commercial-policy

Use when designing or revising a company's commercial policy — the rules of engagement governing discounts off list price, approver thresholds, exception flows, and the deal framework that Deal Desk and AEs operate under. Covers discount matrix design (ARR band x term length x payment terms x strategic value), commercial policy design, exception policy, discount governance, approval thresholds, deal framework structure, and policy linting (contradictions, gaps, cliff edges, gaming surfaces). For Head of Commercial, Head of Deal Desk, VP Sales, or RevOps at the policy-design moment — NOT per-deal application (that is deal-desk) and NOT pricing model selection (that is pricing-strategist).

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name	commercial-policy
description	Use when designing or revising a company's commercial policy — the rules of engagement governing discounts off list price, approver thresholds, exception flows, and the deal framework that Deal Desk and AEs operate under. Covers discount matrix design (ARR band x term length x payment terms x strategic value), commercial policy design, exception policy, discount governance, approval thresholds, deal framework structure, and policy linting (contradictions, gaps, cliff edges, gaming surfaces). For Head of Commercial, Head of Deal Desk, VP Sales, or RevOps at the policy-design moment — NOT per-deal application (that is deal-desk) and NOT pricing model selection (that is pricing-strategist).
version	2.8.0
author	claude-code-skills
license	MIT
tags	["commercial","discount-policy","discount-matrix","exception-flow","governance","deal-framework","commercial-discipline"]
compatible_tools	["claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli"]

commercial-policy

Purpose

Design the rules of engagement that govern discounting off list price — the artifact that Deal Desk and AEs operate under. Three deterministic tools:

discount_matrix_builder.py — builds a 4-dimensional matrix (ARR band × term length × payment terms × strategic value tier), each cell carrying an approved discount band backed by current win-rate + NRR data, plus an approver tier (AE / Manager / Director / VP / CFO).
exception_router.py — when an asks-for-discount lands outside the matrix, routes it through the named approver chain, attaches required compensating commitments (multi-year prepay + named expansion path + reference commitment + MSA tightening), produces machine-readable audit-trail metadata, and flags precedent risk if 3+ similar exceptions have landed in the trailing quarter.
policy_linter.py — lints the matrix for governance defects: approver inversion, band inversion, margin-floor violation, coverage gaps, cliff edges, undefined strategic tiers, inconsistent margin floors, thin data backing.

The output is the policy itself (matrix + exception flow + lint report), not a per-deal application of it.

When to use

A new Head of Commercial or Head of Deal Desk is writing the company's first formal commercial policy
The existing matrix is older than 6 months and discount drift is showing in margin reviews
Reps are citing "Maria approved 28% on Acme last quarter" as precedent and you need to break the precedent loop
Q-over-Q exception count is rising and you suspect the matrix bands are mispriced
CFO has tightened the margin floor and the matrix needs to be rebuilt against the new constraint
A board / exec is asking "why do we discount this much?" and you need a data-backed defensible policy

Do NOT use this skill to:

Approve a specific deal — that's commercial/skills/deal-desk
Set the pricing model + list price — that's commercial/skills/pricing-strategist
Author a proposal / SOW / MSA prose — that's business-growth/contract-and-proposal-writer
Make the strategic "when do we hire a VP Sales" call — that's c-level-advisor/cro-advisor

Workflow

Audit current discount distribution. Pull the last 4 quarters of closed-won + closed-lost deals from CRM. Fill assets/policy_design_template.md (~20 minutes). Capture: arr, , , , , , per deal.

discount_pct

term_months

payment_terms_days

strategic_value

win_lost

nrr_12mo

Script	Purpose	Industry profiles
`scripts/discount_matrix_builder.py`	4-dim data-backed matrix with approver tiers + margin floors	saas, enterprise-software, api, marketplace, services
`scripts/exception_router.py`	Routes exception requests with compensating commitments + audit trail	n/a (matrix-driven)
`scripts/policy_linter.py`	10-rule lint pass over the matrix	n/a (deterministic across profiles)

