Strip openers, hedging, sign-offs, and emoji from LLM text.
npx skills add https://github.com/JGalego/Bag-of-Tricks --skill deadpan
Please help me install this Agent Skill. Skill: deadpan Source: https://github.com/JGalego/Bag-of-Tricks/tree/main/deadpan Command: npx skills add https://github.com/JGalego/Bag-of-Tricks --skill deadpan
Deadpan addresses the issue of excessive personality and unnecessary language in LLM responses, providing users with concise, direct information.
Use the deadpan skill to generate a direct response to the prompt 'What is the capital of France?'.
High-intent search queries and answers about installing and using this skill.
To remove unnecessary personality and fluff from LLM responses, you can use a skill mode or CLI filter to strip out openers, hedging, sign-offs, and emoji, resulting in clear, direct communication.
The best way to clean up LLM generated text for direct communication is to apply a CLI filter that post-processes the output, removing unnecessary language and enhancing clarity without altering the core information.
Yes, you can use a skill mode to generate LLM responses with minimal fluff, activating a mode that instructs the model to produce concise, direct communication by default.
LLM response cleanup removes openers, hedging, sign-offs, and emoji from text output, ensuring the processed content contains only the direct information requested without conversational filler.
Processing LLM output through a CLI filter requires a text input to process, which the filter then cleans up by removing unnecessary language and personality to produce direct output.
You should not use LLM response cleanup when conversational tone, personality, or specific sign-offs are intentionally required, as it strips these elements to enforce clear, direct communication.
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