System Prompt Repositories: Which Examples Can You Reuse?
Public repositories collect thousands of system prompts from leaked sources, community contributions, and vendor documentation. Here is how to assess provenance, check licensing, and choose examples worth adapting.
Updated 6 min read
A system prompt repository is a collection of AI instructions gathered in one place, usually on GitHub or a similar platform. They range from community-sourced collections of ChatGPT prompts to archives of leaked product system prompts from major AI vendors. Some are high quality and well-documented; many are poorly sourced copies of copies. Tools like fenbs help you keep the prompts you actually tested and approved, alongside links to where they came from and notes on when they last worked.
The main questions before using a prompt from a public repository are: where did it come from, is it still accurate, and are you allowed to use it? This guide covers how to answer each one.
Why repositories exist
Prompt libraries started as community collections of useful ChatGPT instructions shared on forums and GitHub. They grew into three distinct types. First: community-contributed libraries where users share prompts for specific tasks. Second: leaked or reconstructed product system prompts from major AI tools, including the base instructions that products like ChatGPT, Claude, and Gemini use internally. Third: official vendor examples published in documentation to show best practices.
Each type has different reliability. Official documentation is authoritative but covers a limited range. Community libraries are broad but inconsistent. Leaked product prompts are informative for understanding how vendors structure instructions, but they change without notice and may be out of date.
How to assess a prompt's provenance
Provenance means knowing where the prompt actually came from. A repository that says "Claude's system prompt" without a date, a source link, or a method of collection may be publishing a fabrication or a prompt from a version that is months out of date.
- Primary source link: does the repository link to the vendor's official documentation or announcement? If so, follow the link and verify the prompt is still there.
- Date of collection: prompt repositories without dates are immediately less useful. AI products update their system prompts regularly.
- Method of collection: was this extracted by a researcher, shared by the vendor, or reconstructed from model behavior? Reconstructed prompts are guesses.
- Contributor history: a repository with many contributors and recent commits is more likely to be maintained. A single author who stopped committing two years ago is not.
For prompts about other AI products, check the vendor's own documentation first. Anthropic, OpenAI, and Google all publish examples and guidelines. A repository that conflicts with the vendor's current documentation is wrong -- the vendor's page is authoritative.
Licensing and attribution
Most GitHub repositories have a license file. Read it before incorporating a prompt into a product. The common open-source licenses (MIT, Apache 2.0, CC0) generally allow reuse with attribution. Some repositories have no license, which under US copyright law means all rights are reserved by default. Using a prompt from an unlicensed repository in a commercial product carries legal risk.
Leaked product system prompts raise a separate question: a vendor's internal system prompt is likely proprietary. Using it as-is in your own product is probably not permitted even if a repository has published it. Using it as a reference to understand structure and adapt your own prompt is a different situation, but consult a lawyer if you need a definitive answer for a commercial deployment.
Community-contributed prompts written by individuals are generally safe under their stated license. Attribution is usually a low-cost way to stay on the right side of any ambiguity.
Which types of prompt repositories are worth browsing
| Type | Best use | What to check |
|---|---|---|
| Official vendor examples | Understanding the recommended structure for a specific model. | Whether the example matches the current API version and documentation. |
| Community task libraries | Finding a starting point for a well-defined task like summarization or translation. | Date of last update, number of issues reported, whether it has been tested on current models. |
| Leaked or reconstructed product prompts | Learning how major products structure long system prompts. | Date collected, source method, whether the vendor has since updated the product. |
| Curated engineering guides | Best practices for structuring instructions, handling edge cases, output formats. | Whether the guide cites primary sources and is updated as models change. |
How to test a prompt before using it
A prompt from a repository was written for someone else's task and possibly tested on a different model version. Before relying on it, test it yourself with the model and version you plan to use.
- Read the prompt fully before testing it. Remove any references to products, personas, or constraints that do not apply to your use case.
- Paste it into the model's playground or API environment with the current model version.
- Test the core behavior it is supposed to produce. Does the model follow the format? Does it stay in scope?
- Test the edge cases: what happens when the user asks an out-of-scope question? When the input is empty? When the user contradicts the instruction?
- If the prompt works well, save your adapted version rather than the original. Note what you changed and why.
Testing also reveals whether the prompt is model-specific. Some community prompts work well on one model family and poorly on another. Note which model you tested on so you can re-test if you change models.
Keep the prompts you tested and approved
fenbs holds a Skills and prompts library alongside your project tasks and Decisions and rules. Save the adapted version of any prompt you plan to reuse, link to the repository it came from, and note the model and date you tested it. Any connected assistant can list and read the current version at the start of a session.
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Keeping track of sources and links
A good prompt is more useful when you can find its source again later. If a repository updates the prompt or the vendor changes the underlying model behavior, you need to know which version you are using and where to check for updates.
Store the source link with the prompt. Record the date you last verified the source. When you update the prompt because the original changed, note the date and why. This turns a collection of prompts into a maintainable library rather than a pile of files with no history.
fenbs includes Links and listings, a place to record every external source with its status and last-checked date. Add the repositories you use regularly, mark them with a check date, and link each stored prompt to its source. When you return months later, you will know which sources are still active.
When to write your own instead
A repository prompt is a starting point, not a finished product. If the task is specific to your domain, the repository is unlikely to have an exact match. Spending thirty minutes adapting a close example is often better than spending an hour searching for a perfect one that does not exist.
For complex prompts -- long instructions with multiple sections, elaborate formatting rules, domain-specific terminology -- writing from scratch with a clear structure is usually faster than reverse-engineering someone else's choices. The prompt engineering guide covers the foundations, and system prompt XML explains a structured approach.
How to do this with fenbs
- Create a free account. Start with a personal kanban board for your prompt library work.
- When you find a repository prompt worth exploring, add a task with the source link, the intended use, and your initial assessment of its provenance and license.
- Test the prompt against the current model. Record the test results in the task: what worked, what you changed, what failed.
- Save the approved version as a Skill in your fenbs project. Link it back to the source repository and note the date tested.
- Add the repository itself to Links and listings so you can check it again when models update. Mark the last-verified date so you know when to check.
For keeping prompts reusable across AI tools, MCP tools, resources and prompts explains how a connected assistant can read your prompt library directly. System prompt examples is a focused collection of patterns worth adapting.