Extract blog candidates from learning logs and generate markdown drafts.
npx skills add https://github.com/jjub0217/claude-code-skills --skill blog-write-jjub0217
Please help me install this Agent Skill. Skill: blog-write Source: https://github.com/jjub0217/claude-code-skills/tree/main/skills/blog-write Command: npx skills add https://github.com/jjub0217/claude-code-skills --skill blog-write-jjub0217
학습 로그에서 블로그 후보를 자동으로 추출하고 초안을 작성하는 과정을 자동화하여, 개발 학습 기록의 공유를 쉽게 만듭니다.
설정된 학습 로그 경로를 사용해 '/blog-write'를 실행하여 블로그 초안을 생성합니다.
High-intent search queries and answers about installing and using this skill.
To turn learning logs into tech blog posts, this Skill parses weekly learning logs to extract blog candidates, generates topic outlines, and drafts full markdown posts saved to a designated directory. It automates topic selection, outline generation, and full-draft creation from your learning records.
The best way to generate blog topics from development study notes is parsing learning logs to identify candidate subjects. This Skill extracts potential topics from your configured learning-log path, allowing you to select from candidates or input a custom topic to begin drafting.
Yes, you can create markdown drafts directly from weekly learning logs. The Skill handles full-draft creation based on context from your logs and outputs the result as a markdown file saved to a specified directory, streamlining the knowledge-sharing process.
To prepare a blog outline from technical study records, the Skill parses your learning-log path to identify blog candidates, then generates a structured outline based on your selected or custom-entered topic. This prepares the framework before the full draft is written.
Yes, you need a configured learning-log path for parsing candidates and a designated directory to save the output markdown drafts. These path configurations are required to execute the drafting process and store the resulting blog post files correctly.
The automation is limited to processing learning logs within a configured path and outputs markdown drafts to a designated directory. It handles topic selection, outline generation, and drafting, but requires a pre-existing learning-log structure to parse candidates effectively.
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