What problem does it solve? Deciding which research sources deserve full extraction is slow and error-prone when done manually, especially after a source quality audit flags dozens of unlinked sources. This Skill quickly estimates finding density per source and produces a prioritized triage report so extraction effort goes only where it pays off. ## Core Features & Use Cases - Verdict Assignment: Applies a decision tree to label each source EXTRACT, SKIP, DEFER, or LINK-ONLY based on actionable pattern counts and existing knowledge base coverage. - Parallel Subagent Batching: Groups sources into batches of 5-8 and dispatches subagents that skim content via WebFetch and return strict JSON verdicts. - KB Deduplication: Builds a lightweight index of existing findings to avoid re-extracting patterns the knowledge base already covers. - Use Case: After a source quality audit identifies 40 sources with zero linked findings, run the triage to discover that 12 warrant full extraction, 8 only need linkage repair, and 20 can be skipped, then queue each group for the right follow-up workflow. ## Quick Start Run source-triage with --all-unlinked to triage every source that has no linked findings and generate a prioritized report.