Very generic agents

[albanx]

I have tested all of these agents trying to create a simple service website. Unfortuantely they do not work as expected, for a simple reason, they are too broad, very generic. An agent needs to be very specialized to create good result, it is not enough saying "React component architecture (hooks, context, performance)", this says nothing to the LLM.

[RensTillmann]

That's how subagents are supposed to work, they do 1 very simple task, they do not have context of what the main agent is working on either. Or very little (only the actual prompt it receives from main agent whatever that might be). That's the main issue with these subagents. You might want to check out somthing like Claude Flow, that might be one solution, although I haven't even tested that out myself yet.

[wshobson]

Hi @albanx and @RensTillmann,

Thank you for taking the time to test the agents and share your feedback. While I appreciate the engagement, I'd like to encourage a more evidence-based approach to help improve the project.

Request for Specific Evidence

When reporting that agents are "too broad" or "very generic," it would be incredibly helpful to have:

1. Specific test cases: Which exact agents did you test? What prompts did you use? What was the expected vs actual output?

2. Quantifiable metrics:

   • Success/failure rate across multiple attempts
   • Time to completion comparisons
   • Quality measurements (did it compile? pass tests? meet requirements?)
   • Context token usage statistics

3. Reproducible examples:

   • Share the exact workflow you attempted
   • Include the agent configuration used
   • Provide input/output pairs that demonstrate the issue

4. Comparative analysis: What specific instructions or constraints would have improved the output? Can you A/B test your proposed improvements?

Scientific Approach

Rather than assertions like "they don't work" or "too generic," let's think scientifically:

• Hypothesis: "Agent X performs poorly on task Y"
• Method: Test with controlled inputs, measure outputs
• Data: Collect specific failures, patterns, success rates
• Analysis: Identify root causes with evidence

Contributing Improvements

This project actively welcomes pull requests! If you've identified specific improvements:

1. Fork the repository
2. Modify the agent definitions with your proposed specializations
3. Include test results showing the improvement
4. Submit a PR with before/after comparisons

For example, instead of saying "React component architecture descriptions are insufficient," you could:

• Show a specific component the current agent failed to create properly
• Propose exact additional constraints/instructions
• Demonstrate how your changes improve the output

Moving Forward

I genuinely want to improve these agents, but I need actionable, measurable feedback to do so. General statements without evidence don't provide a path forward. Please help by:

• Testing systematically
• Documenting failures precisely
• Measuring improvements quantitatively
• Contributing solutions directly

Looking forward to your specific findings and potential contributions!

Best regards