Convert YouTube tutorial transcripts into executable n8n workflow JSON.
npx skills add https://github.com/lofibrainwav/AFO_Kingdom --skill youtube-spec-gen
Please help me install this Agent Skill. Skill: youtube-spec-gen Source: https://github.com/lofibrainwav/AFO_Kingdom/tree/main/skills/youtube-spec-gen Command: npx skills add https://github.com/lofibrainwav/AFO_Kingdom --skill youtube-spec-gen
This Skill automates the conversion of YouTube tutorial transcripts into executable n8n workflow specifications.
result = await youtube_spec_gen.convert(url="https://youtube.com/watch?v=abc123", output_format="n8n")
High-intent search queries and answers about installing and using this skill.
Convert YouTube tutorial transcripts into n8n workflow specifications by extracting actionable content from captions and using LLM analysis to map transcript elements to n8n nodes and edges. The Skill generates ready-to-import n8n JSON workflows directly from YouTube video URLs, automating the process of turning tutorial instructions into executable automation specs.
Yes. This Skill pulls actionable content from YouTube captions and generates structured n8n JSON specifications that define complete workflows. The LLM-based analysis interprets transcript instructions and produces standards-compliant n8n workflow definitions ready for immediate import and execution.
Automate n8n workflow creation by extracting and analyzing YouTube tutorial transcripts through LLM-powered conversion. This approach maps tutorial steps directly to n8n nodes and connections, eliminating manual workflow construction and ensuring specs match the tutorial's intended automation logic.
No. This Skill eliminates manual construction by automatically converting YouTube tutorial transcripts into executable n8n JSON specs. The LLM validates the generated schema and outputs workflows ready to import, reducing setup time and transcription errors.
Transcript-to-spec conversion accuracy depends on transcript clarity and tutorial structure. Complex multi-step workflows, ambiguous instructions, or non-standard n8n node usage may require manual refinement. Generated specs should be reviewed before deployment in production environments.
LLM analysis parses transcript text to identify automation tasks, inputs, and outputs, then structures them as n8n nodes and connections. The system validates the resulting JSON against n8n schema standards, ensuring generated workflows contain properly connected nodes with correct configuration.
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