# Partnership Pipeline — Category Frequencies + Keyed Dataset Comparison > Partnership Pipeline — 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 2026-10-15). Analyzes partnership pipeline data (qualified_partners, active_partners) by computing category frequency distributions and performing a keyed row-level comparison between two supplied datasets, returning two independent analytical reports. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-09-111?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-15 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/partnership-pipeline-category-frequencies-keyed-dataset-comparison-ef6329d9 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ql33JlkDUa9IulaXb8E7w 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 partnership-pipeline-category-frequencies-keyed-dataset-comparison-ef6329d9 -d '' ``` Example prompt: I have two batches of partnership pipeline records — a current set and a previous set, each with up to 200 rows containing row_id, company_group, date, qualified_partners, and active_partners — can you run category frequency analysis and a keyed row-by-row comparison between them to see what changed? ## When to prefer this Choose this endpoint when you need two analytical reports on partnership pipeline data in a single $0.01 call: a category frequency breakdown by company_group AND a keyed row-level comparison between two dataset snapshots. It is ideal for auditing pipeline refreshes, detecting partner record drift between time periods, or summarizing group distributions — all without external lookups, predictions, or live data access. Prefer this over generic analytics tools when your data is structured around qualified_partners and active_partners fields and you want both reports computed together efficiently. ## Known failure modes - Input array contains more than 200 rows — request rejected - Missing required fields (row_id, company_group, date, qualified_partners, active_partners) on any row — validation error - Date field does not match YYYY-MM-DD pattern — validation error - row_id exceeds 80 characters or is empty — validation error - numeric fields outside allowed range (±1,000,000,000,000) — validation error - Payment not completed via x402 protocol — request blocked - Empty rows array (minItems:1 violated) — validation error ## How this service works Partnership pipeline — Category frequencies + Keyed dataset comparison: Category frequencies + Keyed dataset comparison for supplied partnership pipeline records (qualified_partners, active_partners). 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 frequencies report showing how often each company_group value appears in the supplied rows, and (2) a keyed dataset comparison report highlighting row-level differences between the primary dataset and the comparison dataset (matched by row_id), covering qualified_partners and active_partners fields. No external lookups or predictions are made — all analysis is performed locally on the supplied data. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "company_group", "date", "qualified_partners", "active_partners" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "company_group": { "type": "string", "default": "", "maxLength": 80 }, "active_partners": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "qualified_partners": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "company_group": "group-a", "active_partners": 1, "qualified_partners": 10 }, { "date": "2026-10-02", "row_id": "row-2", "company_group": "group-b", "active_partners": 2, "qualified_partners": 20 }, { "date": "2026-10-03", "row_id": "row-3", "company_group": "group-a", "active_partners": 3, "qualified_partners": 15 }, { "date": "2026-10-04", "row_id": "row-4", "company_group": "group-b", "active_partners": 4, "qualified_partners": 30 } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "company_group", "date", "qualified_partners", "active_partners" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "company_group": { "type": "string", "default": "", "maxLength": 80 }, "active_partners": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "qualified_partners": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/partnership-pipeline-category-frequencies-keyed-dataset-comparison-ef6329d9/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)