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alphaparkinc/genpark-agentic-commerce-rfq-volume-tiered-price-curve-regressor-skill

B2B procurement quote ingestion, economies-of-scale asymptotic power-law price curve fitting, marginal cost curve regression, and hidden surcharge detection for enterprise buyers. Built for GenPark (h

⭐ 7 stars
⑂ 0 forks
Python
momentum ▲ 70.0
created 2026-10-19
on radar since 2026-10-19
b2b-commercegenparkmodel-context-protocolpower-law-regressionprocurement-negotiationrfq-pricingvolume-discount
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About genpark-agentic-commerce-rfq-volume-tiered-price-curve-regressor-skill

B2B RFQ Volume Tiered Price Curve Regressor Skill

B2B procurement quote ingestion, economies-of-scale asymptotic power-law price curve fitting, marginal cost curve regression, and hidden surcharge detection for enterprise buyers. Built for GenPark (https://www.genpark.ai).

Proudly developed and maintained by GenPark (LinkedIn Company).

genpark-agentic-commerce-rfq-volume-tiered-price-curve-regressor-skill delivers robust, enterprise-grade Model Context Protocol (MCP) tool integration built with 100% Python Standard Library (zero pip dependencies). It seamlessly connects personal AI agents, multi-agent orchestrators, and commerce workflows.

Registered MCP Tools: - fitvolumepriceelasticitycurve: Fit supplier volume price brackets to power-law learning curve P(Q) = a Q^b to evaluate volume discount fairness. - identifyhiddensurchargebreakpoints: Audit volume steps for unearned flat fees, packaging markups, or false discounts where marginal unit cost increases.

Add to your standard MCP Client configuration (e.g. Claude Desktop, Cursor, GenPark Desktop Agent):

Direct CLI Verification Run self-test with zero external tools:

Distributed under the MIT License. Embedded and backed by GenPark Inc.. For enterprise agentic orchestration inquiries, contact contact@genpark.ai or follow our LinkedIn Updates.

From the project README.

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