# Humidity Observations — Weighted Mean + Keyed Dataset Comparison > Humidity Observations — Weighted Mean + 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 2026-10-23). Computes a secondary-measure weighted mean and performs a keyed dataset comparison on up to 200 rows of supplied humidity observation records. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-04-186?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-23 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/humidity-observations-weighted-mean-keyed-dataset-comparison-b4ea094f - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_am7fwbqpB_-gCSU7t3HPc 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 humidity-observations-weighted-mean-keyed-dataset-comparison-b4ea094f -d '' ``` Example prompt: I have two batches of humidity readings from our weather stations — the main dataset has rows with station group, date, humidity_percent, and observation_count (up to 200 rows), and I have a second comparison dataset with the same structure. Can you compute the observation-count-weighted mean of humidity_percent across the primary dataset and also run a keyed comparison between the two datasets? ## When to prefer this Choose this endpoint when you have pre-collected humidity observation data (up to 200 rows) and need both a weighted mean statistic and a comparative analysis between two datasets in a single paid call. It is ideal for offline batch analytical tasks on weather station humidity records where no live data fetch, prediction, or external API call is needed. Prefer this over general-purpose analytics tools when working specifically with humidity_percent and observation_count fields in the weather/environmental monitoring domain and when cost-efficiency (two reports for $0.01 USDC) matters. ## Known failure modes - More than 200 rows submitted — request will be rejected or truncated - Missing required fields (row_id, station_group, date, humidity_percent, observation_count) cause validation errors - Date field not matching YYYY-MM-DD pattern causes schema validation failure - Empty rows array (minItems: 1 violated) returns an error - Null values in both humidity_percent and observation_count may produce undefined weighted mean - Mismatched row_ids between primary and secondary datasets result in incomplete comparison - Payment not included or insufficient USDC causes 402 rejection ## How this service works Humidity observations — Secondary-measure weighted mean + Keyed dataset comparison: Secondary-measure weighted mean + Keyed dataset comparison for supplied humidity observations records (humidity_percent, observation_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows. ## Output Returns two analytical reports derived from the supplied humidity observation data: (1) a secondary-measure weighted mean, where humidity_percent or observation_count is averaged using the other measure as weights across the provided rows; and (2) a keyed dataset comparison that matches records from the primary and secondary datasets by their identifiers and highlights differences. All processing is performed locally with no external lookups or predictions. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "station_group", "date", "humidity_percent", "observation_count" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "station_group": { "type": "string", "default": "", "maxLength": 80 }, "humidity_percent": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "observation_count": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "station_group": "group-a", "humidity_percent": 10, "observation_count": 1 }, { "date": "2026-10-02", "row_id": "row-2", "station_group": "group-b", "humidity_percent": 20, "observation_count": 2 }, { "date": "2026-10-03", "row_id": "row-3", "station_group": "group-a", "humidity_percent": 15, "observation_count": 3 }, { "date": "2026-10-04", "row_id": "row-4", "station_group": "group-b", "humidity_percent": 30, "observation_count": 4 } ], "maxItems": 200, "minItems": 1 }, "field": { "enum": [ "humidity_percent", "observation_count" ], "type": "string", "default": "humidity_percent" }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "station_group", "date", "humidity_percent", "observation_count" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "station_group": { "type": "string", "default": "", "maxLength": 80 }, "humidity_percent": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "observa … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/humidity-observations-weighted-mean-keyed-dataset-comparison-b4ea094f/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)