Unit Segmentation

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 0ed42b8 3 files · 5.9 KB Updated

File contents

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.

yogsoth-ai/paper-reading/tree/main/skills/unit-segmentation commit 0ed42b8cba

Frequently asked questions

npx skillmds@latest add yogsoth-ai/unit-segmentation