reduce

Extracting atomic claims from unstructured articles and transcripts involves processing raw text into structured statements. This Skill automates that extraction, attaching provenance, confidence, and topic tags to each claim for reuse.

The best way to add provenance metadata to extracted text claims is by generating YAML frontmatter. This format structures source metadata, claim details, and confidence levels directly with the extracted data.

Yes, you can process interview transcripts into structured data with confidence scores. The system reduces long transcripts into compact sets of actionable claims, each tagged with provenance and confidence metrics.

Yes, the claim extraction process supports depth and format options for documents. It satisfies deterministic input processing, allowing you to specify the extraction depth when reducing source material.

Turning raw notes into structured, reusable statements for data analytics requires reducing the text into atomic claims. This transforms unstructured noise into signal by outputting provenance-rich statements with topic tags.

Using atomic claims instead of summarizing the entire source document enables traceability and recombination. Each individual claim retains its specific provenance and confidence level, whereas summaries lose track of distinct facts.