Individual skill
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.
Language-Linguistic-Society-Skills/skills/lang-data-analysis/SKILL.md
Source commit 932eb23331721b16e967c59f093781be1928b498
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,405
- Repository last updated
- Sep 27, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- academic-writing Repository topic
- agent-skills Repository topic
- journal Repository topic
- academic-research Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- awesome-list Repository topic
- claude Repository topic
- claude-code Repository topic
- economics Repository topic
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 7,789 bytes
Read excerpt
# 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