[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CFN0DCDF5H08RTB8MFS04W":3,"skill-same:01M7CFN0DCDF5H08RTB8MFS04W":95},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":14,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":46,"install":47,"resources":60},"01M7CFN0DCDF5H08RTB8MFS04W","deal-desk",null,"Use when reviewing a specific inbound deal before close — when sales has asked for a discount that exceeds AE authority, when the customer has redlined the MSA, when per-deal economics (margin after discount, multi-year payment shape, indemnity exposure) need to be quantified, or when discount approval needs to be routed to a named human approver (Sales Director, VP Sales, CFO, CRO, General Counsel). Covers deal review, discount approval routing, per-deal margin scoring, deal exception handling, MSA redline triage, contract landmine detection (uncapped indemnity, MFN, perpetual license-back, missing DPA), and named-approver chain assembly. NEVER auto-approves — every output is a numeric scorecard plus a routing recommendation to a named human.","azhri1990\u002Fjarvis-mega-repo","azhri1990","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F281207543?v=4","01M7BMC0WHXB2ZA9SH1PFJDYNS","Nashriq Mokhtar","01M7C4WMQVQ450E0NA99BG7BHA",0,"MIT","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: deal-desk\ndescription: Use when reviewing a specific inbound deal before close — when sales has asked for a discount that exceeds AE authority, when the customer has redlined the MSA, when per-deal economics (margin after discount, multi-year payment shape, indemnity exposure) need to be quantified, or when discount approval needs to be routed to a named human approver (Sales Director, VP Sales, CFO, CRO, General Counsel). Covers deal review, discount approval routing, per-deal margin scoring, deal exception handling, MSA redline triage, contract landmine detection (uncapped indemnity, MFN, perpetual license-back, missing DPA), and named-approver chain assembly. NEVER auto-approves — every output is a numeric scorecard plus a routing recommendation to a named human.\nversion: 2.8.0\nauthor: claude-code-skills\nlicense: MIT\ntags: [commercial, deal-desk, discount, margin, approval, redline, msa, terms]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# deal-desk\n\nPer-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.\n\n## Purpose\n\nDeal Desk \u002F RevOps \u002F sales leadership live at the moment between *sales-team-asks-for-discount* and *CFO\u002FCRO\u002Flegal-signs*. This skill quantifies the asks and routes them.\n\nThree deterministic tools:\n\n1. `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 \u002F REVIEW \u002F ESCALATE \u002F DECLINE** — each tied to a named approver chain.\n2. `discount_approval_router.py` — Maps a discount-percent + deal-size + tier to a named approver chain (AE → Manager → Director → VP → CFO\u002FCRO) with estimated cycle days. Honors industry-tuned policy bands.\n3. `terms_redliner.py` — Detects 10 founder\u002Fseller-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\u002Fcommercial approver.\n\n## When to use\n\nInvoke this skill when:\n\n- Sales has flagged a discount request above AE authority.\n- A customer has returned a redlined MSA and you need triage before routing to legal.\n- The deal needs CFO sign-off and you want a defensible margin breakdown.\n- An RFP response requires multi-year terms and you need to score the shape.\n- A renewal expansion is bundled with a discount and you need to verify policy fit.\n- You're building a deal-desk approval queue and need consistent routing.\n\n**Do NOT use this skill to**: author the proposal (use `business-growth\u002Fcontract-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\u002Fskills\u002Fgeneral-counsel-advisor`).\n\n## Workflow\n\n1. **Intake the deal** — Sales\u002FAE fills `assets\u002Fdeal_intake_template.md` with ARR, term, discount, payment terms, customer tier, strategic flags, and any customer-flagged term redlines (20-min fill-out).\n2. **Score margin + risk** — Run `deal_scorer.py --input deal.json --profile {saas|enterprise-software|services|marketplace}`. Read the composite + per-dimension breakdown + verdict.\n3. **Route the discount** — Run `discount_approval_router.py --input deal.json --profile \u003Csame>`. Get the named approver chain + estimated cycle days. Modifiers (enterprise floor, SMB fast-lane) are surfaced explicitly.\n4. **Flag the redlines** — Run `terms_redliner.py --input deal_terms.json`. Get ranked CRITICAL\u002FHIGH\u002FMEDIUM\u002FLOW findings with the counter-language and the approver who must sign each.\n5. **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.