A New Open-Source Repository Lets You Compare System Prompts Across 45 AI Models

A freshly published GitHub repository, spotted barely an hour ago, aims to pull back the curtain on one of generative AI's least visible layers: the system prompt. Dubbed System-Prompt-Open, the project brings together over 45 system prompts from models and tools including Cursor, Lovable, Perplexity, v0, and Trae, offering a live gallery, a companion research paper, and an open dataset built specifically for prompt analysis, security research, and red-teaming.

The repository, authored under the handle pinchhitterequatorialcurrent101, carries a modest single star at the time of this writing. But its scope is ambitious: it touches on prompt engineering, prompt extraction, prompt fusion, multi-agent system behavior, and LLM security — all topics that sit at the center of how production AI applications are built and broken today.

What the Repository Actually Contains

Based on its published description and tagged topics, the project is structured around three main assets:

• A live gallery that lets visitors browse and compare system prompts across dozens of models side by side — no local setup required.
• An accompanying research paper that appears to frame the findings academically, though its methodology and peer-review status are not yet detailed in the repository metadata.
• An open dataset intended for reuse in downstream prompt analysis, extraction experiments, and security testing workflows.

The repository is written primarily in HTML, which suggests the live gallery itself may be a static web interface rather than a heavy application stack. Topic tags reveal the specific areas of focus: agents, multi-agent-systems, llm-security, prompt-engineering, prompt-extraction, prompt-fusion, and red-teaming, along with tool-specific tags for Cursor, Lovable, Perplexity, v0, Trae, and Cluely.

Why System Prompt Comparison Matters Right Now

System prompts are the hidden instructions that shape how an AI model behaves before the user ever types a word. They set tone, constrain outputs, define tool-use rules, and increasingly govern agentic workflows. Yet they are almost never disclosed by the companies shipping these products. That opacity creates real downstream problems:

• Security blind spots. Without knowing what guardrails exist, red teams and security researchers must reverse-engineer behavior from the outside, which is slow and error-prone.
• Vendor lock-in by obscurity. When a tool's system prompt contains proprietary orchestration logic, migrating to another model or platform becomes harder because the behavioral contract is undocumented.
• Inconsistent evaluation. If two models answer the same question differently, is it the base model, the system prompt, or the tool-use scaffolding? Without prompt transparency, attribution is guesswork.

A centralized, open comparison dataset — even an early-stage one — could help developers and researchers isolate these variables. That is the practical gap this repository is attempting to fill.

Who Should Pay Attention

This project is not a polished product launch. It is a research-aligned resource that will resonate most with a specific audience:

• AI founders and product builders who need to understand how competitors' models steer behavior under the hood, especially when building agentic or multi-model workflows.
• Prompt engineers and LLM ops practitioners who want a reference library of real-world system prompt patterns rather than synthetic examples from documentation.
• Security researchers and red teams looking for a structured dataset to test prompt injection, extraction, and jailbreak techniques against known guardrail patterns.
• Developer-tool makers evaluating how IDEs and coding assistants (Cursor, Trae, Lovable) frame their system-level instructions to shape code generation behavior.

For SEO-conscious marketers and content strategists, the repository also offers a rare look at how different AI tools position themselves through their hidden instructional layer — which can inform competitive content analysis.

Practical Use Cases for the Dataset

If the published dataset is well-structured, it could serve several concrete workflows:

• Diffing system prompts across model versions. Track how a tool like Cursor or Perplexity changes its behavioral instructions over time, which may correlate with feature shifts or safety incidents.
• Building prompt extraction detectors. Use the known prompts as ground truth to train or evaluate tools that detect when a model's system prompt has been successfully exfiltrated.
• Benchmarking agent behavior. When evaluating multi-agent frameworks, compare the system prompts used by different orchestrators to understand how they partition responsibility between agents.
• Informing your own system prompt design. For teams building on APIs like OpenAI API or deploying agents through the OpenAI Agents SDK, studying how production tools structure their instructions can surface patterns worth adopting or deliberately avoiding.

Limitations and Risks to Keep in Mind

The repository's potential is real, but several caution flags are visible from its current metadata:

• Brand-new and unvetted. With only one star and an appearance roughly an hour ago, there has been no time for community review, replication, or validation of the prompts' authenticity.
• Unknown extraction methodology. The repository's topic tags include prompt-extraction, which implies these system prompts were recovered rather than officially disclosed. The legality and reliability of extraction-based collections vary by jurisdiction and technique.
• No indication of maintenance commitment. Single-contributor repositories with academic framing sometimes go dormant after the associated paper is released. Long-term usefulness depends on updates as models evolve.
• Potential for stale or hallucinated prompts. Without confirmation from the original model providers, some prompts in the dataset may be incomplete, outdated, or incorrectly attributed.

Anyone using this dataset for production decisions should independently verify the prompts against their own testing before relying on them.

How to Evaluate AI Tools Through a Prompt-Awareness Lens

Regardless of whether this specific repository becomes a lasting resource, it highlights a capability that is increasingly important when evaluating AI tools for your stack: prompt transparency. Here are questions to ask about any AI product you are considering:

• Is the system prompt disclosed or documented? Some platforms, like custom GPTs built via GPTs by OpenAI, allow creators to share their instructions — but most commercial tools do not.
• Can you inspect or override the system prompt via API? Tools like the OpenAI API give developers full control over the system message field, which is essential for reproducibility and security auditing.
• What happens when the system prompt conflicts with user input? Testing this boundary reveals how rigid or flexible a tool's guardrails are, which has direct implications for both safety and usability.
• Does the provider have a history of prompt leaks or extraction incidents? A tool whose system prompt has been extracted and published may have reduced security value, since attackers can study its constraints offline.

For teams building on managed AI services, the growing availability of prompt comparison datasets makes it easier to audit what hidden instructions might be running inside the models you depend on — even when vendors do not volunteer that information.

FAQ

What is a system prompt?

A system prompt is a set of initial instructions given to a large language model that defines its behavior, tone, constraints, and tool-use rules. It operates behind the scenes and is typically invisible to end users.

Why compare system prompts across different models?

Comparing system prompts helps researchers understand how different AI products steer model behavior, identify security patterns and weaknesses, and build better prompts for their own applications.

Is the System-Prompt-Open dataset verified or official?

No. The prompts appear to have been extracted rather than officially released by the model providers. Independent verification is strongly recommended before using the data for any production purpose.

Which AI tools are covered in the comparison?

The repository tags reference Cursor, Lovable, Perplexity, v0, Trae, and Cluely, among others. The full list of 45+ models is expected to be available in the live gallery and dataset.

Could this kind of dataset raise legal or ethical concerns?

Yes. Prompt extraction may violate terms of service for some platforms, and publishing extracted prompts can be legally contested. Researchers and practitioners should evaluate the provenance and legality of any prompt dataset they use.