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Issue Kind Labelled JSONL Export
x402
Issue Kind Labelled JSONL Export is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Exports up to 200 labelled issue-classification records (bug/request/question/other) as a JSONL dataset for local ML evaluation and naive-Bayes baselines.
Price / call$0.01
Success—
StatusUnknown
How this service works
Issue kind - Labelled JSONL export: Labelled JSONL export for Issue kind. 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 labelled jsonl export deliverable.
Output
A bounded JSONL export containing up to 200 labelled text records, each with an ID, the issue text, and its actual category label (bug, request, question, or other), suitable for local dataset tooling, evaluation pipelines, and small naive-Bayes baselines.
Example use cases
Issue classifier evaluation dataset
“I have 120 labelled support tickets tagged as bug, request, question, or other — can you package them into a JSONL export so I can run a local naive-Bayes evaluation?”
Baseline model training data prep
“Give me a JSONL export of these 80 labelled GitHub issues — they're each marked bug, request, question, or other — so I can use them as a small training set for a text classifier.”
Team audit of issue label quality
“Export these 40 issue records with their actual labels (bug, request, question, other) into a JSONL file so I can review how consistently my team has been tagging issues.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-issue-kind-jsonl-export?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a cheap, bounded labelled JSONL export of issue-kind records (bug/request/question/other) for local ML evaluation or naive-Bayes baselines — not for hosted model inference, large-scale training data generation, or foundation-model fine-tuning.
Side effects
- Fewer than 2 records supplied (minItems violation)
- More than 200 records supplied (maxItems exceeded)
- Invalid label value outside enum [bug, request, question, other]
- Record text exceeds 1000 character limit
- Missing required fields (id, text, actual) on any record
- Payment not received or insufficient USDC balance
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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