deal-desk

Purpose

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

• 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.

business-growth/contract-and-proposal-writer

commercial-policy

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

scripts/deal_scorer.py

scripts/discount_approval_router.py

scripts/terms_redliner.py

--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

commercial/skills/pricing-strategist

business-growth/contract-and-proposal-writer

commercial/skills/commercial-policy

c-level-advisor/skills/general-counsel-advisor

c-level-advisor/skills/cfo-advisor

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

Forcing-question library (Matt Pocock grill discipline)

/cs:grill-commercial

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%+.

deal_scorer.py

discount_approval_router.py

terms_redliner.py