lang-data-analysis
Use when planning or auditing the analysis of a Language (LSA) manuscript so the evidence credibly supports the theoretical claim. Covers quantitative modeling (mixed-effects in R), phonetic measurement, corpus statistics, and the analytic trail from glossed data or judgments to the generalization. Improves the analysis chain; it does not fabricate results.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Analysis (lang-data-analysis) At *Language* the analysis exists to make the **theoretical claim credible** — not to display technique or notation. A cross-subfield, double-anonymous reviewer will ask whether the evidence actually warrants the generalization and whether uncertainty is handled honestly. Where the work is quantitative, *Language* now expects **properly specified models** (typically mixed-effects models in R) rather than by-subject t-tests or raw counts; where it is analytic, it expects the pattern to be demonstrable from the glossed data. This skill stress-tests the analysis chain in the idiom of your work. ## When to trigger - Planning the analysis, or auditing it before writing up - A reader doubts the statistics, the evidence-to-claim link, or the treatment of variability - Reconciling multiple data sources (corpus + experiment, judgments + text) into one argument - Deciding which analyses are confirmatory vs. exploratory ## Analysis norms (by mode) ### Quantitative (experiment / corpus) - Fit **mixed-effects models with the random-effects structure the design justifies** (crossed by-subject and by-item random effects; random slopes for within-cluster predic
- When to trigger
- Analysis norms (by mode)
- Quantitative (experiment / corpus)
- Phonetic
- Analytic (formal)
- Historical / typological
- Convergent evidence (a Language strength)
- Referee-pushback patterns on the evidence chain (Language fixes)
- Calibration (Language appetite, hedged)
- Anti-patterns
- Evidence pass for Language
- Output format
- Supplementary resources
What does the lang-data-analysis skill do?
Use when planning or auditing the analysis of a Language (LSA) manuscript so the evidence credibly supports the theoretical claim. Covers quantitative modeling (mixed-effects in R), phonetic measurement, corpus statistics, and the analytic trail from glossed data or judgments to the generalization. Improves the analysis chain; it does not fabricate results.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill lang-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.