tabular-cleanup

name	tabular-cleanup
description	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.
compatibility	Requires Python 3.9+
metadata	{"version":"0.2.0"}

Tabular Cleanup Loop

A single agent that takes a messy data dump (<artifact>) to the cleanest defensible state, no human in the loop once running. The objective is a checklist, not a score: the agent infers a data contract, compiles it into deterministic binary checks (each reports a violation count, never a weighted float), then each iteration profiles the table, picks the worst open check, applies one pandas transform to resolve it, and keeps it only if that check's violations strictly drop with no collateral damage. Every accepted transform appends to a replayable pipeline.py; every attempt logs to the ledger. The work decomposes into structure (parse correctly, one tidy table, sane types) → contract synthesis (turn every observed anomaly into a check) → the fix loop. Contract synthesis is where quality is won or lost: an issue the profiler notices but never compiles into a check (classically, many spellings of one category) silently survives — a green checklist over dirty data. Checks read the stored value, so canonicalization is real work the loop must do, not a check-time trick.

When to use

Use this to autonomously clean a messy table to an inferred, confirmed contract where every defect is a deterministic check the loop must drive to zero or to an honest residual. Default to strict contract inference (a lenient contract that lets dirty data go "all green" fast is the primary failure mode); the only human checkpoint is confirming the contract at setup, after which the loop runs to a stop condition. Not for open-ended discovery over an already-clean dataset, diagnosing one known anomaly, or checking an external claim against sources — those are analytical loops; this one rewrites the data to match a contract.

Setup

Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it, confirm the values in one line, and skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is available) infer a likely value for each binding and present it as the recommended option; on other hosts ask each as a quoted plain-text prompt. Then write loop.run.yaml (format: examples/run.example.yaml) and confirm the values before creating any other files. The contract (below) is the one decision the user must actively approve — infer it, then get explicit sign-off; everything after is autonomous.

binding	meaning	default	how to infer
<artifact>	messy table to clean; READ-ONLY (v0 is a copy)	—	scan the working dir for a data file; detect <format> + delimiter/encoding/header/quote from the extension
<contract>	inferred per-column contract + cross-column rules + guardrails; the sole authority for "correct"	—	profile the raw artifact, then synthesize (see below) and confirm
<retention_floor>	cumulative unique_rows_kept / unique_rows_in_v0 must stay ≥ this (denominator is v0 deduped)	0.95	—
<protected_columns>	columns that must survive; dropping one needs explicit allowance	all (*)	—
<impute_cap>	max fraction of a column's cells that may be imputed (synthetic substitute inserted)	0.20	—
<analysis_cmd>	interpreter that runs profiling/transform code in the user's env (has pandas)	python3	pyproject.toml/.venv/uv in the working dir
<sandbox_root>	where tcl/ (versions, transforms, profiles, ledger, pipeline) lives	./sandbox	—
<gate> / <budget>	backstop only: iterations/tokens/time and its cap	iterations / 30	—

Profiling and transform code run in the user's environment via <analysis_cmd>, so they may use pandas. Keep helper code stdlib-first: probe heavy imports with try/except ImportError and degrade, or offer a consented uv pip install "pandas==<ver>" — never assume it is present.

Contract synthesis (the make-or-break step — do not skip)

Profile the raw artifact, then in two parts turn what you observe into the contract:

Part 1 — Structure. Confirm it parsed correctly (delimiter/encoding/header), is a single tidy table (one variable per column, one value per cell, one observation per row — no merged cells, stacked sub-tables, or multi-value cells), and that names/dtypes are sane. A mis-parsed table makes every column check meaningless; structural defects become transforms/checks too.

Part 2 — Per-column contract + rules + guardrails. Per column, determine its semantic type and canonical form, then emit: dtype, nullable?, key?, range (numeric min/max), regex (format), canonical categories + a merge_map of variants → canonical (repair guidance, not a check-time substitution), severity (default high for keys/required columns, else normal), and an optional outlier method. Add cross-column rules the data evidences (e.g. start ≤ end). Then the guardrails above (retention_floor, protected_columns, impute_cap). Three rules keep this trustworthy: commitment — every observed anomaly compiles to a check or is explicitly waived with a reason (several spellings of a value MUST get a categories check, never stay free text); canonical form is the stored value — declaring a canonical set creates the open violations the loop clears by rewriting; strictness bias — when unsure, add the stricter check (over-strictness is cheap to undo; a wrong check is worse than a missing one, so never guess column meaning the data doesn't evidence).

