Commercial Skills

# 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: 1. Highest-confidence lane first → run sub-skill in forked context → digest 2. Ask: "Now run [second lane]? Recommended: yes, because [dependency]." 3. 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 1. User has commercial authority OR is preparing analysis for someone who does. 2. User wants **deterministic decision support**, not the final answer — the human approves the deal, sets the price, signs the partner. 3. 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`