# 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. - Skill: `yogsoth-ai/unit-segmentation` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add yogsoth-ai/unit-segmentation` - Raw SKILL.md: https://api.skillmd.com/api/skills/yogsoth-ai/unit-segmentation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: yogsoth-ai (https://skillmd.com/u/yogsoth-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/yogsoth-ai/unit-segmentation --- # 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. |