semvec/pypi-packages huggingface dataset:
gha-debug 0.2.0
Debug GitHub Actions workflows locally with step-by-step execution
License: MIT
Requires Python: >=3.8
Keywords: github-actions, developer-tools, cli, ci-cd, workflow-debugging, devops

# gha-debug

Debug GitHub Actions workflows locally with step-by-step execution

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Overview

`gha-debug` is a lightweight CLI tool that helps developers test and debug GitHub Actions workflows locally before pushing to CI. Unlike heavy Docker-based solutions, it provides fast validation, clear step-by-step execution visibility, and helpful error messages.

## Installation

```bash
pip install gha-debug
```

## Usage

### Run a workflow locally

```bash
gha-debug run .github/workflows/test.yml
```

### Run a specific job with verbose output

```bash
gha-debug run .github/workflows/build.yml --job build --verbose
```

### List all workflows and jobs

```bash
gha-debug list
```

### Show environment variables for a job

```bash
gha-debug env .github/workflows/deploy.yml --job deploy
```

### Validate workflow syntax

```bash
gha-debug validate .github/workflows/*.yml
```

## Features

- ✅ Parse and validate GitHub Actions workflow YAML files
- ✅ List all workflows, jobs, and steps with clear formatting
- ✅ Run workflows locally with simulated GitHub Actions environment
- ✅ Show detailed step-by-step execution with timing information
- ✅ Display environment variables and contexts for debugging
- ✅ Validate workflow syntax and catch common errors before pushing
- ✅ Colorized output for better readability
- ✅ Support for filtering by specific job within a workflow

## Why gha-debug?

Debugging GitHub Actions workflows is painful:
- Logs are hard to navigate in the web interface
- Re-running failed jobs wastes time
- No simple local testing that mirrors the CI environment

`gha-debug` solves this by providing a fast feedback loop. It doesn't try to perfectly replicate GitHub's environment (which causes compatibility issues), but instead focuses on what developers need most: quick validation and clear error messages.

## Example Output

```
$ gha-debug run .github/workflows/test.yml

Running workflow: test.yml
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Job: test
  ✓ Checkout code (1.2s)
  ✓ Setup Python 3.11 (0.8s)
  ✓ Install dependencies (4.5s)
  ✓ Run tests (12.3s)

✓ Workflow completed successfully in 18.8s
```

## License

MIT License - Copyright (c) 2026 Intellirim
csv-surgeon 1.1.0
Intelligent CSV repair and sanitization for broken data files
License: MIT
Requires Python: >=3.8
Keywords: csv, data-cleaning, cli-tool, data-engineering, etl, data-quality

# CSV Surgeon

Intelligent CSV repair and sanitization for broken data files

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Overview

CSV Surgeon is a CLI tool that automatically detects and repairs broken CSV files with encoding issues, delimiter mismatches, malformed quotes, embedded linebreaks, and structural inconsistencies. Unlike web-based tools or manual approaches, it uses statistical analysis and heuristic algorithms to infer correct file structure, normalize encodings, and reconstruct malformed records while preserving data integrity.

## Installation

```bash
pip install csv-surgeon
```

## Usage

```bash
# Repair broken CSV
csv-surgeon repair broken.csv

# Save to file with encoding
csv-surgeon repair data.csv -o cleaned.csv --encoding utf-8

# Analyze without repair
csv-surgeon analyze messy.csv

# Redact PII
csv-surgeon repair input.csv --delimiter , --sanitize-pii
```

## Features

- **Multi-pass encoding detection**: Uses chardet with fallback chain (UTF-8, UTF-16, Latin-1, CP1252) and confidence scoring
- **Statistical delimiter inference**: Tests 4 delimiter types (comma, semicolon, tab, pipe) using coefficient of variation
- **Context-aware quote repair**: Detects and repairs unclosed quotes using delimiter-aware heuristics
- **Embedded linebreak reconstruction**: Merges records split across multiple lines using column count consistency
- **Header auto-detection**: Distinguishes headers from data using type consistency analysis
- **PII sanitization**: Detects and redacts emails, phone numbers (3 formats), and SSNs using regex patterns
- **Detailed reporting**: Provides confidence scores (0.0-1.0) for encoding and delimiter detection
- **Error handling**: Gracefully handles permission errors, empty files, and binary files

## How It Works

Uses chardet for encoding detection, statistical analysis for delimiter inference (coefficient of variation), state machine for quote repair, and column count mode for record reconstruction.

## Why CSV Surgeon?

Unlike RepairMyCSV (web-only), csvkit (complex), OpenRefine (GUI), or Excel (manual), CSV Surgeon combines automatic detection, CLI automation, and intelligent algorithms in one simple tool.

## License

MIT License - Copyright (c) 2026 Intellirim