Sibling	Scope	Difference
`commercial/skills/deal-desk`	Applies the policy to one deal at a time	Commercial-policy designs the policy itself. Deal-desk consumes the matrix; commercial-policy produces it.
`commercial/skills/pricing-strategist`	Sets pricing model (per-seat / usage / value / tiered) + list price	Commercial-policy governs discounts off list. Pricing-strategist sets the menu; commercial-policy governs the menu's discount discipline.
`c-level-advisor/cro-advisor`	Strategic CRO judgment ("when do we hire VP Sales?", "is our motion product-led or sales-led?")	Strategic, not operational. Commercial-policy is the artifact CRO commissions; it isn't CRO judgment itself.
`c-level-advisor/cfo-advisor`	Margin floor + unit-economics judgment	The CFO supplies `min_margin_pct` to commercial-policy as an input. Commercial-policy operationalizes the CFO's constraint as per-cell margin floors.
`business-growth/contract-and-proposal-writer`	Authors proposal/SOW/MSA prose	Commercial-policy emits structured matrix + audit-trail JSON, not customer-facing prose.

"What's your observed discount distribution across the last 4 quarters — and is the median inside or outside your current matrix?" Recommended: pull the corpus before designing any band. If the observed median is outside the matrix, the matrix is rhetoric. Canon: OpenView SaaS Benchmarks; RevOps Co-op playbooks. Anti-pattern AP-2.
"What's the win-rate AND the 12-month NRR for deals at your current 'max discount' band?" Recommended: both, not one. A band with high win-rate but low NRR is buying logos with leaky-bucket retention. Tunguz benchmarks: top-NRR-quartile companies discount 6 pts less than bottom quartile. Canon: Tomasz Tunguz; Bessemer State of the Cloud.
"Who at the company owns the margin floor, AND who owns the discount-band cap — are those the same person?" Recommended: CFO owns floor; CRO/Head of Deal Desk owns cap. Same owner = drift toward what they're compensated on. Canon: Bain Pricing Power — separation of accountability is the structural fix. Anti-pattern AP-4.
"How is 'strategic value' defined in your current policy — with concrete tests, or with adjectives?" Recommended: concrete tests. "Top-20 named account in 2026 target list" is a test; "important customer" is not. Canon: SaaStr (Lemkin); Forrester deal-desk research. Lint rule L06. Anti-pattern AP-7.
"For exceptions above your matrix max, what compensating commitments are required — and are they in writing before the approver signs?" Recommended: minimum multi-year prepay + named expansion path; deeper exceptions require reference commitment + MSA tightening + executive sponsor. Canon: Winning by Design (van der Kooij); McKinsey B2B pricing studies. Anti-pattern AP-3.
"Has the same kind of exception been approved 3+ times in the trailing quarter — and if so, is the matrix wrong?" Recommended: 3+ similar exceptions means the band is mispriced. Rebuild the matrix; don't keep approving exceptions. Canon: OpenView discount drift studies; exception_router._precedent_risk. Anti-pattern AP-1.
"When was the last time you re-ran the matrix against the previous 4 quarters of data?" Recommended: quarterly. Annual review is too slow; the disciplined cohort revises quarterly. Canon: OpenView benchmarks; RevOps Co-op. Anti-pattern AP-8.
"For every exception in the last quarter, is there a machine-readable audit-trail record — or is the approval in Slack and email?" Recommended: structured record in CPQ or equivalent. Slack/email approvals don't survive year-2 renewal negotiations. Canon: Salesforce CPQ best practices; Forrester deal-desk maturity research. Anti-pattern AP-5.

# Design the matrix
python3 scripts/discount_matrix_builder.py --sample
python3 scripts/discount_matrix_builder.py --input policy_intake.json --profile saas --output json > matrix.json

# Lint the matrix
python3 scripts/policy_linter.py --sample
python3 scripts/policy_linter.py --input matrix.json

# Walk the exception flow
python3 scripts/exception_router.py --sample
python3 scripts/exception_router.py --input request.json --output json