commercial-skills – skill Marketing & SEO cho agent AI

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