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Project Status Label and Prediction Audit
x402
Project Status Label and Prediction Audit is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Audits classification labels and model predictions for project status (planned/active/blocked/complete) across up to 200 labelled text records, returning evaluation metrics.
Price / call$0.01
Success—
StatusUnknown
How this service works
Project status - Label and prediction audit: Label and prediction audit for Project status. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded label and prediction audit deliverable.
Output
Returns a bounded label and prediction audit deliverable covering prediction quality metrics (e.g. accuracy, per-class breakdown, probability calibration scores) for up to 200 project status records with classes planned, active, blocked, and complete.
Example use cases
Sprint classifier accuracy review
“I trained a classifier to label Jira tickets as planned, active, blocked, or complete. Here are 120 records with my model's predicted labels and probability scores alongside the ground-truth labels — can you run a label and prediction audit so I see where it's making mistakes?”
Blocked project detection model audit
“My model keeps misidentifying blocked projects as active. I have 80 labelled records with actual statuses, predicted statuses, and four-element probability arrays — can you audit the predictions and show me the breakdown of errors?”
Pre-deployment classifier sanity check
“Before I ship this project status classifier, I want a full prediction audit on my 200-record test set. Each record has an id, a text description, the actual status (planned/active/blocked/complete), the predicted status, and four class probabilities — can you evaluate how it's performing?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-project-status-label-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, bounded audit of project status classifier predictions without access to a hosted model or foundation model — ideal for validating naive Bayes or similar small classifiers on local labelled datasets of up to 200 records using the four-class project status schema (planned/active/blocked/complete).
Side effects
- Records array exceeds 200 items — rejected or truncated
- Probabilities array does not have exactly 4 elements per record — validation error
- Label values outside the enum (planned/active/blocked/complete) — schema rejection
- Empty records array — no audit produced
- Malformed probability values outside [0,1] — validation error
- Payment of 0.01 USDC not received — 402 response blocking execution
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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