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Fallback Python Shell

skill-hkuds-openspace-fallback-python-shell · by HKUDS

Use run_shell with Python heredoc when execute_code_sandbox or read_file fail

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Install

$ agentstack add skill-hkuds-openspace-fallback-python-shell

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Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Shell-Based Python Fallback

When to Use

Use this fallback pattern when:

  • execute_code_sandbox returns 'unknown error'
  • read_file returns 'unknown error' for supported formats
  • You need to process documents, analyze data, or read files programmatically

Core Technique

Run Python code through run_shell using a heredoc. This bypasses sandbox execution issues while maintaining Python's full capabilities for file I/O and data processing.

Basic Pattern

python3 << 'EOF'
# Your Python code here
import json
import os

# Example: Read and process a file
with open('/path/to/file.txt', 'r') as f:
    content = f.read()
    print(content)
EOF

Key Syntax Points

  1. Use python3 << 'EOF' (quoted EOF prevents variable expansion in the heredoc)
  2. Indent Python code starting from column 0 (no extra indentation from shell)
  3. End with EOF on its own line with no leading/trailing whitespace
  4. Print results to stdout to capture them in the tool response

Common Use Cases

Reading Files (Fallback for read_file)

python3 << 'EOF'
import json

# Read text file
with open('document.txt', 'r') as f:
    content = f.read()
    print(content)

# Read JSON file
with open('data.json', 'r') as f:
    data = json.load(f)
    print(json.dumps(data, indent=2))
EOF

Processing Excel/CSV Files

python3 << 'EOF'
import pandas as pd

# Read Excel file
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')
print(df.to_string())
print(f"Shape: {df.shape}")

# Read CSV
df = pd.read_csv('data.csv')
print(df.head(10))
EOF

Reading PDF Files

python3 << 'EOF'
import fitz  # PyMuPDF

doc = fitz.open('document.pdf')
for page_num in range(len(doc)):
    page = doc[page_num]
    text = page.get_text()
    print(f"=== Page {page_num + 1} ===")
    print(text)
doc.close()
EOF

Reading Word Documents

python3 << 'EOF'
from docx import Document

doc = Document('document.docx')
for para in doc.paragraphs:
    print(para.text)
EOF

Data Analysis

python3 << 'EOF'
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')

# Basic statistics
print(f"Shape: {df.shape}")
print(f"Columns: {list(df.columns)}")
print(df.describe())

# Filter and aggregate
result = df.groupby('category').agg({'value': 'sum'})
print(result)
EOF

Best Practices

  1. Error Handling: Wrap file operations in try/except blocks

``bash python3 << 'EOF' try: with open('file.txt', 'r') as f: content = f.read() print(content) except FileNotFoundError: print("ERROR: File not found") except Exception as e: print(f"ERROR: {e}") EOF ``

  1. Large Output: For large files, process in chunks or print summaries

``bash python3 << 'EOF' with open('large_file.csv', 'r') as f: for i, line in enumerate(f): if i < 10: print(line.strip()) else: print("... truncated ...") break EOF ``

  1. Working Directory: Remember run_shell executes in the current working directory. Use absolute paths or ensure you're in the right directory.
  1. Multiple Steps: Chain related operations in a single heredoc rather than multiple calls

```bash python3 << 'EOF' # Do all related work in one call with open('input.json') as f: data = json.load(f)

processed = [transform(x) for x in data]

with open('output.json', 'w') as f: json.dump(processed, f)

print("Processing complete") EOF ```

Troubleshooting

  • Module not found: Some packages may not be available. Stick to standard library or commonly pre-installed packages (pandas, numpy are often available).
  • Permission errors: Ensure files aren't in protected directories.
  • Character encoding: Specify encoding explicitly: open('file.txt', 'r', encoding='utf-8')
  • Very long code: Split into multiple heredocs or write to a temporary .py file first.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.