Confirm the inferred contract with the user (Claude Code: present as a compact table via AskUserQuestion; other: print as YAML and ask to confirm/amend). After sign-off, write the contract + the compiled checklist into <sandbox_root>/tcl/schema.yaml; the contract is then fixed for the run. Full contract shape: examples/run.example.yaml.

The checklist

Each contract rule compiles to one binary check that reports a violation count and a state. There is no weighted float and no epsilon — that single fact removes all denominator ambiguity.

check id (pattern)	dimension	counts violations where…
<col>.required	completeness	a nullable:false cell is null
<col>.type	validity	a non-null cell isn't parseable as the contract dtype
<col>.range	validity	a non-null numeric cell is outside [min,max]
<col>.regex	validity	a non-null cell fails the format regex
<col>.categories	consistency	a non-null stored value isn't a canonical category
<rule_id> (cross-column)	consistency	a row violates a cross-column rule
<col>.key	uniqueness	a declared-key value is duplicated
rows.unique	uniqueness	a row is an exact duplicate
<col>.outlier (opt-in)	plausibility	a numeric cell is a statistical outlier (only if a method is declared)

Check states: pass (0 violations) · open (violations remain, still attackable) · residual (violations remain but can't be fixed within guardrails without regressing another check — an accepted, reported dead end). Nulls are a violation only for .required — the .type/.range/.regex checks ignore nulls. Priority among open checks: highest severity first, then most violations. The headline checks_passing% = checks_in_pass / total_checks is for report.md and status lines only — it never drives keep/revert. Honest limit: the checklist measures well-formed & self-consistent, not true accuracy (is "John Smith" the correct name?) — outlier/range checks are the plausibility proxy.

The loop

Let <best> be the current accepted version (starts at v0, an exact copy of <artifact>). Copy this checklist and tick items off, iterating on <best> until a stop condition fires:

• Profile <best> — run deterministic profiling code → profiles/profile-vN.json (schema below): per-column stats + every check's violation count + state + retention + headline checks_passing%; write a 4–8 line human summary.
• Pick the target & propose ONE transform — among open checks, pick by priority (severity high, then most violations). State the check id, its violation count, the repair strategy, and the expected effect. Never propose a transform you can't tie to a specific open check.
• Write the transform as a pure transform(df) -> df pandas function, deterministic, touching only what the target check requires (it must run standalone in pipeline.py later).
• Apply → candidate — load <best>, apply, write versions/vN+1.<ext>. On error, fix once; if still broken, log status=crash and discard (don't advance <best>).
• Re-profile the candidate — re-run the checklist on vN+1 for new violation counts + retention.
• Keep or revert (pure checklist logic, no epsilon) — accept iff the target check's violations strictly decreased AND no other check's violations increased AND no guardrail tripped. Else revert. If that was the last guardrail-safe strategy for the target, mark it residual.
• Log + persist — append one ledger.tsv row. If kept: copy the function to transforms/tNN_<slug>.py and append its call to pipeline.py in order.
• Check stops — if none fired, go to the next iteration on the new <best>.

Least-destructive principle (the loop's bias): prefer repair over removal. Try, and take the first that is guardrail-safe and regression-free: (a) repair (parse/standardize/ canonicalize the value), (b) impute (within <impute_cap>, flagged synthetic — nulling an unrecoverable value in a nullable:true column is repair, not imputation, and is uncapped), (c) remove (drop rows/cols, within retention + protected floors). Removal is a last resort. A check becomes residual only when all three genuinely fail — e.g. a malformed value in a nullable:false column where repair can't recover it, dropping breaches retention, and nulling would regress that column's .required check. An honest residual is correct; never null a required field or invent a value just to clear a check.