\n\n## Scripts\n\n| Script | Purpose | Industry profiles |\n|---|---|---|\n| `scripts\u002Fdeal_scorer.py` | 5-dimension scorecard with verdict + chain | saas, enterprise-software, services, marketplace |\n| `scripts\u002Fdiscount_approval_router.py` | Discount % → named approver chain + cycle days | saas, enterprise-software, services, marketplace |\n| `scripts\u002Fterms_redliner.py` | 10-pattern landmine scanner with counters | n\u002Fa (terms-driven) |\n\nAll three: stdlib-only, `--help`, `--sample`, `--input \u003Cjson>`, `--output {human,json}`.\n\n## References\n\n- `references\u002Fdeal_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.\n- `references\u002Fdiscount_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%).\n- `references\u002Fcontract_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\u002FWorldCC commercial management research, Practical Law contracts library, Bradley Tusk on enterprise contracts, GC100 guidance.\n\n## Assumptions\n\n- 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.\n- 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.\n- The terms redliner detects the 10 most common landmines. It is **not** a substitute for General Counsel review on the full contract.\n- 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.\n\n## Anti-patterns\n\n- **Auto-approving deals.** This skill never says \"approved\". Every verdict (including `APPROVE`) names the human(s) who must sign. The output is a recommendation.\n- **Skipping the redline scan** because the score is high. A high composite with `UNCAPPED_INDEMNITY` is still a DECLINE — critical signals override composite.\n- **Using this for legal review of arbitrary contract text.** This skill takes a *structured* terms JSON. For prose redlining, use `c-level-advisor\u002Fskills\u002Fgeneral-counsel-advisor\u002Fscripts\u002Fcontract_risk_scanner.py`.\n- **Treating the discount router as a discount calculator.** It routes a discount the AE\u002Fcustomer has already proposed; it does not calculate the right discount. Pricing logic lives in `commercial\u002Fskills\u002Fpricing-strategist`.\n- **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.\n- **Hand-editing the chain to skip a hop.** Modifiers (enterprise floor, SMB fast-lane) are explicit; hidden skips defeat the audit trail.\n\n## Distinct from\n\n| Sibling | Scope | Difference |\n|---|---|---|\n| `commercial\u002Fskills\u002Fpricing-strategist` | Sets the pricing **model** (per-seat vs usage vs tiered, list prices, packaging) | Operates at the strategy layer — not per deal |\n| `business-growth\u002Fcontract-and-proposal-writer` | **Authors** proposals, SOWs, MSAs | Output is a document; deal-desk is the gate **before** signing |\n| `commercial\u002Fskills\u002Fcommercial-policy` (sibling) | Designs the discount matrix and approval thresholds | Deal-desk **applies** that policy to one deal at a time |\n| `c-level-advisor\u002Fskills\u002Fgeneral-counsel-advisor` | Deep legal redline + term-sheet analysis | Operates on full contract prose; deal-desk uses structured terms JSON |\n| `c-level-advisor\u002Fskills\u002Fcfo-advisor` | Burn rate, unit economics, fundraising models | Strategic finance; deal-desk is one-deal granularity |\n\n## Quick examples\n\n```bash\n# Score a deal\npython3 scripts\u002Fdeal_scorer.py --sample\npython3 scripts\u002Fdeal_scorer.py --input my_deal.json --profile enterprise-software\n\n# Route the discount\npython3 scripts\u002Fdiscount_approval_router.py --sample\npython3 scripts\u002Fdiscount_approval_router.py --input my_deal.json --profile saas\n\n# Flag the redlines\npython3 scripts\u002Fterms_redliner.py --sample\npython3 scripts\u002Fterms_redliner.py --input my_deal_terms.json --output json\n```\n\nThe sample (a 28%-discount enterprise SaaS deal with uncapped indemnity + MFN) correctly DECLINEs at 52.7 \u002F 100 composite — the 28% discount destroys 35.9% of the deal's margin dollars under fixed COGS — and routes to AE → Deal Desk → VP Sales → CFO → CRO → General Counsel.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nWalked one at a time by `\u002Fcs:grill-commercial` or the Commercial orchestrator. Recommended answer + canon citation per question. Never bundled.\n\n1. **\"What's the gross margin at full discount, AND what does next quarter's pipeline look like at the same terms?\"**\n   Recommended: model both. Refuse to approve until the AE can articulate the precedent risk.\n   Canon: David Skok (For Entrepreneurs — discount math), Tomasz Tunguz benchmarks. Anti-pattern: one 40% precedent reshapes 3 quarters of pipeline.\n\n2. **\"Is this discount inside or outside the standard discount matrix?