data-cleaning

name	data-cleaning
description	Clean, profile, validate, reshape, and document messy tabular, text, JSON, and relational data through an evidence-first, reproducible workflow. Use when preparing data for analysis, reporting, modeling, ingestion, migration, or matching. Do not use for statistical modeling, dashboard design, or operating a named data platform; route those tasks to data-scientist, data-engineering, or the relevant tool skill.
license	MIT
compatibility	Works with any Agent Skills client. The bundled profiler requires Python 3.9+ and the standard library; ecosystem tools are optional.
metadata	{"domain":"data-quality-and-cleaning","source":"primary-docs-plus-orientation-article"}

Data cleaning

Treat cleaning as a controlled transformation of an observed dataset, not cosmetic editing. Preserve raw input, state the target use and grain, make every lossy decision explicit, and prove that the cleaned output satisfies a contract.

Route by task

Need	Read next
End-to-end method, scope, and stopping rules	references/methodology.md
Choose a library or platform	references/tool-selection.md
Missingness, duplicates, types, ranges, categories, dates, joins	references/operations.md
Text, identifiers, Unicode, and entity resolution	references/text-and-entity.md
Schemas, contracts, validation, drift, scale	references/validation-and-scale.md
CLI, OpenRefine, monitoring, and interactive remediation	references/cli-and-interactive-tools.md
Source claims and version-sensitive caveats	references/sources.md
Plan, logs, exceptions, contracts, or reports	templates/cleaning-plan.md, templates/transformation-log.jsonl, templates/exception-register.csv, templates/schema-contract.yml, templates/quality-report.md
Lightweight profile or reconciliation	Run python3 scripts/profile_dataset.py --help or python3 scripts/reconcile_dataset.py --help

Available Scripts

Script	Purpose	Invocation
scripts/profile_dataset.py	Dependency-free first-pass profiling of a CSV, TSV, or JSONL input without modifying it: missingness, cardinality, type candidates, duplicates, ranges, and value anomalies. Run it at workflow step 3 (Profile before changing) as the evidence-gathering pass before designing any cleaning decision.	python3 scripts/profile_dataset.py data.csv --output profile.json
scripts/reconcile_dataset.py	Reconciliation between a before and after delimited dataset: row counts, key uniqueness/overlap, and per-column sums (--sum), keyed by --key, writing a machine-readable report. Run it during Validate twice / Review to prove grain preservation and quantify exactly what a transformation changed.	python3 scripts/reconcile_dataset.py raw.csv cleaned.csv --key id --sum amount --output reconciliation.json
scripts/test_profile_dataset.py	Pytest suite covering the profiler's behavior on representative inputs. Run it after modifying the profiler or when auditing its output; CI discovers it automatically.	python3 -m pytest scripts/test_profile_dataset.py
scripts/test_reconcile_dataset.py	Pytest suite covering the reconciler's keying, summing, and reporting behavior. Run it after modifying the reconciler or when auditing its output; CI discovers it automatically.	python3 -m pytest scripts/test_reconcile_dataset.py

Default workflow

1. Frame: identify the decision, owner, source, privacy constraints, unit of observation, keys, expected grain, time window, and acceptance threshold. Do not silently infer a business rule from a suspicious value.
2. Freeze evidence: record source path/URI, retrieval time, file size/hash where feasible, encoding, delimiter, schema, row/column counts, and software versions. Keep raw data read-only and write to a new output.
3. Profile before changing: inspect missingness, sentinel values, duplicates, cardinality, type candidates, ranges, invalid dates, whitespace/Unicode anomalies, cross-field relationships, and drift. Use the bundled profiler for a dependency-free first pass.
4. Design decisions: classify each finding as preserve, standardize, repair, impute, quarantine, reject, or escalate. Record rationale, rule, affected rows, confidence, reversibility, and owner.
5. Transform in layers: prefer deterministic named steps: parse → canonicalize → type/coerce → validate → deduplicate → resolve entities → impute/quarantine → reshape. Keep raw, staged, rejected, and final datasets distinct.
6. Validate twice: run structural checks before and after transformation. Validate row/grain preservation, key uniqueness, referential integrity, allowed values, units, bounds, null policy, and expected distributions. Tests should identify failing records.
7. Review and release: compare before/after metrics, inspect samples of every changed class, obtain domain approval for semantic or lossy changes, publish the report and provenance, and make the run reproducible.

Non-negotiable controls

• Never overwrite raw data or silently drop rows, columns, categories, outliers, or unmatched entities.
• Separate invalid, missing, not applicable, not collected, and withheld when the domain distinguishes them.
• Parse dates and numbers with an explicit locale, timezone, unit, and error policy. Count parse failures; do not silently turn them into nulls.
• Normalize text conservatively. Retain original and normalized values plus confidence when matching or repairing.
• Fit imputers, encoders, normalization parameters, and deduplication rules only on the permitted training/reference partition. Avoid leakage across time or evaluation boundaries.
• Treat profiling as evidence for investigation, not permission to auto-fix. An anomaly can be a real event.
• Use quarantine for records that cannot be repaired safely. “Clean” means accepted by a stated contract, not “no rows remain.”

Completion gate

A cleaning task is complete only when the output, transformation/decision log, validation evidence, provenance, and unresolved issues exist; raw data remains intact; acceptance checks pass; and a reviewer can reproduce or audit the result. If semantic ambiguity remains, stop at quarantine or escalation rather than inventing a value.

When not to use

Do not use this skill for inferential statistics or model selection, which belong to data-scientist; for ETL orchestration, storage, or production data-quality operations, route to data-engineering; or for operating a named validation or database platform, route to that tool's skill. This skill supplies cleaning judgment and artifacts those workflows consume.

Prerequisites

• Python 3.9+ with the standard library only for both bundled scripts (per compatibility); ecosystem tools (OpenRefine, pandas-backed tooling) are optional accelerators covered in references/cli-and-interactive-tools.md.
• A raw input you can keep read-only plus write access to a separate output location — every script reads without modifying its input.
• The templates above when the task warrants formal artifacts: a cleaning plan, transformation log, exception register, schema contract, or quality report.
• pytest only when running the bundled test suites.

Limitations

• The bundled profiler and reconciler are first-pass evidence tools: they surface anomalies and quantify deltas but do not decide preserve/repair/impute/quarantine — those classifications stay with the workflow's decision step.
• Both scripts handle delimited text and JSONL; binary formats, relational databases, and nested document stores need other tooling.
• Profiling output is evidence for investigation, never permission to auto-fix; an anomaly can be a real event.
• A passing reconciliation proves structural preservation on the checked keys and sums only — semantic correctness of values still requires the review and release gate.