Misc

About the Misc system prompts

This collection holds 8 Misc system prompts, covering Fellou Browser, Kagi Assistant and Le Chat. Each entry is the instruction text that sits above the conversation and shapes how the model answers — its role, its tone, the tools it may reach for, and the things it must refuse. Reading them is the fastest way to understand why a Misc model behaves the way it does, and the fastest way to borrow patterns that already work.

Every prompt on this page is reproduced in full, with no truncation or paraphrase, so you can copy the exact wording into your own build and compare it line by line against the prompts other providers ship.

How to use them

Treat a Misc prompt as a starting structure, not a script to paste unchanged. The parts worth keeping are the scaffolding: how the role is stated up front, how tool use is gated, how refusals are worded, and how output format is pinned down. The parts worth replacing are the product-specific details — feature names, brand voice, and any capability your own model does not have.

A prompt tuned for one model rarely transfers cleanly to another. Context windows, tool-calling conventions, and refusal behaviour all differ, so run a short evaluation set through your target model before you rely on a borrowed prompt in production.

What is a system prompt?

A system prompt is the hidden first message in a conversation. The user never sees it, but the model reads it before every reply, which makes it the single strongest lever over an assistant’s behaviour — stronger than few-shot examples and, in most cases, stronger than anything the user types afterwards. That is why published and leaked system prompts are studied so closely: they are the clearest available record of how a production assistant was actually built.

How to read one critically

A system prompt is a snapshot, not a specification. Providers revise them continuously, sometimes weekly, and a prompt captured in one release may already be out of date — so treat the date on an entry as part of the evidence. Length is not quality either: some of the most effective prompts here are a few hundred words, while others run to tens of thousands because they carry an entire tool schema inline.

The most transferable material is usually the negative space — the explicit “do not” rules and the tie-breakers that tell the model what to do when two instructions conflict. Those are the parts written after something went wrong in production, which makes them the parts worth copying.