# Texture Baking Analytics — Category Frequencies + Keyed Dataset Comparison > Texture Baking Analytics — Category Frequencies + Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-27). Computes category frequency distributions and performs keyed row-by-row dataset comparison for texture baking records (bake_seconds, texture_pixels) across up to 200 rows. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-creative-10-111?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-27 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/texture-baking-analytics-category-frequencies-keyed-dataset-comparison-35ce7173 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_QFFwH3YXiyBI17NGKx9HY Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently. ## How to call it through Zero Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`): ```sh zero fetch --capability texture-baking-analytics-category-frequencies-keyed-dataset-comparison-35ce7173 -d '' ``` Example prompt: Can you run a category frequency analysis and a keyed comparison on my two texture baking datasets? The first has rows like {row_id: 'bake-001', asset_group: 'character', date: '2025-03-10', bake_seconds: 45, texture_pixels: 2048} and I want to compare it against a second dataset with the same row IDs to see where bake_seconds and texture_pixels differ. ## When to prefer this Choose this endpoint when you need both a category frequency breakdown and a keyed row-by-row comparison of texture baking data in a single low-cost call. It is purpose-built for the texture baking domain (bake_seconds, texture_pixels, asset_group) and delivers two reports for $0.01 USDC without any external API calls, predictions, or live data lookups. Prefer it over general analytics tools when working within 3D asset pipelines where you need fast, offline statistical summaries of bake job datasets. ## Known failure modes - Exceeding 200 rows returns a validation error - Missing required fields (row_id, asset_group, date, bake_seconds, texture_pixels) cause rejection - Mismatched row_id keys between 'rows' and 'other' may result in unmatched comparison entries - Invalid date format (not YYYY-MM-DD) triggers schema validation failure - Numeric values outside allowed range (-1e12 to 1e12) are rejected - Payment failure via x402 protocol prevents endpoint execution ## How this service works Texture baking — Category frequencies + Keyed dataset comparison: Category frequencies + Keyed dataset comparison for supplied texture baking records (bake_seconds, texture_pixels). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows. ## Output Returns two independent analytical reports: (1) a category frequency table showing how often each asset_group value appears in the supplied rows, and (2) a keyed dataset comparison report showing row-by-row differences between the 'rows' and 'other' datasets matched on row_id, highlighting changes in bake_seconds and texture_pixels. No external lookups or predictions are made; all analysis is local. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "asset_group", "date", "bake_seconds", "texture_pixels" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "asset_group": { "type": "string", "default": "", "maxLength": 80 }, "bake_seconds": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "texture_pixels": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "asset_group": "group-a", "bake_seconds": 10, "texture_pixels": 1 }, { "date": "2026-10-02", "row_id": "row-2", "asset_group": "group-b", "bake_seconds": 20, "texture_pixels": 2 }, { "date": "2026-10-03", "row_id": "row-3", "asset_group": "group-a", "bake_seconds": 15, "texture_pixels": 3 }, { "date": "2026-10-04", "row_id": "row-4", "asset_group": "group-b", "bake_seconds": 30, "texture_pixels": 4 } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "asset_group", "date", "bake_seconds", "texture_pixels" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "asset_group": { "type": "string", "default": "", "maxLength": 80 }, "bake_seconds": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "texture_pixels": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/texture-baking-analytics-category-frequencies-keyed-dataset-comparison-35ce7173/health.json - [Zero catalog index](https://www.zero.xyz/llms.txt) - [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)