Process inbound lesson files into researched markdown outputs via pipeline.yaml.
npx skills add https://github.com/bonjohen/claude-local --skill file-pipeline
Please help me install this Agent Skill. Skill: /file-pipeline Source: https://github.com/bonjohen/claude-local/tree/main/bundle/skills/file-pipeline Command: npx skills add https://github.com/bonjohen/claude-local --skill file-pipeline
Inbox lesson files pile up and require consistent routing, research, validation, and writing without manual bookkeeping.
Run the pipeline once by executing the command: python C:/Projects/morelessons/scripts/file_pipeline.py process
High-intent search queries and answers about installing and using this skill.
To automate lesson generation, you can use a file pipeline with a state machine to route inbound markdown files from a drop folder, perform web research to fill gaps, and output completed lessons with frontmatter. It processes topic ideas and context pastes automatically.
A state machine file pipeline is an automated routing system that detects inbound lesson files and moves them through processing, done, and error folders. This ensures consistent research, validation, and traceable retries without manual bookkeeping.
You can schedule automated file processing by configuring a durable cron job within your pipeline.yaml file. This schedules the pipeline to run twice daily, automatically routing and researching inbound lesson files without manual intervention.
Yes, the pipeline requires a pipeline.yaml configuration file and an EXTERNAL_LESSONS_PATH folder structure. It uses this setup to route files through processing, done, and error directories and generates outputs in data/lessons/ using the morelessons schema.
Failed lesson files are automatically moved to an error folder for traceable retries. You can rerun the pipeline process command to reattempt research and validation on these files, ensuring no inbound lessons are lost during processing.
The pipeline generates markdown outputs using the morelessons schema with frontmatter fields like original_source, difficulty, and phase. This ensures completed lessons are properly structured after automated web research and validation.
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