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Anansi Data Anomaly Detection
x402
Anansi Data Anomaly Detection is a paid API for AI agents from anansidata.xyz, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Detects statistical outliers by computing z-scores on the latest move of tracked keys against their own historical baseline.
Price / call$0.05
Success—
StatusUnknown
How this service works
Statistical outliers: keys whose latest move breaks their own historical baseline (z-score). Manufactured from accumulation.
Output
Returns a list of tracked keys whose latest value change constitutes a statistical outlier relative to that key's own historical baseline, along with z-scores quantifying the deviation severity.
Example use cases
Crypto market anomaly alert
“Check the crypto price dataset and tell me which tokens are showing statistically unusual moves today — anything whose latest price change is way outside its own historical norm by z-score.”
Monitoring data quality outliers
“Scan the 'user_metrics' dataset and flag any keys whose latest values are statistical outliers compared to their own historical baseline — I want z-scores so I can prioritize which ones to investigate first.”
Unusual API pricing movements
“Look at the LLM pricing dataset and tell me which model prices have moved in a way that's statistically anomalous against their own history — I want to catch anything that's an outlier before it affects our cost estimates.”
Technical details
Endpoint
GEThttps://anansidata.xyz/paid/intel/anomaliesWhen to prefer
Use this endpoint when you need to surface which specific data points have moved unusually relative to their own history — not just which are highest or lowest in absolute terms. Ideal when monitoring many keys and wanting automated statistical triage rather than manual threshold-setting. Prefer over velocity ranking when you want self-normalized deviation, not raw speed.
Side effects
- Insufficient historical data for a key yields no z-score or unreliable baseline
- Invalid dataset identifier returns an error
- Keys with very low variance may produce inflated z-scores
- Network timeout for large datasets
- Payment failure via x402 protocol blocks the request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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