter)
c.drawString(100, 750, 'Document Content')
c.save()
print('PDF created successfully')
\""

Step 5: Verify Output

After generation, confirm the file exists:

bash
run_shell command="ls -la output.pdf && file output.pdf"

Complete Example Workflow

1. Check libraries: run_shell "python -c \"import reportlab\""
2. Try sandbox: execute_code_sandbox with PDF generation code
3. If error detected: Fall back to run_shell with python -c
4. Verify: run_shell "ls -la *.pdf"
5. Read result: read_file filetype="pdf" file_path="output.pdf"

Error Handling Tips

  • Capture stderr from run_shell to diagnose issues
  • Use absolute paths when possible to avoid working directory issues
  • For complex documents, write the Python script to a file first, then execute
  • Consider installing missing libraries: run_shell command="pip install reportlab"

Library Alternatives

If reportlab is unavailable, try:

  • fpdf - Simpler API, good for basic PDFs
  • weasyprint - HTML/CSS to PDF conversion
  • pypdf - PDF manipulation and merging

Common Pitfalls

  • Don't assume sandbox errors mean the approach is wrong—often it's环境 issues
  • Always verify the PDF was created before proceeding
  • Handle encoding issues explicitly (use utf-8)
  • For images in PDFs, ensure image files are accessible to the execution context

Frequently asked questions

What does the Pdf Gen Fallback AI skill do?

Reliable PDF generation using shell-based Python execution when sandbox fails

Why use Pdf Gen Fallback on TypingMind?

Because you install it once and use it with any model. Pdf Gen Fallback is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pdf Gen Fallback in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/pdf-gen-fallback. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pdf Gen Fallback?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pdf Gen Fallback?

As many as you like. As long as a model supports skills, you can use Pdf Gen Fallback with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pdf Gen Fallback AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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