Extract grounded question–reasoning–answer items from text with optional domain context.
npx skills add https://github.com/grahama1970/fetcher --skill qra
Please help me install this Agent Skill. Skill: qra Source: https://github.com/grahama1970/fetcher/tree/main/.agents/skills/qra Command: npx skills add https://github.com/grahama1970/fetcher --skill qra
QRA enables automatic extraction of grounded question–reasoning–answer pairs from plain text, reducing manual curation and ensuring answers are anchored in source material.
Run the QRA pipeline on a text file using the run script with an optional domain context to focus extraction.
High-intent search queries and answers about installing and using this skill.
You can extract question–reasoning–answer pairs from plain text by applying an LLM pipeline that splits content into logical sections, validates grounding against the source, and optionally stores results in memory.
Grounded QRA extraction identifies questions, reasoning steps, and answers from plain text while ensuring every answer is anchored in the original source material through grounding validation and fallback heuristics.
Yes, you can supply optional domain context when extracting QRA items from transcripts or notes, allowing the LLM to tailor the generated question–reasoning–answer pairs to your specific subject area.
Grounding validation checks that extracted answers are backed by the original source content, and the pipeline supports fallback heuristics to maintain source anchoring when direct validation is insufficient.
Yes, the pipeline supports storing extracted QRA items to memory, enabling downstream retrieval and workflow integration directly from the text analysis and knowledge extraction process.
Automatic QRA extraction relies on LLM accuracy and source text clarity; while grounding validation and fallback heuristics help, complex or ambiguous notes may still produce less reliable question–reasoning–answer pairs.
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