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Openpyxl Sandbox Workaround

OrganizationPopular
HKUDS
openpyxl-sandbox-workaround

Use run_shell with inline Python as a fallback when execute_code_sandbox fails for openpyxl/spreadsheet operations

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill nameopenpyxl-sandbox-workaround
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 Openpyxl Sandbox Workaround 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/openpyxl-sandbox-workaround
  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/openpyxl-sandbox-workaround .claude/skills/openpyxl-sandbox-workaround
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openpyxl Sandbox Workaround 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 Openpyxl Sandbox Workaround 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 Openpyxl Sandbox Workaround 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.

Openpyxl Sandbox Workaround

When to Use

Use this pattern when execute_code_sandbox repeatedly fails for openpyxl or spreadsheet manipulation tasks. The sandbox environment may have compatibility issues with certain openpyxl operations, but running Python directly via run_shell often succeeds.

Common Failure Indicators

  • execute_code_sandbox returns errors related to openpyxl imports or operations
  • Multiple retry attempts fail with similar errors
  • Errors mention workbook creation, cell writing, or file saving issues

The Workaround Pattern

Instead of using execute_code_sandbox, use run_shell with an inline Python script:

bash
python3 << 'EOF'
from openpyxl import Workbook
from openpyxl.utils import get_column_letter

# Your openpyxl code here
wb = Workbook()
ws = wb.active
ws.title = "Sheet1"

# Add data
ws['A1'] = 'Header'
ws['B1'] = 'Value'

# Save file
wb.save('output.xlsx')
print('File created successfully')
EOF

Multi-Step Spreadsheet Tasks

For complex operations involving multiple files or data processing:

bash
python3 << 'EOF'
from openpyxl import Workbook, load_workbook
import os

# Load existing workbook if needed
if os.path.exists('input.xlsx'):
    wb = load_workbook('input.xlsx')
    ws = wb.active
    # Process data...
    
# Create new workbook
wb = Workbook()
ws = wb.active

# Add data with proper formatting
for row_idx, row_data in enumerate(data, start=1):
    for col_idx, value in enumerate(row_data, start=1):
        ws.cell(row=row_idx, column=col_idx, value=value)

# Auto-adjust column widths
for column in ws.columns:
    max_length = 0
    column_letter = get_column_letter(column[0].column)
    for cell in column:
        try:
            if len(str(cell.value)) > max_length:
                max_length = len(str(cell.value))
        except:
            pass
    adjusted_width = min(max_length + 2, 50)
    ws.column_dimensions[column_letter].width = adjusted_width

wb.save('output.xlsx')
EOF

Best Practices

  1. Use heredoc syntax: The << 'EOF' pattern prevents variable expansion issues
  2. Print success messages: Always include print statements to confirm completion
  3. Handle file paths: Use absolute paths or ensure you're in the correct working directory
  4. Validate output: After creation, verify the file exists and has expected content
  5. Error handling: Add try/except blocks for robustness in complex scripts

Verification Step

After running the script, verify the file was created:

bash
ls -la *.xlsx

Or check file details:

bash
python3 << 'EOF'
from openpyxl import load_workbook
wb = load_workbook('output.xlsx')
print(f"Sheets: {wb.sheetnames}")
print(f"Active sheet: {wb.active.title}")
EOF

When to Fall Back to This Pattern

  • First execute_code_sandbox attempt fails with openpyxl errors
  • Error persists after 1-2 retries
  • Task is time-sensitive and needs a reliable solution
  • The operation is straightforward file creation/modification

Limitations

  • Output is limited to stdout/stderr (no direct artifact download from run_shell)
  • Files are created in the current working directory
  • More verbose than execute_code_sandbox for simple operations

Frequently asked questions

What does the Openpyxl Sandbox Workaround AI skill do?

Use run_shell with inline Python as a fallback when execute_code_sandbox fails for openpyxl/spreadsheet operations

Why use Openpyxl Sandbox Workaround on TypingMind?

Because you install it once and use it with any model. Openpyxl Sandbox Workaround 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 Openpyxl Sandbox Workaround in TypingMind?

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

Which AI models can use Openpyxl Sandbox Workaround?

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 Openpyxl Sandbox Workaround?

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

Is the Openpyxl Sandbox Workaround 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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