Skip to content
cccskills
FreePublished on GitHub

python-spreadsheet-debug

Diagnose Python spreadsheet failures through staged checks of the interpreter, library imports, file access, and basic operations. Minimal test scripts help separate environment problems from errors in the full processing script.

OriginalShow the original description

Systematic Python debugging workflow for spreadsheet tasks to isolate environment issues from script logic

Works withExcel
See how to install

Good for

  • Investigating unclear errors returned by run_shell
  • Diagnosing spreadsheet scripts that suddenly stop working
  • Checking Python setup in a new environment or container
  • Isolating Excel reading and aggregation failures

Files included (2)

  • SKILL.md3.4 KB
  • .skill_id38 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Python Spreadsheet Debugging Workflow

When executing Python scripts for spreadsheet/data processing tasks, use this systematic debugging approach to efficiently isolate environment configuration issues from script logic errors, especially when run_shell returns opaque errors.

Step 1: Verify Python Environment

First, confirm the Python interpreter path and version to ensure you're working in the expected environment:

which python python3
python --version
python3 --version

This identifies whether Python is available and which version is being used.

Step 2: Test Library Imports in Isolation

Before running your full script, verify that required libraries can be imported successfully. Test each critical import individually:

python -c "import pandas; print('pandas:', pandas.__version__)"
python -c "import openpyxl; print('openpyxl:', openpyxl.__version__)"
python -c "import xlrd; print('xlrd:', xlrd.__version__)"

Replace library names based on your script's requirements. This identifies missing dependencies or version conflicts early.

Step 3: Run Minimal Test Script

Create and execute a minimal script that exercises only the core functionality without full business logic:

# test_minimal.py
import pandas as pd

# Test 1: Can we read a file?
try:
    df = pd.read_excel('sample.xlsx')
    print(f"✓ File read successful: {len(df)} rows")
except Exception as e:
    print(f"✗ File read failed: {e}")

# Test 2: Can we perform basic operations?
try:
    result = df.groupby('category')['amount'].sum()
    print(f"✓ GroupBy operation successful")
except Exception as e:
    print(f"✗ Operation failed: {e}")

Run this with: python test_minimal.py

Purpose: This isolates whether the issue is with file access, library functionality, or specific script logic.

Step 4: Execute Full Script

Once the minimal test passes, run the complete script:

python your_script.py

If errors occur now, you know the environment is correctly configured and can focus on debugging the specific logic.

Quick Diagnostic Command

For rapid diagnosis, run all checks in sequence:

echo "=== Python Version ===" && python --version && \
echo "=== Key Imports ===" && python -c "import pandas, openpyxl; print('OK')" && \
echo "=== Ready for full script ==="

Common Issues and Resolutions

SymptomLikely CauseResolution
ModuleNotFoundErrorMissing packagepip install <package-name>
ImportError with versionVersion conflictCheck pip list, reinstall specific version
FileNotFoundErrorWrong path/contextVerify working directory with pwd
PermissionErrorFile accessCheck file permissions or path

When to Use This Pattern

  • run_shell returns cryptic or truncated error messages
  • Script worked previously but suddenly fails
  • Deploying to a new environment or container
  • Debugging spreadsheet processing with pandas, openpyxl, xlrd, etc.

Benefits

  • Fast isolation: Identifies environment vs. logic issues within seconds
  • Incremental confidence: Each passing step narrows the problem space
  • Reusable pattern: Apply consistently across all Python spreadsheet tasks

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.

  • Match audio parts with different durations
  • Extend drums and melodic parts using suitable methods
  • Check audio specifications before mixing
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Produce audio in separate stems, meaning individual instrument tracks, and verify each output as you go. Align track lengths with silence, loops, or crossfades, then apply effects, mix a master, and package the files.

  • Align instrument tracks to a reference audio file
  • Choose how to extend tracks that are too short
  • Verify audio properties after generation and effects
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Build serverless endpoints that relay external API requests for a dashboard while keeping API keys on the server. Includes Vercel examples for input checks, caching, cross-origin access, and consistent JSON responses.

  • Connect a dashboard to external APIs without exposing API keys
  • Create endpoints for stock quotes and news
  • Standardize API responses and error handling
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Produce a master track and separate audio parts through a structured Python workflow. Apply filters and reverb, export WAV files with consistent settings, package them in a ZIP archive, and check that the deliverables meet the requested specifications.

  • Export a master track with separate audio parts
  • Apply filters and reverb to audio
  • Check duration and format across deliverables
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.

  • Continue drafting after repeated web retrieval failures
  • Preserve task requirements during network errors
  • Mark information that could not be externally verified
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.

  • Retry Python work through the shell after repeated sandbox failures
  • Run longer tasks with an adjusted timeout
  • Execute Python work that needs multiple files or system utilities
HKUDS/OpenSpace7.8KUpdated Aug 13, 2026

See all skills by HKUDS

Report a problem with this skill