data-profiler
Skill78 starsChanged 23 days agoMITView on GitHub
Profile a dataset without changing it: shape, dtypes, missingness, duplicates, distributions, anomalies, candidate group/target/time columns. Emits profile.json.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- TerryFYL/ai-research-armytheir own version, first seen Nov 8, 2026
- JiamingShi-JsPhys/chelate-shelltheir own version, first seen Oct 25, 2026
- aspi6246/Claude-Code-Skills-for-Academicstheir own version, first seen Mar 26, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- aspi6246/Claude-Code-Skills-for-Academics★ 153same name
- JiamingShi-JsPhys/chelate-shell★ 10same name
- TerryFYL/ai-research-army★ 9same 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-explorerSkill
- 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. About 0 are common, 3 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
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 routing, before any cleaning plan exists.
- Very specific: line 41
Every column carries dtype, n_missing, n_unique and the raw text evidence for its role.
- Distinctive: line 58
do not modify the file being profiled.
- Very specific: line 59
do not sample silently -- sampled: true must appear in the profile and the report.
- Distinctive: line 60
do not use the profiler's dtype guess as the final dtype.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
data-profiler
Stage 1b 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 routing, before any cleaning plan exists. The profile is the only artifact the rest of the pipeline is allowed to use to decide what a column is.
Inputs
- the raw file, read as text for tabular formats (
read_mode="text") route.jsonfrom data-router
Outputs
analysis/profile.jsonfor tabular:schema_version, source, modality, sampled, read_mode, shape, columns, column_roles, anomalies, target_candidate, group_candidate, timeseries_candidate, independent_unit_hint- per-modality profiles for text / time-series / image-set / audio-set / geospatial
Commands
python scripts/profile_tabular.py --in <raw.csv> --out outputs/analysis/profile.jsonGate (default FAIL)
Every column carries dtype, n_missing, n_unique and the raw text
evidence for its role. group_candidate.rejected must explain every rejected column --
including the ones rejected for having fewer than two observed levels.
Failure modes seen in real runs
- An all-null column wins the grouping score. With
n_unique = 0, a naive balance metric returns1.0and an entropy term divided bymax(nu,1)returns2.0-- the highest score in the table. Rejectn_unique < 2before scoring. - A numeric-looking code is profiled as a measurement.
species_codeis a numeric restatement ofspecies; profiling it as an outcome produces a duplicate test. The structural restatement check must catch it. - Reading with inferred dtypes destroys the evidence.
02134becomes2134before the profiler can see it.
Do not
- do not modify the file being profiled;
- do not sample silently --
sampled: truemust appear in the profile and the report; - do not use the profiler's dtype guess as the final dtype; the cleaning plan decides.
Upstream / downstream
- previous:
../data-router/SKILL.md - next:
../data-cleaner/SKILL.md - contract:
../../references/workflow-contract.md
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