commercial-skills

What this skill does

# 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