\"**\n   Recommended: if outside, surface the policy exception explicitly and route to the named exception approver.\n   Canon: OpenView discount benchmarks, RevOps Co-op playbooks.\n\n3. **\"What's the strategic value beyond ARR — logo, reference, expansion path?\"**\n   Recommended: require a named, verifiable expansion or reference commitment in writing.\n   Canon: SaaStr (Jason Lemkin) on logo discounts; Winning by Design on commitment language.\n\n4. **\"Has the customer signed an indemnity cap, a liability cap, and a DPA (if EU data)?\"**\n   Recommended: required. Uncapped indemnity is a critical-signal override that blocks APPROVE regardless of margin.\n   Canon: WorldCC (formerly IACCM) commercial management research, GC100 contract guidance.\n\n5. **\"What payment terms — NET-30, NET-45, or NET-60+?\"**\n   Recommended: prefer NET-30; NET-45+ is a cash flow drag worth quantifying.\n   Canon: KeyBanc SaaS Survey, Pacific Crest data — every 15 days of payment terms costs ~2% of effective deal value.\n\n6. **\"Is the term multi-year with annual prepay, or annual auto-renew?\"**\n   Recommended: multi-year prepay > annual prepay > annual auto-renew. Auto-renew without 60-day notice is a redline.\n   Canon: Salesforce Deal Desk best practices, OpenView NRR studies.\n\n7. **\"Who is the named human approver at each hop of the discount chain?\"**\n   Recommended: surface the name, not just the role. \"VP Sales\" is not an approver; \"Maria Singh, VP Sales\" is.\n   Canon: Bridge Group SaaS AE compensation research — named approval reduces precedent drift by 50%+.\n\nWalk 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.\n","# deal-desk\n\nPer-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.\n\n## Purpose\n\nDeal Desk \u002F RevOps \u002F sales leadership live at the moment between *sales-team-asks-for-discount* and *CFO\u002FCRO\u002Flegal-signs*. This skill quantifies the asks and routes them.\n\nThree deterministic tools:\n\n1. `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 \u002F REVIEW \u002F ESCALATE \u002F DECLINE** — each tied to a named approver chain.\n2. `discount_approval_router.py` — Maps a discount-percent + deal-size + tier to a named approver chain (AE → Manager → Director → VP → CFO\u002FCRO) with estimated cycle days. Honors industry-tuned policy bands.\n3. `terms_redliner.py` — Detects 10 founder\u002Fseller-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\u002Fcommercial approver.\n\n## When to use\n\nInvoke this skill when:\n\n- Sales has flagged a discount request above AE authority.\n- A customer has returned a redlined MSA and you need triage before routing to legal.\n- The deal needs CFO sign-off and you want a defensible margin breakdown.\n- An RFP response requires multi-year terms and you need to score the shape.\n- A renewal expansion is bundled with a discount and you need to verify policy fit.\n- You're building a deal-desk approval queue and need consistent routing.\n\n**Do NOT use this skill to**: author the proposal (use `business-growth\u002Fcontract-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\u002Fskills\u002Fgeneral-counsel-advisor`).\n\n## Workflow\n\n1. **Intake the deal** — Sales\u002FAE fills `assets\u002Fdeal_intake_template.md` with ARR, term, discount, payment terms, customer tier, strategic flags, and any customer-flagged term redlines (20-min fill-out).\n2. **Score margin + risk** — Run `deal_scorer.py --input deal.json --profile {saas|enterprise-software|services|marketplace}`. Read the composite + per-dimension breakdown + verdict.\n3. **Route the discount** — Run `discount_approval_router.py --input deal.json --profile \u003Csame>`. Get the named approver chain + estimated cycle days. Modifiers (enterprise floor, SMB fast-lane) are surfaced explicitly.\n4. **Flag the redlines** — Run `terms_redliner.py --input deal_terms.json`. Get ranked CRITICAL\u002FHIGH\u002FMEDIUM\u002FLOW findings with the counter-language and the approver who must sign each.\n5. **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.\n\n## Scripts\n\n| Script | Purpose | Industry profiles |\n|---|---|---|\n| `scripts\u002Fdeal_scorer.py` | 5-dimension scorecard with verdict + chain | saas, enterprise-software, services, marketplace |\n| `scripts\u002Fdiscount_approval_router.py` | Discount % → named approver chain + cycle days | saas, enterprise-software, services, marketplace |\n| `scripts\u002Fterms_redliner.py` | 10-pattern landmine scanner with counters | n\u002Fa (terms-driven) |\n\nAll three: stdlib-only, `--help`, `--sample`, `--input \u003Cjson>`, `--output {human,json}`.\n\n## References\n\n- `references\u002Fdeal_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.