learn-from

To assess the credibility of academic papers and web articles, this Skill routes sources to specialized sub-skills for credibility scoring and security scanning. It automatically classifies your input, runs secure-* checks, and generates a unified report with a final credibility verdict before any application.

You can extract techniques, metrics, and gotchas from arXiv papers and code repositories by routing them through this knowledge-ingestion process. It identifies source types, extracts structured insights, and recommends whether to apply, partially apply, or skip the findings with citations.

The best way to resolve contradictions between new findings and existing guardrails is using a side-by-side comparison protocol. This Skill presents conflicting information and requires an explicit user decision before replacing any established guardrails, ensuring safe knowledge application.

To run a security scan on ingested sources before modifying your skill library, this Skill enforces security gating by invoking secure-* checks. It scans academic papers, repositories, and articles during classification, blocking any skill creation or updates until security verdicts pass.

This knowledge ingestion tool works directly with conversational notes and pasted content alongside formal papers and repositories. It detects the source type automatically, classifies the input format, and routes it to the appropriate sub-skills for insight extraction and credibility assessment.

You should not use automated source classification when your content lacks identifiable structure or bypasses standard security scanning requirements. This Skill requires source-type detection and adherence to a shared application protocol, meaning unstructured or unscannable inputs will fail the secure-* gating process.