tabular-cleanup

tabular-cleanup is an Exploratory analysis & notebooks skill for AI agents, published by gaasher in the Agent-Loop-Skills repository on GitHub. Skillspace rates tabular-cleanup D (59/100) and it passed the safety scan; the rating is based on static analysis only.

Use when the user has a messy tabular data dump (CSV/TSV/parquet/Excel/JSON) and wants it iteratively cleaned to an inferred data contract — a checklist of deterministic pass/fail checks, not a quality score. A single agent profiles the table, synthesizes a per-column contract compiled into binary checks (types, nulls, duplicates, inconsistent categories, format/range violations, outliers), then applies one targeted transform at a time, keeping it only if it reduces its target check's violations with no regression and no guardrail breach. Stops deterministically when every check passes, every remaining check is an unfixable residual, or a budget is hit; emits a replayable pipeline and an auditable ledger. Not for open-ended analysis of an already-clean dataset, diagnosing one known anomaly, or verifying a claim against sources — those are analytical loops; this rewrites the data to a contract.

Grade

D

Quality

59 / 100

Safety

Passed

GitHub stars

132

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

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