\n- `references\u002Fdiscount_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%).\n- `references\u002Fcontract_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\u002FWorldCC commercial management research, Practical Law contracts library, Bradley Tusk on enterprise contracts, GC100 guidance.\n\n## Assumptions\n\n- 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.\n- 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.\n- The terms redliner detects the 10 most common landmines. It is **not** a substitute for General Counsel review on the full contract.\n- 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.\n\n## Anti-patterns\n\n- **Auto-approving deals.** This skill never says \"approved\". Every verdict (including `APPROVE`) names the human(s) who must sign. The output is a recommendation.\n- **Skipping the redline scan** because the score is high. A high composite with `UNCAPPED_INDEMNITY` is still a DECLINE — critical signals override composite.\n- **Using this for legal review of arbitrary contract text.** This skill takes a *structured* terms JSON. For prose redlining, use `c-level-advisor\u002Fskills\u002Fgeneral-counsel-advisor\u002Fscripts\u002Fcontract_risk_scanner.py`.\n- **Treating the discount router as a discount calculator.** It routes a discount the AE\u002Fcustomer has already proposed; it does not calculate the right discount. Pricing logic lives in `commercial\u002Fskills\u002Fpricing-strategist`.\n- **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.\n- **Hand-editing the chain to skip a hop.** Modifiers (enterprise floor, SMB fast-lane) are explicit; hidden skips defeat the audit trail.\n\n## Distinct from\n\n| Sibling | Scope | Difference |\n|---|---|---|\n| `commercial\u002Fskills\u002Fpricing-strategist` | Sets the pricing **model** (per-seat vs usage vs tiered, list prices, packaging) | Operates at the strategy layer — not per deal |\n| `business-growth\u002Fcontract-and-proposal-writer` | **Authors** proposals, SOWs, MSAs | Output is a document; deal-desk is the gate **before** signing |\n| `commercial\u002Fskills\u002Fcommercial-policy` (sibling) | Designs the discount matrix and approval thresholds | Deal-desk **applies** that policy to one deal at a time |\n| `c-level-advisor\u002Fskills\u002Fgeneral-counsel-advisor` | Deep legal redline + term-sheet analysis | Operates on full contract prose; deal-desk uses structured terms JSON |\n| `c-level-advisor\u002Fskills\u002Fcfo-advisor` | Burn rate, unit economics, fundraising models | Strategic finance; deal-desk is one-deal granularity |\n\n## Quick examples\n\n```bash\n# Score a deal\npython3 scripts\u002Fdeal_scorer.py --sample\npython3 scripts\u002Fdeal_scorer.py --input my_deal.json --profile enterprise-software\n\n# Route the discount\npython3 scripts\u002Fdiscount_approval_router.py --sample\npython3 scripts\u002Fdiscount_approval_router.py --input my_deal.json --profile saas\n\n# Flag the redlines\npython3 scripts\u002Fterms_redliner.py --sample\npython3 scripts\u002Fterms_redliner.py --input my_deal_terms.json --output json\n```\n\nThe sample (a 28%-discount enterprise SaaS deal with uncapped indemnity + MFN) correctly DECLINEs at 52.7 \u002F 100 composite — the 28% discount destroys 35.9% of the deal's margin dollars under fixed COGS — and routes to AE → Deal Desk → VP Sales → CFO → CRO → General Counsel.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nWalked one at a time by `\u002Fcs:grill-commercial` or the Commercial orchestrator. Recommended answer + canon citation per question. Never bundled.\n\n1. **\"What's the gross margin at full discount, AND what does next quarter's pipeline look like at the same terms?\"**\n   Recommended: model both. Refuse to approve until the AE can articulate the precedent risk.\n   Canon: David Skok (For Entrepreneurs — discount math), Tomasz Tunguz benchmarks. Anti-pattern: one 40% precedent reshapes 3 quarters of pipeline.\n\n2. **\"Is this discount inside or outside the standard discount matrix?\"**\n   Recommended: if outside, surface the policy exception explicitly and route to the named exception approver.\n   Canon: OpenView discount benchmarks, RevOps Co-op playbooks.\n\n3. **\"What's the strategic value beyond ARR — logo, reference, expansion path?\"**\n   Recommended: require a named, verifiable expansion or reference commitment in writing.\n   Canon: SaaStr (Jason Lemkin) on logo discounts; Winning by Design on commitment language.\n\n4. **\"Has the customer signed an indemnity cap, a liability cap, and a DPA (if EU data)?\"**\n   Recommended: required. Uncapped indemnity is a critical-signal override that blocks APPROVE regardless of margin.\n   Canon: WorldCC (formerly IACCM) commercial management research, GC100 contract guidance.