/commercial-policy

name: commercial-policy
description: "Use when writing or revising discount rules: discount matrix, approver thresholds, exception flows and the deal framework sales works under. Builds the matrix with discount_matrix_builder.py, routes exceptions with exception_router.py and checks the policy with policy_linter.py."
license: MIT
metadata:
  version: 2.8.0
  author: claude-code-skills
  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:

1. `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). 2. `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. 3. `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

1. **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`, `discount_pct`, `term_months`, `payment_terms_days`, `strategic_value`, `win_lost`, `nrr_12mo` per deal.

2. **Design the data-backed matrix.** Run `scripts/discount_matrix_builder.py --input policy_intake.json --profile {saas|enterprise-software|api|marketplace|services}`. Output is a 4-dimensional matrix with approved discount band + approver tier + margin floor + observed win-rate + observed NRR per cell. Cells with `n < 5` observed deals are flagged `THIN`.

3. **Design the exception flow.** Run `scripts/exception_router.py --sample` to see the structure. For each severity band of exception (0-5 pts over, 5-10, 10-20, 20+), the router enforces required compensating commitments. Codify the flow in your policy doc; the router becomes the operational implementation.

4. **Lint the matrix.** Run `scripts/policy_linter.py --input matrix.json`. Get a ranked findings report — BLOCKER / MAJOR / MINOR — across 10 lint rules. Resolve every BLOCKER before publishing the matrix to AEs.

5. **Publish + quarterly review.** Publish the matrix as a versioned artifact. Re-run the builder and the linter every quarter against the new 4-quarter rolling deal corpus. Cells where observed NRR < `target_nrr` are flagged for review.

Scripts

| 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) |

All three: stdlib-only, `--help`, `--sample`, `--input <json>`, `--output {markdown,json}`.

References

• `references/discount_governance_canon.md` — Discount governance evidence base: OpenView Partners benchmarks, David Skok (For Entrepreneurs) discount math, Tomasz Tunguz on discount distribution, Bessemer State of the Cloud, KeyBanc Capital Markets SaaS Survey, Bridge Group AE-compensation research, RevOps Co-op playbooks, Forrester deal-desk research. 8 sources.
• `references/policy_design_canon.md` — Policy-as-artifact design: SaaStr (Jason Lemkin), Winning by Design (Jacco van der Kooij) on commercial discipline, Forrester deal-desk maturity research, MIT Sloan on incentive-system gaming, McKinsey on commercial-policy effectiveness, Bain *Pricing Power*, Salesforce CPQ implementation guides. 7 sources.
• `references/policy_anti_patterns.md` — 8 named anti-patterns with sourced studies + countermeasures + lint-rule mapping: precedent-sets-policy, no-data-backing, no-compensating-commitments, approver/margin misalignment, no audit trail, cliff edges, undefined "strategic value", no quarterly review. 8 sources.

Assumptions

• The skill assumes the **pricing model and list price already exist** (set via `commercial/skills/pricing-strategist`). Commercial-policy governs **discounts off list** — it does