Autopilot - Ctrl: AI Content Auditing with GitHub Copilot CLI

Question

What I Built

autopilot-ctrl is a command-line tool that audits AI-generated social media content before publishing. Think of it as a "quality gate" for your content pipeline.

The Problem

My blog has an autopilot system that automatically generates posts for Twitter, LinkedIn, and Newsletter every time I publish an article. It works great... most of the time. But sometimes the AI produces:

• Generic tweets without hooks
• LinkedIn posts without proper structure
• Newsletter intros that reveal too much (or too little)

I needed a way to evaluate quality BEFORE publishing and, if something doesn't pass, improve it automatically.

The Solution

autopilot-ctrl uses GitHub Copilot CLI to:

1. Audit content against platform-specific criteria
2. Assign a quality score (0-10)
3. Identify specific issues
4. Generate improved versions of failing content

                                 Audit Results                                
┌─────────────┬─────────┬───────────┬─────────────────────────────────────────┐
│ Platform    │  Score  │  Status   │ Issues                                  │
├─────────────┼─────────┼───────────┼─────────────────────────────────────────┤
│ Twitter     │ 3.0/10  │ [XX] FAIL │ No hook, missing hashtags               │
│ Linkedin    │ 7.0/10  │ [OK] PASS │ -                                       │
│ Newsletter  │ 8.0/10  │ [OK] PASS │ -                                       │
└─────────────┴─────────┴───────────┴─────────────────────────────────────────┘

Demo

{{< youtube KNjx5IB8jr8 >}}

Available commands:

# Check that Copilot CLI is installed
python -m ctrl check

# Audit content
python -m ctrl audit content.json

# Fix failing content
python -m ctrl fix content.json --apply

Screenshots of the flow:

Source code: github.com/Dalaez/datalaria/autopilot/ctrl

My Experience with GitHub Copilot CLI

How I Used Copilot CLI

The magic of autopilot-ctrl lies in how it integrates Copilot CLI in non-interactive mode:

# auditor.py
result = subprocess.run(
    ['copilot', '-s', '--no-ask-user', '-p', prompt],
    capture_output=True,
    text=True,
    timeout=60,
    encoding='utf-8'
)

Each audit sends a structured prompt to Copilot CLI and parses the natural language response to extract:

• Numeric score (e.g., "Rating: 7/10")
• List of issues (e.g., "No engagement", "Generic hook")
• Improvement suggestions

What I Learned

1. Flag order matters: -p MUST be the last argument
2. Simple prompts work better: Long, structured prompts in non-interactive mode return empty responses
3. Copilot responds in natural language: I had to create flexible parsers to extract data from responses like "Rating: 7/10"

⚡ The Impact on My Workflow

Before autopilot-ctrl, I manually reviewed every generated post. Now:

1. git push → Autopilot generates content
2. python -m ctrl audit generated_content.json → Copilot evaluates
3. If something fails → python -m ctrl fix generates improvements
4. Approved content → Gets published automatically

Time saved: ~15 minutes per publication.

️ Tech Stack

• Python + Click: CLI framework
• Rich: Terminal UI with tables and colors
• GitHub Copilot CLI: AI evaluation engine
• YAML configs: Customizable prompts per platform

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Conclusion

autopilot-ctrl demonstrates that GitHub Copilot CLI isn't just for generating code. It's a powerful tool for integrating AI into any pipeline - in this case, content quality evaluation.

If you have a system that generates content automatically, consider adding a "quality gate" with Copilot CLI. Your audience (and your engagement metrics) will thank you.

Questions? Drop them in the comments

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This post is part of the Autopilot Project series, where I document how I automate content creation and publishing using AI.