\n\n5. **\"What payment terms — NET-30, NET-45, or NET-60+?\"**\n   Recommended: prefer NET-30; NET-45+ is a cash flow drag worth quantifying.\n   Canon: KeyBanc SaaS Survey, Pacific Crest data — every 15 days of payment terms costs ~2% of effective deal value.\n\n6. **\"Is the term multi-year with annual prepay, or annual auto-renew?\"**\n   Recommended: multi-year prepay > annual prepay > annual auto-renew. Auto-renew without 60-day notice is a redline.\n   Canon: Salesforce Deal Desk best practices, OpenView NRR studies.\n\n7. **\"Who is the named human approver at each hop of the discount chain?\"**\n   Recommended: surface the name, not just the role. \"VP Sales\" is not an approver; \"Maria Singh, VP Sales\" is.\n   Canon: Bridge Group SaaS AE compensation research — named approval reduces precedent drift by 50%+.\n\nWalk 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.\n",{"name":5,"tags":24,"author":32,"license":15,"version":33,"description":7,"compatible_tools":34},[25,5,26,27,28,29,30,31],"commercial","discount","margin","approval","redline","msa","terms","claude-code-skills","2.8.0",[35,36,37,38,39,40],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","46d43f3edecde97283ac410e9051444d90120cfb","repos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo","https:\u002F\u002Fgithub.com\u002Fazhri1990","2026-10-10 07:58:58",{"source":8,"skillName":5,"commands":48,"prompt":58,"zipAvailable":59},[49,52,55],{"tool":50,"command":51},"npx","npx skills add azhri1990\u002Fjarvis-mega-repo --skill deal-desk",{"tool":53,"command":54},"bunx","bunx skills add azhri1990\u002Fjarvis-mega-repo --skill deal-desk",{"tool":56,"command":57},"pnpm","pnpm dlx skills add azhri1990\u002Fjarvis-mega-repo --skill deal-desk","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002FSKILL.md",true,[61,67,73,77,81,87,91],{"path":62,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":65,"sourceHtmlUrl":66},"assets\u002Fdeal_intake_template.md","ASSET","资产文件",4909,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002Fassets\u002Fdeal_intake_template.md",{"path":68,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":71,"sourceHtmlUrl":72},"references\u002Fcontract_landmines.md","REFERENCE","参考文档",9221,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002Freferences\u002Fcontract_landmines.md",{"path":74,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":75,"sourceHtmlUrl":76},"references\u002Fdeal_desk_canon.md",5256,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002Freferences\u002Fdeal_desk_canon.md",{"path":78,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fdiscount_economics.md",6214,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002Freferences\u002Fdiscount_economics.md",{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"scripts\u002Fdeal_scorer.py","SCRIPT","脚本",13815,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002Fscripts\u002Fdeal_scorer.py",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002Fdiscount_approval_router.py",9156,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002Fscripts\u002Fdiscount_approval_router.py",{"path":92,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":93,"sourceHtmlUrl":94},"scripts\u002Fterms_redliner.py",11574,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002Fcommercial\u002Fskills\u002Fdeal-desk\u002Fscripts\u002Fterms_redliner.py",[96,103,110,117,124,131],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":19},"01M7CYPF9V9EMA4ZXS1TTCMBFH","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[],[],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":19},"01M7CYNRJDTQCRF5KGPMBMSB1S","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[],[],{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":19},"01M7CYNAVSE84QD7HAWZWTY2FS","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[],[],{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":19},"01M7CYN0CE8P5ZN7WV2X06HKPC","notebooklm","Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio\u002FVideo Overviews, Mind Maps, Reports incl. Briefing Doc\u002FStudy Guide\u002FFAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[],[],{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":130,"publishedAt":19},"01M7CYMHZ3QRJ9BKRB859KW8TQ","litreview","Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[],[],{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":19},"01M7CYM3D8YKV7R6DT4RNJSF0A","grants","NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance\u002FInnovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget\u002Fscope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fgrants\u002Fskills\u002Fgrants\u002FSKILL.md",[],[]]