deal-desk

Per-deal review and discount-approval routing. Scores deal margin + risk, routes discount approval to the right human, redlines T&Cs against commercial policy. Never auto-approves. Every output is a score plus a routing recommendation to a named human approver.

Purpose

Deal Desk / RevOps / sales leadership live at the moment between sales-team-asks-for-discount and CFO/CRO/legal-signs. This skill quantifies the asks and routes them.

Three deterministic tools:

1. deal_scorer.py — Scores a deal 0-100 across 5 dimensions (margin, risk, strategic value, commercial fit, term shape) and assigns one of four verdicts: APPROVE / REVIEW / ESCALATE / DECLINE — each tied to a named approver chain.
2. discount_approval_router.py — Maps a discount-percent + deal-size + tier to a named approver chain (AE → Manager → Director → VP → CFO/CRO) with estimated cycle days. Honors industry-tuned policy bands.
3. terms_redliner.py — Detects 10 founder/seller-killer patterns in deal terms (uncapped indemnity, MFN, perpetual license-back, missing DPA, NET-60+, broad non-solicit, etc.) with severity + standard counter + named legal/commercial approver.

When to use

Invoke this skill when:

• Sales has flagged a discount request above AE authority.
• A customer has returned a redlined MSA and you need triage before routing to legal.
• The deal needs CFO sign-off and you want a defensible margin breakdown.
• An RFP response requires multi-year terms and you need to score the shape.
• A renewal expansion is bundled with a discount and you need to verify policy fit.
• You're building a deal-desk approval queue and need consistent routing.

Do NOT use this skill to: author the proposal (use business-growth/contract-and-proposal-writer), redesign the discount matrix (use the commercial-policy sibling skill), or do deep legal redline of full contract text (use c-level-advisor/skills/general-counsel-advisor).

Workflow

1. Intake the deal — Sales/AE fills assets/deal_intake_template.md with ARR, term, discount, payment terms, customer tier, strategic flags, and any customer-flagged term redlines (20-min fill-out).
2. Score margin + risk — Run deal_scorer.py --input deal.json --profile {saas|enterprise-software|services|marketplace}. Read the composite + per-dimension breakdown + verdict.
3. Route the discount — Run discount_approval_router.py --input deal.json --profile <same>. Get the named approver chain + estimated cycle days. Modifiers (enterprise floor, SMB fast-lane) are surfaced explicitly.
4. Flag the redlines — Run terms_redliner.py --input deal_terms.json. Get ranked CRITICAL/HIGH/MEDIUM/LOW findings with the counter-language and the approver who must sign each.
5. Assemble the packet — Combine the three outputs into a deal-desk review packet. Always include the named approver chain. The packet is a recommendation, not an approval.

Scripts

All three: stdlib-only, --help, --sample, --input <json>, --output {human,json}.

References

• references/deal_desk_canon.md — Deal-desk operating practice: SaaStr playbooks (Jason Lemkin), Winning by Design (van der Kooij + Reichl), Forrester research, RevOps Co-op, OpenView benchmarks, Bridge Group AE comp, Salesforce Deal Desk best practices.
• references/discount_economics.md — Discount math + LTV impact: David Skok (For Entrepreneurs), Bessemer State of the Cloud, Tomasz Tunguz, OpenView NRR research, Pacific Crest + KeyBanc SaaS surveys, Insight Partners revenue ops. Includes worked margin math (a 30% discount on an 80% gross-margin product loses 37.5% of margin, not 30%).
• references/contract_landmines.md — 10+ named landmine patterns with example counter-language: YC startup library, Robert Klingberg (Founder's Guide to SaaS Agreements), Bowman + Brooke redline guides, IACCM/WorldCC commercial management research, Practical Law contracts library, Bradley Tusk on enterprise contracts, GC100 guidance.

Assumptions

• The skill assumes the commercial policy already exists (discount bands, payment-terms norms, indemnity caps). It applies the policy; it does not design it. See the commercial-policy sibling skill for policy design.
• Industry profiles bake in customary thresholds. If your company has a documented discount matrix, pass it via policy_thresholds in the input JSON to override.
• The terms redliner detects the 10 most common landmines. It is not a substitute for General Counsel review on the full contract.
• Scoring weights (margin 30%, risk 20%, strategic 15%, commercial 20%, term 15%) reflect a CFO-leaning bias. RevOps-led shops may want to reweight; the weights are constants at the top of score_deal() and are easy to tune.

