[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QC11APP2JD4DBQFH7EZJSJ":3,"skill-same:01M6QC11APP2JD4DBQFH7EZJSJ":82},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":14,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6QC11APP2JD4DBQFH7EZJSJ","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.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fdeal-desk\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"deal","deal desk","auto","for","recommendation","close","director","human","deal review","scoring","general","gemini","use","msa","redline","cro","detection","routing","approval routing","triage","2026-11-10 10:12:30",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":44,"author":50,"license":15,"version":51,"description":7,"compatible_tools":52},[45,5,46,47,48,33,32,49],"commercial","discount","margin","approval","terms","claude-code-skills","2.8.0",[53,54,55,56,57,58],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fdeal-desk","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill deal-desk",{"tool":74,"command":75},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill deal-desk",{"tool":77,"command":78},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill deal-desk","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fdeal-desk\u002FSKILL.md",true,[],[83,114,138,165,188,212],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":94,"publishedAt":39},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[89],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,22,111,112,113],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":39},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[121,122,123,124,99,125,126,127,128,129,130,131,132,133,31,134,135,136,137,22],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","platform","grow","posting","competitive",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":147,"publishedAt":39},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[144],{"categoryNo":145,"categoryName":146,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000023","Skill与智能体开发",[148,132,149,150,151,152,153,154,31,155,156,59,157,158,159,160,161,162,163,164],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":171,"publishedAt":39},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[172,173,174,30,175,176,177,178,179,31,180,106,181,182,24,183,184,185,186,187],"wrap up","up","wrap","deployment","run","file","hardening","closeout","stop","upgrade","founder","companion","you","tables","directions","phase",{"skillNo":189,"name":190,"nameZh":6,"description":191,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":192,"categories":193,"keywords":195,"publishedAt":39},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[194],{"categoryNo":145,"categoryName":146,"taxonomyType":92,"taxonomyTypeDesc":93},[196,197,198,199,200,150,176,201,202,203,204,205,132,206,105,207,208,209,210,211],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":218,"publishedAt":39},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[219,220,221,222,223,224,225,226,227,204,228,229,230,231,232,233,107,234,235,187],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]