data-checklisted

Checks the data quality of a survey CSV before any substantive analysis: fill/completion rates, speeders (via completion time), straightlining, thinly filled columns. Gives a traffic-light assessment and recommendations. Result as Markdown to reports/. Use as the first step on a new file or when data quality is in doubt. Invoke with your request as the argument, e.g. /data-check [optional CSV path].
kayspiegel/survey-insight-skills · ★ 2 · Data & Documents · score 78

Install: claude install-skill kayspiegel/survey-insight-skills

# Data-Quality Check Goal: **Before** any substantive analysis, assess how trustworthy the data is, so that weak data doesn't lead to strong conclusions. Result as Markdown in `reports/`. A path in the user's request selects the CSV; otherwise the one in the folder. ## Tool ``` python3 scripts/survey.py quality [--file CSV] python3 scripts/survey.py profile [--file CSV] python3 scripts/survey.py freq COL [--file CSV] ``` Run these from the project root. If `scripts/survey.py` is not there, check `.claude/scripts/survey.py` or locate `survey.py` in the project. `quality` directly returns: - **Fill rate per column** (thin columns < 50% flagged), - **Completion time** (median, range) and **speeders** (< 1/3 of the median duration), - **Straightlining** in detected rating batteries (identical answer across ≥3 shared scales). `freq` is needed only to verify a suspected contradiction with the survey context (see below), not for the quality metrics themselves. `--json` for structured values. ## Survey context If a context document sits next to the CSV (`<name>.context.md`, or `survey-context.md` when the folder holds exactly one CSV), read it — and then do something no other skill does: **check it against the data.** Everywhere else the context is background; here it is a claim under test. A contradiction between what the context states and what the data show is a **finding in its own right**, often a more serious one than a handful of speeders: - **Population vs. behavior