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Channel Economics

Models the true financial structure of go-to-market channels — direct, reseller, marketplace, OEM — and designs partner tiers, rebates and channel-conflict rules.

Data & AnalyticsIntermediate★ 1,083⑂ 205AI score 9/10Last updated: Nov 8, 2026

What it does

  • Picks a channel mix via a decision tree keyed to product complexity and ACV: self-serve, direct, reseller/VAR, distributor, marketplace, OEM/embedded, MSP
  • Compares channels on fully-loaded TCO (COGS, partner discount, allocated sales/marketing/enablement/channel-ops/support) instead of headline discount percentages
  • Designs multi-tier partner programs — revenue thresholds, discount and rebate percentages, benefits, certification gates — plus per-tier break-even math
  • Recommends a front-end discount / back-end rebate / MDF-SPIFF mix by partner type
  • Supplies conflict-resolution playbooks for direct-vs-partner, partner-vs-partner and marketplace-vs-direct situations, including deal registration rules
  • Ships three stdlib-only CLI scripts: channel_margin_calculator.py, partner_tier_economics.py, channel_mix_optimizer.py

Who it's for

  • CROs and channel leads standing up a first partner program at a B2B/SaaS company
  • BD teams weighing an AWS/Azure Marketplace listing against direct list-price economics
  • Finance and sales-ops folks who must defend reseller discounts, rebate pools and MDF budgets with numbers

Example uses

  1. "Direct or partner-led for this new product?" — narrows options with the decision tree, then builds a side-by-side net-contribution table for a $100k ACV deal across direct, reseller and marketplace
  2. "Design silver/gold/platinum tiers" — sets revenue gates, discount plus rebate structure, certification and renewal requirements, and models partner break-even per tier
  3. "A direct rep and a partner are chasing the same account" — applies the first-registered rule, weighs exceptions, drafts the written decision and a consolation package for the losing side

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/borghei/Claude-Skills/HEAD/business-growth/channel-economics/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on AI Skill Mart.
Copy the business-growth/channel-economics folder from the GitHub repo borghei/Claude-Skills into my ~/.claude/skills/channel-economics/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/borghei/Claude-Skills.git && mkdir -p ~/.claude/skills && cp -r Claude-Skills/business-growth/channel-economics ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repository: git clone https://github.com/borghei/Claude-Skills.git
  3. Create the skills directory if needed: mkdir -p ~/.claude/skills
  4. Copy just this skill: cp -r Claude-Skills/business-growth/channel-economics ~/.claude/skills/
  5. Restart Claude Code and try a prompt like "Compare reseller vs direct contribution margin for a $150k ACV deal."
  6. Python 3 is required for the scripts, and you must prepare your own deal.yaml, tiers.yaml and revenue.csv inputs — nothing connects to a CRM or partner portal.
  7. Replace the illustrative discounts, marketplace fees and tier thresholds with your actual contract terms and cost-allocation rules before presenting results.
View source on GitHub ↗License: NOASSERTION