set_llm_instructions

To set custom LLM instructions, you provide directives for tone, formatting, and behavior using a strict input schema. The validated instructions are stored in-memory and steer all subsequent interactions within the current session until updated.

Yes, you can enforce custom LLM behavior across multiple prompts by applying session-based instructions. Once configured, the directives persist in-memory and affect all subsequent responses within that specific session until explicitly changed.

The best way to adapt an LLM for a high-precision coding task is to set custom instructions that enforce concise, formal explanations and consistent formatting. These directives persist in-memory to steer all subsequent responses within the active session.

Yes, you need pydantic installed to apply session-based LLM instructions. The Skill uses pydantic to enforce a strict input schema that validates your custom tone, formatting, and behavior directives before storing them in-memory.

No, custom LLM instructions are not preserved across different sessions. The directives are stored strictly in-memory for the current session only, meaning they affect subsequent responses temporarily and must be reapplied for new sessions.

Using a strict input schema for setting LLM behavior directives ensures that tone, formatting, and content emphasis rules are properly validated before being applied. This prevents malformed instructions from disrupting in-session LLM customization.