data-transformer
Skill78 starsChanged 23 days agoMITView on GitHub
Derive analysis variables from cleaned data under an explicit, reversible transformation ledger. No silent recoding, no leakage.
In this repository10 other files it gives its agents.
- data-masterSkill
- data-cleanerSkill
- data-explorerSkill
- data-profilerSkill
- data-routerSkill
- 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, 4 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 17
When the analysis needs a derived variable (log transform, ratio, binning, unit conversion, date parts) that is not already a column.
- Very specific: line 42
Every derived column has a ledger record and a stated inverse (or reversible: false with the reason).
- Distinctive: line 58
do not derive a variable inside the statistical script and forget to record it.
- Distinctive: line 59
do not transform the grouping variable into the outcome.
- Distinctive: line 60
do not transform a column the cleaning plan withheld.
≈ “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-transformer
Stage 2b 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
When the analysis needs a derived variable (log transform, ratio, binning, unit conversion, date parts) that is not already a column. Optional: a run with no derived variables skips this skill and records that it did.
Inputs
- cleaned data (manifest replayed)
- an explicit transformation spec: source columns, operation, parameters, output name
Outputs
analysis/transformed.csv(or an in-memory frame recorded in the ledger)analysis/transform_ledger.json: one record per derived column with{output, operation, inputs, parameters, reversible, rule_source, alternative_considered, rejected_reason}
Commands
# transformations are declared in the run spec, not typed at a prompt
python -c "import json,sys; print(json.dumps(json.load(open('outputs/analysis/transform_ledger.json')), indent=2))Gate (default FAIL)
Every derived column has a ledger record and a stated inverse (or
reversible: false with the reason). A derived column may not be created from the
outcome it will later predict -- that is leakage, and it is the single most common way a
clean pipeline produces an impressive, meaningless result.
Failure modes seen in real runs
- Target leakage. A feature computed from the outcome inflates the effect to implausibility; the ledger makes the input columns visible so the leak can be caught.
- Silent recoding.
M/F->0/1without a mapping record makes the direction of the effect unrecoverable. - Transforming after cleaning. A log transform applied to the raw column while the cleaned column keeps its original scale produces two incompatible reports.
Do not
- do not derive a variable inside the statistical script and forget to record it;
- do not transform the grouping variable into the outcome;
- do not transform a column the cleaning plan withheld.
Upstream / downstream
- previous:
../data-cleaner/SKILL.md - next:
../statistical-analyzer/SKILL.md - contract:
../../references/workflow-contract.md
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