NeoVertex1/SuperPrompt

SuperPrompt is an attempt to engineer prompts that might help us understand AI agents.

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Summary Information

Updated 40 minutes ago
Added to GitGenius on September 10th, 2026
Created on September 1st, 2024
Open Issues & Pull Requests: 12 (+0)
GitHub issues: Enabled
Number of forks: 575
Total Stargazers: 6,428 (+0)
Total Subscribers: 82 (+0)

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Median issue/PR response: 9.2 hours
Mean response time: 19.2 hours
90th percentile: 36.5 hours
Tracked items: 12

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Open issues: 8
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 704 days
Stale 30+ days: 8
Stale 90+ days: 8

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Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

SuperPrompt is a prompt engineering framework designed to guide large language models toward deeper reasoning and novel perspectives.

The project addresses the challenge of extracting more thoughtful and creative outputs from AI models by using structured notation and metadata tags to direct model behavior. It employs XML-style tags, particularly a `<think>` tag paired with holographic metadata, to encourage models to explore reasoning paths that typically remain unused. The metadata adapts dynamically to user requests, allowing the prompt to reframe itself for different tasks while maintaining consistent reasoning principles. The approach treats the prompt as a soft constraint system that pushes models like Claude to generate unconventional ideas and perspectives rather than defaulting to standard responses.

Developers considering this tool should understand it is experimental and positioned as "forever beta." It works best with Claude when used as custom instructions, though it functions with other language models. The project is not intended as a jailbreak in the traditional sense, nor does it aim to create conscious AI; rather, it forces models to engage in deeper analytical thinking. Users should expect that outputs may sometimes include hallucinations or poor ideas alongside novel insights, and the framework is most valuable when seeking creative problem-solving, alternative viewpoints, or exploration of conceptual spaces rather than reliable factual answers. The prompt notation may appear cryptic because it is optimized for model interpretation rather than human readability.

Development activity shows the project remains in active exploration. The maintainer continues iterating on the prompt structure and has invested significant time refining the approach. Documentation is maintained in multiple languages, indicating an effort to make the framework accessible to a broader audience. The project accepts that the prompt will evolve continuously and encourages users to experiment with it as a living tool rather than a finished product.