unit-classification
Skill1 starsChanged 3 months agoApache-2.0View on GitHub
Classify each pre-segmented text unit independently against a fixed label set (Argumentative Zoning, CoreSC, PubMed-RCT, Swales move/step, CODA-19, TDMS, or CSFCube's facet labels), single-layer with no cross-unit dependency. Use this after unit-segmentation has split the text, whenever a sentence- or clause-level rhetorical/functional classification is needed; do not use this for methods requiring document-level coreference reasoning (see multi-stage-cascade-extraction instead).
In this repository34 other files it gives its agents.
- acu-nugget-recallSkill
- argumentative-zoningSkill
- atomic-unit-matchingSkill
- atomic-unit-recall-aggregateSkill
- atomic-unit-writingSkill
- claim-label-predictionSkill
- claim-writingSkill
- domain-level-judgmentSkill
- dual-column-self-checkSkill
- engineering-config-gradingSkill
- first-pass-skimSkill
- keshav-three-passSkill
- multi-stage-cascade-extractionSkill
- paper-fetchSkill
- qalmriSkill
- qalmri-worksheetSkill
- qasper-evidence-qaSkill
- quality-appraisal-checklistSkill
- question-framingSkill
- rationale-selectionSkill
- reforms-gradingSkill
- reporting-standard-checklistSkill
- reproducibility-third-party-verificationSkill
- research-question-appraisalSkill
- rhetorical-structure-qualitySkill
- second-pass-graspSkill
- signalling-question-answeringSkill
- star-awardingSkill
- study-design-tool-gateSkill
- sum-threshold-scoringSkill
- template-slot-fillingSkill
- third-pass-deep-readSkill
- unit-segmentationSkill
- worst-case-lookupSkill
The file
Files
Unit Classification
Single-layer per-unit classification against a fixed, parameterized label set — no cross-unit or document-level dependency. Covers 7 methods (AZ/CoreSC/PubMed-RCT/NICTA-PIBOSO/CSAbstruct/CODA-19/Swales) plus TDMS's tuple-output variant, plus CSFCube's 3 facet labels as one more label_set option.
Execution
Subagent — spawned via spawn-agent skill.
Why SciERC/SciREX/NCG Are NOT Parameterized Here
An earlier graph draft tried to fold SciERC/SciREX into this node via a boolean toggle; the coverage audit (S6) found this doesn't work — those methods need document-level coreference clustering and (for SciREX) saliency judgment over ALL mentions in the paper, not per-unit independent classification. A boolean can't absorb that difference; they live in multi-stage-cascade-extraction instead.
CSFCube's Role Here
csfcube-facet is documented as out-of-scope as its own SOP (its real task — multi-document pairwise relevance ranking — has no single-paper analog), but its 3 facet-label definitions (Background/Objective, Method, Result) are reused here as one more valid label_set option, per spec §3.
<!-- BEGIN available-tables (generated) -->Available SOPs
| SOP | When to use |
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
<!-- END available-tables (generated) -->Does it work?
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