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Release Notes Number-Token Inventory
x402
Release Notes Number-Token Inventory is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).
Counts and inventories number tokens (numeric values, dates, quantities) found in release notes Markdown or plain text documents up to 20,000 characters.
Price / call$0.01
Success—
StatusUnknown
How this service works
Release notes - Number-token inventory: Number-token inventory for Release notes. Supplied Markdown or plain text, up to 20,000 characters. Deterministic processing and document templates; no generative prose or fact checking. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded number-token inventory deliverable.
Output
A structured inventory of all number tokens found in the supplied release notes text, including numeric values, dates, version numbers, quantities, and any other number-like tokens. Output is deterministic and derived directly from the input document with no generative content.
Example use cases
Audit dates in a release changelog
“I need to inventory all the dates and numeric tokens in this release notes Markdown — can you scan it and give me a full list of every number or date that appears in the document?”
Validate numeric consistency in docs
“Before we publish these release notes, can you extract every number token so I can double-check all the version numbers, dates, and quantities are correct?”
Review TODO dates in release notes
“We have a draft release notes file with several TODO items referencing deadlines like 2026-10-10 — can you run a number-token inventory so I can see all the dates and numbers scattered through the document?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-writing-release-notes-number-inventory?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a deterministic, reproducible inventory of all numeric tokens in release notes or changelog documents. It is ideal for auditing version numbers, dates, and quantities in software documentation without any generative AI interpretation. Prefer this over general NLP or LLM-based extraction when consistency and determinism are required.
Side effects
- Input text exceeds 20,000 character limit — request will be rejected or truncated
- Empty or whitespace-only input returns an empty inventory
- Non-release-notes content may produce unexpected token classifications
- Binary or heavily encoded input may not parse correctly
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string |
Tags
x402
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