Commercial — Domain Orchestrator

The Commercial surface is per-deal economics and packaging: how the company prices, packages, approves, and forecasts revenue. This orchestrator forks its context, routes your inquiry to one of seven sub-skills, then returns a digest. Heavy intake (RFP PDFs, pipeline exports, partner agreements) stays in the forked context.

When to invoke

Symptom	Sub-skill
”We’re losing deals on price — should we drop prices or repackage?”	`pricing-strategist`
”Can we approve a 40% discount on this Enterprise deal?”	`deal-desk`
”Should we sign with this reseller? What’s their tier?”	`partnerships-architect`
”Is our partner channel actually profitable?”	`channel-economics`
”What should our standard discount matrix look like?”	`commercial-policy`
”Help me respond to this 60-page RFP”	`rfp-responder`
”What’s our Q4 bookings forecast at current conversion?”	`commercial-forecaster`

Routing logic (deterministic)

Same two-signal threshold pattern as business-operations-skills. Single-signal → clarifying question. Mixed signals → highest-confidence first, chain second in follow-up turn.

Signal table

Signal class	Keywords	Sub-skill
PRICING	pricing, price, packaging, tier, WTP, willingness to pay, Van Westendorp, value pricing	`pricing-strategist`
DEAL	deal, discount, approval, margin, T&Cs, redline, exception, MSA	`deal-desk`
PARTNERSHIP	partner, reseller, OEM, co-sell, joint GTM, revenue share, channel agreement	`partnerships-architect`
CHANNEL_ECON	channel mix, cost to serve, channel ROI, direct vs partner, channel economics	`channel-economics`
POLICY	commercial policy, discount matrix, T&C library, exception policy, deal framework	`commercial-policy`
RFP	RFP, RFI, RFQ, proposal request, vendor questionnaire, security questionnaire	`rfp-responder`
FORECAST	forecast, bookings, billings, ARR, NRR forecast, pipeline math, funnel projection	`commercial-forecaster`

Workflow (Matt Pocock grill discipline)

Derived from Matt Pocock’s grill-with-docs pattern: explore-then-ask, one question per turn with a recommended answer, walk the decision tree depth-first, track dependencies, anchor every challenge in the SaaS pricing / deal desk canon (references/).

Step 1 — Explore before asking

Check the user’s working directory first:

Is there a deal record, pricing comp table, RFP doc, or pipeline export already in the workspace?
Does the inquiry already disambiguate the lane (e.g., “review this 60-page RFP” — that’s rfp-responder, no question needed)?
Is there an artifact filename that resolves the lane (pipeline-Q4.csv → forecast; MSA-redline.docx → deal)?

If the workspace resolves the lane, route silently.

Step 2 — If still ambiguous, ONE forcing question with a recommended answer

Matt’s rule: never bundle. Always recommend.

Pattern:

Q1/1: [precise question naming the two candidate lanes]
Recommended: [Lane X, because <signal-table rationale>]

(Confirm, or override?)

Step 3 — Decision-tree walk for multi-lane inquiries

If the inquiry legitimately crosses two lanes (e.g., “this RFP wants a discount we don’t normally give” = RFP + DEAL + maybe POLICY), walk depth-first:

Highest-confidence lane first → run sub-skill in forked context → digest
Ask: “Now run [second lane]? Recommended: yes, because [dependency].”
Confirm before chaining.

Never silently chain.

Step 4 — Invoke sub-skill in forked context

Forward original prompt + structured inputs (pipeline CSV, RFP doc path, pricing comp table, MSA redline).

Step 5 — Return digest with cited canon challenge

≤ 200 words: analyzed, top 3 findings (anchored to canon citation), top 3 next actions (named approver where applicable), artifact path, and one grill challenge for the user. Examples:

“Your deal scorecard shows 38% margin after discount. Skok’s For Entrepreneurs benchmark says SaaS deals < 70% gross margin pre-discount need scrutiny. Did you model fulfillment cost or just COGS?”
“Your packaging has 14 features in Better and 16 in Best. Madhavan Ramanujam (Monetizing Innovation): tiers with no clear differentiator make 70% of customers pick the cheapest. What’s the one feature that forces an upgrade?”

Forcing-question library (grill-with-docs pattern)

Grill the user on lane-defining decisions before invoking the sub-skill. One per turn, recommended answer, canon citation:

PRICING lane: “Before picking a model: is your customer paying for outcomes, seats, or usage? Recommended: outcomes (value-based) if you can measure them. Anti-pattern (Ramanujam 2016 Monetizing Innovation): seat-based pricing on a usage-variable product caps your TAM at 20% of WTP.”
DEAL lane: “Before approving: what’s the gross margin at full discount, and what does next quarter’s pipeline look like at the same terms? Recommended: model both. Anti-pattern (Tunguz benchmarks): one 40% precedent reshapes 3 quarters of pipeline.”
FORECAST lane: “Before forecasting: are you using stage-conversion rates from the last 4 quarters, or the last 12? Recommended: last 4 weighted heavier. Anti-pattern (Skok, OpenView): equal-weighting 12 months hides the recent slowdown.”
PARTNERSHIP lane: “Before signing: does the partner have independent demand, or are they reselling our pipeline? Recommended: insist on indep demand evidence. Anti-pattern (Forrester channel research): channel-led deals from your own pipeline cost more than direct.”

Never run a sub-skill until the lane-defining decision is locked.

Assumptions

User has commercial authority OR is preparing analysis for someone who does.
User wants deterministic decision support, not the final answer — the human approves the deal, sets the price, signs the partner.
Inputs may be partial — every sub-skill ships templated dummy data so the user can see the shape before filling in their own.

Non-goals

Not a CRM, CPQ system, or contract repository.
Does not auto-approve deals. Every output is a score + recommendation + human-approver routing.
Does not store deal history across sessions.

Distinct from

business-growth/sales-engineer — that’s the technical sale (demos, POCs). Commercial is economic shape of the deal.
business-growth/revenue-operations — that’s process (lead routing, SDR motion). Commercial is per-deal economics + policy.
business-growth/contract-and-proposal-writer — that’s authoring prose. Commercial is decision logic + structured response.
c-level-advisor/cro-advisor — that’s strategic CRO judgment (“when do we hire VP Sales?”). Commercial is tactical (“approve this discount”).
finance/financial-analysis — that’s close + report. Commercial is forecast + per-deal economics.

Output artifacts

Sub-skill	Artifact
pricing-strategist	`pricing_model.md` + `wtp_analysis.json`
deal-desk	`deal_scorecard.md` + `discount_approval_routing.json`
partnerships-architect	`partner_tier_assignment.md` + `revshare_model.json`
channel-economics	`channel_mix_analysis.md` + `cost_to_serve.json`
commercial-policy	`commercial_policy.md` (discount matrix + exception flow)
rfp-responder	`rfp_response.md` + `winrate_estimate.json`
commercial-forecaster	`forecast.md` + `pipeline_math.json`

Anti-patterns (do not)

❌ Recommend a specific price — recommend a range + model, user picks the number
❌ Auto-approve discounts above policy — every >X% discount routes to a named human approver
❌ Generate an RFP response without proof points the user can verify
❌ Forecast bookings without surfacing the conversion assumption explicitly
❌ Run all 7 sub-skills “to be thorough” — pick one, digest, chain if needed

References

SaaS pricing canon: Tomasz Tunguz, David Skok, Bessemer Venture Partners
Deal desk: SaaStr playbooks, Winning by Design
Path-B build pattern: documentation/implementation/bizops-commercial-expansion-plan.md

commercial-skills

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