Convert PDFs, URLs, and text inputs into in-memory QRA pairs.
npx skills add https://github.com/grahama1970/fetcher --skill doc-to-qra
Please help me install this Agent Skill. Skill: doc-to-qra Source: https://github.com/grahama1970/fetcher/tree/main/.agents/skills/doc-to-qra Command: npx skills add https://github.com/grahama1970/fetcher --skill doc-to-qra
Convert documents into QRA pairs stored in memory to enable quick knowledge capture and reasoning without persisting data.
Run the script with a document and a memory scope to generate and store QRA pairs in memory.
High-intent search queries and answers about installing and using this skill.
To convert PDFs into question and answer pairs, this Skill processes local files or web URLs and generates in-memory Question-Reasoning-Answer triplets. It streamlines knowledge capture for research, summaries, and archival workflows without persisting data to disk.
Yes, you can generate QRA pairs from web URLs and text inputs in memory. The tool accepts flexible input formats and stores the resulting Question-Reasoning-Answer triplets under a defined memory scope for immediate querying.
The best way to distill document content into reasoning pairs without saving files is using a distill-based command path in run.sh. This approach captures knowledge in-memory, requiring a defined memory scope to store and query the generated QRA triplets.
Yes, the document to QRA conversion tool supports dry runs using the --dry-run flag and accepts optional context alongside your file or URL input. This allows you to test the distillation process before fully committing data to the memory scope.
To set up a memory scope for in-memory document distillation, you run the script with your target document and specify the desired memory scope. This configuration ensures the generated Question-Reasoning-Answer triplets are correctly stored for rapid knowledge retrieval.
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