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the optimized, token-efficient version of the leaked Claude Fable 5 / Mythos 5 system prompt. Re-engineered into clean M
A vendor-neutral adaptation of Anthropic's publicly available Claude Fable 5 / Mythos 5 system prompt.
The original prompt contains a large amount of infrastructure that exists specifically for Claude's production environment, including XML conventions, tool interfaces, product-specific behavior, and internal implementation guidance. While appropriate for Claude, much of that content provides little or no benefit when reused with other frontier LLMs.
This repository filters out those implementation details while retaining the behavioral guidance that generalizes well across modern models.
| File | Purpose |
|---|---|
universal/core.md | Core behavioral principles with minimal token usage. |
universal/balanced.md | Recommended for most use cases. Balances prompt size with behavioral coverage. |
universal/complete.md | Retains nearly all vendor-neutral behavioral guidance while remaining model-agnostic. |
This project intentionally excludes Claude-specific implementation such as:
The filtered prompts retain behavioral guidance including:
The prompts are designed to work well across modern frontier models, including:
Paste the contents of the desired prompt into your model's system prompt or system instructions field.
Where supported, the prompts can also be used as custom instructions, custom GPT system prompts, Gems, or equivalent features.
This project is not intended to replace Anthropic's original system prompt or claim to improve it.
Instead, it asks a different question:
Which behavioral principles from Claude's system prompt remain valuable after removing implementation details that are specific to Claude itself?
The result is a set of prompts optimized for portability rather than platform-specific functionality.
Contributions are welcome.
If you discover vendor-specific behavior that should be generalized, redundant instructions that can be simplified, or opportunities to improve compatibility across modern LLMs, feel free to open an issue or submit a pull request.
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