Anti-patterns

• Auto-approving deals. This skill never says "approved". Every verdict (including APPROVE) names the human(s) who must sign. The output is a recommendation.
• Skipping the redline scan because the score is high. A high composite with UNCAPPED_INDEMNITY is still a DECLINE — critical signals override composite.
• Using this for legal review of arbitrary contract text. This skill takes a structured terms JSON. For prose redlining, use c-level-advisor/skills/general-counsel-advisor/scripts/contract_risk_scanner.py.
• Treating the discount router as a discount calculator. It routes a discount the AE/customer has already proposed; it does not calculate the right discount. Pricing logic lives in commercial/skills/pricing-strategist.
• Routing every deal to CFO. The router stops at the lowest-authority hop that can sign the deal. Over-escalation slows the funnel and trains AEs to over-discount.
• Hand-editing the chain to skip a hop. Modifiers (enterprise floor, SMB fast-lane) are explicit; hidden skips defeat the audit trail.

Distinct from

Quick examples

# Score a deal
python3 scripts/deal_scorer.py --sample
python3 scripts/deal_scorer.py --input my_deal.json --profile enterprise-software

# Route the discount
python3 scripts/discount_approval_router.py --sample
python3 scripts/discount_approval_router.py --input my_deal.json --profile saas

# Flag the redlines
python3 scripts/terms_redliner.py --sample
python3 scripts/terms_redliner.py --input my_deal_terms.json --output json

The sample (a 28%-discount enterprise SaaS deal with uncapped indemnity + MFN) correctly DECLINEs at 55.4 / 100 composite and routes to AE → Deal Desk → VP Sales → CFO → CRO → General Counsel.

Forcing-question library (Matt Pocock grill discipline)

Walked one at a time by /cs:grill-commercial or the Commercial orchestrator. Recommended answer + canon citation per question. Never bundled.

1. "What's the gross margin at full discount, AND what does next quarter's pipeline look like at the same terms?" Recommended: model both. Refuse to approve until the AE can articulate the precedent risk. Canon: David Skok (For Entrepreneurs — discount math), Tomasz Tunguz benchmarks. Anti-pattern: one 40% precedent reshapes 3 quarters of pipeline.

2. "Is this discount inside or outside the standard discount matrix?" Recommended: if outside, surface the policy exception explicitly and route to the named exception approver. Canon: OpenView discount benchmarks, RevOps Co-op playbooks.

3. "What's the strategic value beyond ARR — logo, reference, expansion path?" Recommended: require a named, verifiable expansion or reference commitment in writing. Canon: SaaStr (Jason Lemkin) on logo discounts; Winning by Design on commitment language.

4. "Has the customer signed an indemnity cap, a liability cap, and a DPA (if EU data)?" Recommended: required. Uncapped indemnity is a critical-signal override that blocks APPROVE regardless of margin. Canon: WorldCC (formerly IACCM) commercial management research, GC100 contract guidance.

5. "What payment terms — NET-30, NET-45, or NET-60+?" Recommended: prefer NET-30; NET-45+ is a cash flow drag worth quantifying. Canon: KeyBanc SaaS Survey, Pacific Crest data — every 15 days of payment terms costs ~2% of effective deal value.

6. "Is the term multi-year with annual prepay, or annual auto-renew?" Recommended: multi-year prepay > annual prepay > annual auto-renew. Auto-renew without 60-day notice is a redline. Canon: Salesforce Deal Desk best practices, OpenView NRR studies.

7. "Who is the named human approver at each hop of the discount chain?" Recommended: surface the name, not just the role. "VP Sales" is not an approver; "Maria Singh, VP Sales" is. Canon: Bridge Group SaaS AE compensation research — named approval reduces precedent drift by 50%+.

Walk depth-first. Lock 1-4 before opening 5-7. After all 7 are answered, invoke deal_scorer.py → discount_approval_router.py → terms_redliner.py in sequence.