train-voice

To capture your LinkedIn writing voice, provide past posts or Shares.csv samples for analysis. The tool extracts your sentence rhythm, openings, closings, and word choices to generate a reusable markdown voice-profile.

Yes, you can train a writing voice profile without samples by using the interview fallback. This interactive method codifies your unique tone and word choices into a markdown profile when no past posts are available.

To maintain consistent tone across LinkedIn posts and comments, use a generated voice-profile.md. Downstream drafting tools read this profile to ensure every piece of content matches your codified writing voice.

The best way to make AI posts sound natural is analyzing your past emails and LinkedIn posts. This extracts your unique rhythm and vocabulary into a profile that downstream writing tools apply to drafts.

Yes, the generated voice profile works with downstream drafting tools. It outputs a standardized markdown file that write-post and write-comment skills read to maintain your specific tone across all content.

To analyze your writing voice from past LinkedIn content, you need to provide text samples such as Shares.csv files, previous posts, or emails. These formats supply the text required for rhythm and word choice extraction.