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