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Token Rule-Flag Fraction — Sequential Input-Change Attribution
x402
Token Rule-Flag Fraction — Sequential Input-Change Attribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-10).
Computes the sequential (waterfall) attribution of changes in a token's rule-flag fraction by comparing before/after flagged-rule and evaluated-rule counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Token supplied rule-flag fraction — Sequential input-change attribution: Sequential input-change attribution for Token supplied rule-flag fraction. Supplied token observations only. Deterministic calculation or caller-threshold verdict, not a live scan, safety proof or trade recommendation. Specialised preset in a shared 50-profile, 20-operation engine. 0.01 USDC includes one bounded sequential input-change attribution report.
Output
A deterministic sequential (waterfall) attribution report showing how changes in the before/after flagged_rules and evaluated_rules inputs each contributed to the overall change in the token rule-flag fraction. Not a live scan or safety proof — results are based purely on the supplied numbers.
Example use cases
Compliance rule-flag ratio drift check
“My token monitoring system just updated: we went from 2 flagged rules out of 10 evaluated to 3 flagged rules out of 12 evaluated. Can you run the sequential attribution to show me how much of that flag-fraction change came from the flagged-rule count vs the evaluated-rule count?”
Token policy review after rule tuning
“After tightening our rules, the flagged rule count dropped from 5 to 3 while evaluated rules went from 20 to 20. Do a waterfall attribution on the change in the rule-flag fraction so I understand what drove the shift.”
Pre/post audit flag share comparison
“I need a sequential input-change attribution report for a token's flag share — before the audit we had 4 flagged out of 15 evaluated rules, and after the audit we have 6 flagged out of 18 evaluated. Break down the attribution step by step.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-bx-token-flag-share-waterfall?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a deterministic, auditable waterfall decomposition of exactly how much each input change (flagged_rules vs evaluated_rules) contributed to a shift in a token's rule-flag fraction between two states. Prefer it for compliance audits, rule-tuning analyses, or any scenario where you need a structured before/after attribution rather than a raw ratio difference.
Side effects
- evaluated_rules set to 0 (excluded by exclusiveMinimum constraint) causes validation error
- flagged_rules or evaluated_rules out of allowed numeric range
- missing required before or after fields returns schema validation error
- network or payment authorization failure for the $0.01 USDC x402 charge
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | object | |
| before | object |
Tags
x402
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