| name | unit-segmentation |
| description | 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. |
| version | 1.0.0 |
| category | paper-reading |
| type | sop |
| execution | subagent |
| prompt | ./prompt.md |
| input | source_path (string), meta_path (string), segmentation_granularity (string: "sentence" | "clause"), scope (string: "full_text" | "abstract" | "intro_only") |
| output | units (list of strings), unit_offsets (list of {line: int, start: int, end: int} — line is 1-indexed into source.md, start/end are character offsets within that line) |
| reads | exactly the range named by scope |
| dependencies | {"sops":["spawn-agent"]} |
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. |
