unit-segmentationlisted

Split a paper's text into sentence- or clause-level units (with character offsets) for downstream classification, at a caller-specified granularity and scope (full text, abstract-only, or intro-only). Use this as the mandatory first step whenever any sentence/clause-level classification method (Argumentative Zoning, CoreSC, PubMed-RCT, CSAbstruct, Swales move analysis, CODA-19) needs its input pre-segmented — always precedes unit-classification.
yogsoth-ai/paper-reading · ★ 1 · AI & Automation · score 78

Install: claude install-skill yogsoth-ai/paper-reading

# Unit Segmentation Splits text into labeling units (sentence or clause granularity, scoped to full text/abstract/intro) — pure segmentation, no labeling. ## Execution Subagent — spawned via spawn-agent skill. ## Why This Exists As Its Own Step 7 different classification methods (AZ, CoreSC, PubMed-RCT, NICTA-PIBOSO, CSAbstruct, CODA-19, Swales) all need pre-segmented units but disagree on granularity and scope — factoring segmentation out once, parameterized, avoids duplicating this logic inside `unit-classification` seven times over (graph correction L17/L18: the original graph was missing this step entirely, silently assuming pre-segmented input existed).  ## Available SOPs | SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |