scholar-ling
scholar-ling is an NLP, speech & audio ML skill for AI agents, published by joshzyj in the open-scholar-skill repository on GitHub. Skillspace rates scholar-ling D (69/100) and it passed the safety scan; the rating is based on static analysis only.
Design and analyze studies in sociolinguistics, language variation, acoustic phonetics, discourse analysis, language contact, and computational linguistics. Covers VARBRUL/Rbrul for variation analysis, mixed-effects models for acoustic data, conversation analysis, critical discourse analysis, language attitudes (matched guise), corpus linguistics, computational sociolinguistics (conText embedding regression, LLM annotation for linguistic coding, BERT classification, semantic change detection, STM topic models), experimental sociolinguistics (factorial vignette experiments, IAT, reaction time paradigms, priming studies), Biber Multi-Dimensional Analysis (67 features, register comparison), and TTS-based matched guise tests. Produces R/Python code, publication-quality tables and figures, and saves output to disk. Use for Language in Society, Journal of Sociolinguistics, Language, Applied Linguistics, Nature Human Behaviour, Science Advances, and Nature Computational Science.
- Grade
- D
- Quality
- 69 / 100
- Safety
- Passed
- GitHub stars
- 102
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 97 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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