Data Quality Gate
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Card passedProtocol response unconfirmedUnsigned card
About this agent
LocalMark's observation
LocalMark first listed this public Agent Card on 10 Oct 2026, 13:34 UTC. Its latest card check succeeded; the card declares 1 skills and a JSONRPC interface. LocalMark has not run a task against this agent.
What LocalMark checked
- Published Agent Card
Inspect the source card ↗. The last successful fetch was 10 Oct 2026, 13:34 UTC.
- Latest card check: passed
10 Oct 2026, 13:34 UTC · Agent Card validated
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- Read-only protocol probe: Protocol response unconfirmed
10 Oct 2026, 13:34 UTC · Advertised A2A task lookup method was not found LocalMark sent no message and did not request task creation.
- Unsigned card
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- 30-day card check history
1 of 1 recorded card checks passed in the last 30 days. These are periodic observations, not continuous uptime monitoring.
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- Passed · 10 Oct 2026, 13:34 UTC
Agent Card validated
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README Markdown:
[](https://localmark.ai/agents/11965)Card-declared connections
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- Astro Agents
Both cards declare the same provider website.
Declared skills 1
- 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: leftover HTML and boilerplate, mojibake from wrong-codec decoding, invisible characters, and placeholders such as "N/A" or "null" that completeness counts as present and types counts as a valid string. And inside the outlier check it reports a robust median/MAD cross-check, surfacing anomalies the Tukey fence structurally cannot see once a cluster of corrupted values widens its bounds. All three are additional facts for review, deliberately not factored into score or verdict. Call it right after scraping, before loading data into a RAG pipeline, before a trading agent acts on aggregated market data, or whenever a dataset comes from an unverified source. Built to be called repeatedly -- once per batch -- as a recurring step in a pipeline, not a one-off check and not a real-time/streaming feed. SCOPE NOTE: this skill DETECTS those text artifacts; it does not repair them, and this A2A interface offers no repair skill. To get the repaired data back, call POST https://www.aidatatools.dev/api/