semantic-pipeline
Skill2 starsChanged 7 months agoNo licenceView on GitHub
Compute three measures of semantic diversity on a parquet of texts — word-level DSI (BERT layers 6+7, Beaty/Patterson 2022 recipe), sentence-level mean pairwise cosine distance (SBERT all-MiniLM-L6-v2), and document-level chamfer Distinctness (OpenAI text-embedding-3-large, min cosine distance to any other doc in the same cohort, per Moon et al.). Use when the user says "/semantic-pipeline", "compute semantic diversity", "do the three-level diversity analysis", or any request to replicate the Moon-Kibum / Beaty semantic homogenization pipeline.
- Reads credentials
- Installs packages
In this repository18 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- archive-raw-dataSkill
- aspredictedSkill
- data-analysisSkill
- gwsSkill
- latex-gdoc-roundtripSkill
- latex-workflowSkill
- log-offSkill
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- render-surveySkill
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The file
We split this skill into the rules it gives an agent
9 rules. About 0 are common, 6 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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