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CSV Pipeline Integrity Check
x402
CSV Pipeline Integrity Check is a paid API for AI agents from foundry-csv-pipeline-integrity-mainnet.inference-chip-index.workers.dev, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Runs deterministic ETL/feed regression checks on CSV data, combining schema drift detection, data-quality deltas, severity scoring, and evidence to produce a PASS/WARN/FAIL decision.
Price / call$0.05
Success—
StatusUnknown
How this service works
CSV Pipeline Integrity: deterministic ETL/feed regression decision combining profiles, schema drift, data-quality deltas, severity, evidence, and one PASS/WARN/FAIL action.
Output
Returns a structured integrity report containing a top-level PASS/WARN/FAIL action decision, detected schema drift details, data-quality delta measurements, severity scores for each issue, and supporting evidence. The deterministic verdict allows automated pipeline gating without manual review.
Example use cases
Pre-deployment ETL regression gate
“Before we promote this ETL job output to production, run it through the CSV integrity check against our registered baseline profile and tell me if it passes or if there's any schema drift or data quality regression I should know about.”
Daily data feed quality monitoring
“We get a new vendor CSV feed every morning — can you validate today's feed against yesterday's profile, score any data-quality deltas, and alert me if anything comes back as WARN or FAIL?”
Schema drift detection after upstream change
“Our upstream system just pushed a new data export — can you check it for schema drift against our expected column definitions and give me a severity-rated evidence report on what changed?”
Technical details
Endpoint
POSThttps://foundry-csv-pipeline-integrity-mainnet.inference-chip-index.workers.dev/v1/csv/integrityWhen to prefer
Choose this endpoint when you need a deterministic, auditable PASS/WARN/FAIL verdict for CSV pipeline outputs — especially when tracking schema drift across runs, enforcing data-quality SLAs, or automating regression gates in ETL workflows. Prefer it over generic data-validation tools when you need evidence-backed severity scoring and profile-based comparison in a single call.
Side effects
- Malformed or unparseable CSV input returns a validation error
- Missing or incompatible baseline profile causes comparison failure
- Schema drift too severe to score may return an indeterminate result
- Exceeding payload size limits results in a rejection error
- Network timeout if CSV payload is unusually large
Output
Schema · Response
{
"type": "json",
"example": {
"reasons": [
"INFERRED_TYPE_CHANGE",
"SCHEMA_CURRENT_TYPE_MISMATCH"
],
"service": "csv_pipeline_integrity",
"version": "csv-integrity-v1.0.0",
"findings": [
{
"code": "INFERRED_TYPE_CHANGE",
"action": "BLOCK_PIPELINE",
"column": "amount",
"message": "Column amount changed inferred type from integer to string.",
"category": "TYPE",
"evidence": {
"currentType": "string",
"baselineType": "integer"
},
"severity": "HIGH"
},
{
"code": "SCHEMA_CURRENT_TYPE_MISMATCH",
"action": "BLOCK_PIPELINE",
"column": "amount",
"message": "current column amount does not conform to type number.",
"category": "SCHEMA",
"evidence": {
"expectedType": "number",
"observedType": "string"
},
"severity": "HIGH"
}
],
"comparison": {
"rowDelta": 0,
"columnDelta": 0,
"addedColumns": [],
"removedColumns": [],
"reorderedColumns": [],
"missingRateChanges": [
{
"delta": 0,
"column": "id",
"current": 0,
"baseline": 0
},
{
"delta": 0,
"column": "name",
"current": 0,
"baseline": 0
},
{
"delta": 0,
"column": "amount",
"current": 0,
"baseline": 0
}
],
"duplicateRateChange": {
"delta": 0,
"current": 0,
"baseline": 0
},
"inferredTypeChanges": [
{
"column": "amount",
"current": "string",
"baseline": "integer"
}
],
"malformedRowRateChange": {
"delta": 0,
"current": 0,
"baseline": 0
}
},
"overallResult": "FAIL",
"currentProfile": {
"columns": [
{
"name": "id",
"position": 0,
"missingRate": 0,
"uniqueCount": 2,
"inferredType": "integer",
"missingCount": 0,
"sampleValues": [
"1",
"2"
]
},
{
"name": "name",
"position": 1,
"missingRate": 0,
"uniqueCount": 2,
"inferredType": "string",
"missingCount": 0,
"sampleValues": [
"Ada",
"Lin"
]
},
{
"name": "amount",
"position": 2,
"missingRate": 0,
"uniqueCount": 2,
"inferredType": "string",
"missingCount": 0,
"sampleValues": [
"10",
"unknown"
]
}
],
"headers": [
"id",
"name",
"amount"
],
"rowCount": 2,
"columnCount": 3,
"blankRowCount": 0,
"duplicateRowRate": 0,
"malformedRowRate": 0,
"duplicateRowCount": 0,
"malformedRowCount": 0
},
"recommendation": "BLOCK_PIPELINE",
"baselineProfile": {
"columns": [
{
"name": "id",
"position": 0,
"missingRate": 0,
"uniqueCount": 2,
"inferredType": "integer",
"missingCount": 0,
"sampleValues": [
"1",
"2"
]
},
{
"name": "name",
"position": 1,
"missingRate": 0,
"uniqueCount": 2,
"inferredType": "string",
"missingCount": 0,
"sampleValues": [
"Ada",
"Lin"
]
},
{
"name": "amount",
"position": 2,
"missingRate": 0,
"uniqueCount": 2,
"inferredType": "integer",
"missingCount": 0,
"sampleValues": [
"10",
"20"
]
}
],
"headers": [
"id",
"name",
"amount"
],
"rowCount": 2,
"columnCount": 3,
"blankRowCount": 0,
"duplicateRowRate": 0,
"malformedRowRate": 0,
"duplicateRowCount": 0,
"malformedRowCount": 0
},
"breakingChanges": [
{
"code": "INFERRED_TYPE_CHANGE",
"action": "BLOCK_PIPELINE",
"column": "amount",
"message": "Column amount changed inferred type from integer to string.",
"category": "TYPE",
"evidence": {
"currentType": "string",
"baselineType": "integer"
},
"severity": "HIGH"
},
{
"code": "SCHEMA_CURRENT_TYPE_MISMATCH",
"action": "BLOCK_PIPELINE",
"column": "amount",
"message": "current column amount does not conform to type number.",
"category": "SCHEMA",
"evidence": {
"expectedType": "number",
"observedType": "string"
},
"severity": "HIGH"
}
],
"schemaConformance": {
"current": "FAIL",
"baseline": "PASS",
"supplied": true,
"violations": 1
},
"processingLatencyMs": 0,
"analysisCompleteness": "FULL"
}
}Tags
x402
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