Stops

Because checks are deterministic counts, "done" is exact — no epsilon, no plateau heuristic. This is the key difference from the autoresearch loops: it is designed to terminate. Stop on any:

1. All green — every check is pass. The clean, successful exit.
2. All-residual — zero open checks remain (every check is pass or residual); the remaining violations are provably unfixable within the guardrails. The natural exit.
3. Budget — <gate>/<budget> reached. Backstop only.

Before marking the last open check residual (which triggers stop #2), confirm you actually tried all three strategies (repair → impute → remove) — don't declare it unfixable just because the first regressed another check or hit a floor.

On stop: set cleaned.<ext> to <best>, write report.md, and print the final summary (headline checks_passing%, pass/residual counts, which stop fired).

Outputs & formats

The run produces three deliverables:

1. cleaned.<ext> — the best version (copy of <best>).
2. pipeline.py — a standalone, replayable script: read raw <artifact> → apply each accepted transform in order → write cleaned. Deterministic and idempotent; re-running it on the raw dump reproduces cleaned.<ext> exactly. The audit-grade artifact.
3. ledger.tsv + report.md — the full audit trail and a before/after summary.

<sandbox_root>/tcl/ layout: schema.yaml (bindings + contract + compiled checklist), versions/ (v0.<ext> is the READ-ONLY copy of the raw artifact; new vN written per candidate), transforms/ (one file per accepted transform), profiles/, ledger.tsv, pipeline.py, report.md.

ledger.tsv — tab-separated, append-only, one row per attempt, never commas in free text. One row per schemas/ledger.schema.json; status ∈ {keep,revert,residual,crash}:

iter	transform_id	target_check	dimension	viol_before	viol_after	regressions	retention	status	rows_affected	cells_affected	summary
1	t01_drop_dupes	rows.unique	uniqueness	7	0	0	1.000	keep	7	0	remove 7 exact duplicate rows
2	t02_canon_status	status.categories	consistency	142	0	0	1.000	keep	0	142	rewrite variants to canonical set
3	-	contact.regex	validity	3	3	-	0.94	residual	-	-	repair impossible; drop breaches retention; null regresses contact.required

profiles/profile-vN.json — per schemas/profile.schema.json (per-column stats + every check's count & state + retention + headline). Compact instance:

{"version":"v2","rows":980,"cols":7,"checks_total":11,"checks_passing":9,
 "checks_passing_pct":0.82,"total_violations":3,"retention":0.98,
 "checks":[{"id":"status.categories","dimension":"consistency","scope":"status",
            "severity":"normal","violations":0,"state":"pass"}
 
             
 

report.md — v0-vs-final headline checks_passing%, the full checklist with start/end violation counts, the confirmed contract, the ordered accepted transforms (the pipeline), the residual set (with why), what was dropped/imputed, and which stop fired.

Constraints

• The raw <artifact> is never modified — all work is in <sandbox_root>/tcl/; v0 is a copy, and the loop only writes new vN versions, because the raw dump is the replay ground truth.
• Never add rows, fabricate a key/index, or invent values silently — imputation is allowed but flagged synthetic, logged, and capped at <impute_cap> per column.
• Respect the guardrails — a candidate breaching the retention floor, dropping a protected column, or exceeding the impute cap is reverted no matter how many checks it cleared, because a checklist is otherwise gameable by deletion.
• Every check is a deterministic count, not LLM judgement; the confirmed contract is the sole authority for "correct" — do not silently change the contract or compiled checklist mid-run.
• Keep/revert is pure checklist logic — keep iff the target check improves with no regression and no guardrail breach. No epsilon, no weighted score driving the decision.
• One transform per iteration, a pure pandas function tied to one open check, so each violation delta is attributable; no blind multi-step rewrites.
• A check unfixable within the guardrails becomes residual and is reported — never forced shut by fabricating or nulling required data.
• Keep pipeline.py faithful — exactly the accepted transforms in order; re-running it on the raw dump must yield cleaned.<ext>.
• Do not pause to ask whether to continue while open checks remain — setup is the only human checkpoint; the loop runs to a stop condition. Do not install packages the env lacks beyond a consented pinned install, and do not commit tcl/ to git.