data-explorer
Skill78 starsChanged 23 days agoMITView on GitHub
Descriptive analysis and EDA on cleaned data: distributions, missingness mechanism screen, correlations, categorical structure. No p-values for group comparisons.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- liangdabiao/claudesdk-data-chattheir own version, first seen May 24, 2026
- google-gemini/gemini-managed-agents-templatestheir own version, first seen May 17, 2026
- liangdabiao/claude-data-analysis-ultra-maintheir own version, first seen May 9, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- liangdabiao/claude-data-analysis-ultra-main★ 282same name
- google-gemini/gemini-managed-agents-templates★ 99same name
- liangdabiao/claudesdk-data-chat★ 4same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository10 other files it gives its agents.
- data-masterSkill
- data-cleanerSkill
- data-profilerSkill
- data-routerSkill
- data-transformerSkill
- data-validatorSkill
- deep-analyzerSkill
- report-generatorSkill
- statistical-analyzerSkill
- visualizerSkill
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 3 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 17
After cleaning passes its gate.
- Very specific: line 40
Every numeric variable carries n, missing_pct, mean and median, SD, IQR and skew.
- Distinctive: line 54
do not compute group p-values here -- that is the statistical-analyzer's job.
- Very specific: line 55
do not read the cleaned file with pd.read_csv.
- Distinctive: line 56
do not drop the median because the mean was requested.
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
data-explorer
Stage 2a of the data-master pipeline. The router in ../../SKILL.md owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in ../../SKILL.md (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.
When to invoke
After cleaning passes its gate. Run this before inferential statistics so the test choice is informed by the actual distributions, not assumed.
Inputs
outputs/cleaned/cleaned.csvviadm_types.load_cleaned(manifest replayed)analysis/profile.json
Outputs
analysis/descriptive.json:numeric(mean, SD, SE, median, IQR, range, skew, missing_pct),categorical(levels, counts, mode),missing_mechanism_screen,correlation_pearson
Commands
python scripts/stats_descriptive.py --clean outputs/cleaned/cleaned.csv --out outputs/analysis/descriptive.jsonGate (default FAIL)
Every numeric variable carries n, missing_pct, mean and median, SD, IQR and
skew. Reporting only the mean for a strongly skewed variable is a fail, because the
mean is then the least trustworthy number on the page.
Failure modes seen in real runs
- Mean without median hides a 3 SD outlier.
- A missingness screen presented as a mechanism proof. Little's test describes the pattern; it cannot prove MCAR. The wording must say "screening result".
- Pearson only on a monotone-but-curved pair understates the association; Spearman is reported alongside, and its use is recorded.
Do not
- do not compute group p-values here -- that is the statistical-analyzer's job;
- do not read the cleaned file with
pd.read_csv; that re-guesses the types; - do not drop the median because the mean was requested.
Upstream / downstream
- previous:
../data-cleaner/SKILL.md - next:
../statistical-analyzer/SKILL.md - contract:
../../references/workflow-contract.md
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