[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-microsoft-cross-sell-target-identifier":3,"mdc--p6ir2y-key":36,"related-repo-microsoft-cross-sell-target-identifier":1677,"related-org-microsoft-cross-sell-target-identifier":1777},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":34,"mdContent":35},"cross-sell-target-identifier","identify cross-sell targets from customer patterns","Analyzes successful product customers to identify patterns, then finds similar accounts that are good cross-sell candidates with fit scores and reasoning. Use when user asks \"who should I pitch this product to\", \"find cross-sell opportunities\", \"which customers should buy Product X\", \"identify upsell targets\", \"product expansion candidates\", or \"who else would buy this\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"microsoft","Microsoft","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fmicrosoft.png",[12,14,17,20,23],{"name":9,"slug":8,"type":13},"tag",{"name":15,"slug":16,"type":13},"CRM","crm",{"name":18,"slug":19,"type":13},"Sales","sales",{"name":21,"slug":22,"type":13},"Analytics","analytics",{"name":24,"slug":25,"type":13},"Dataverse","dataverse",41,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fdataverse-business-skills","2026-04-06T18:36:37.380929",null,9,[],{"repoUrl":27,"stars":26,"forks":30,"topics":33,"description":29},[],"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fdataverse-business-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fcross-sell-target-identifier","---\nname: cross-sell-target-identifier\ndescription: Analyzes successful product customers to identify patterns, then finds similar accounts that are good cross-sell candidates with fit scores and reasoning. Use when user asks \"who should I pitch this product to\", \"find cross-sell opportunities\", \"which customers should buy Product X\", \"identify upsell targets\", \"product expansion candidates\", or \"who else would buy this\".\nmetadata:\n  author: Dataverse\n  version: 1.0.0\n  category: sales-analytics\n---\n\n# Cross-Sell Target Identifier\n\nWhen launching a new product or looking to expand product adoption, sales teams need to identify which existing customers are most likely to purchase. This skill analyzes the characteristics of successful customers for a given product, finds similar customers who don't own it yet, and provides prioritized recommendations with justification.\n\n## Instructions\n\n### Step 1: Identify the Target Product\nWhen user asks \"Which customers should I pitch Product X to?\":\n\n1. **Identify the Product:**\n```\nSELECT productid, name, description, producttypecode, productstructure\nFROM product\nWHERE name LIKE '%[product name]%'\nAND statecode = 0\n```\n\n2. **Confirm with user if multiple matches**\n\n#### Step 2: Analyze Successful Product X Customers\n\n**2.1 Find Customers Who Own Product X**\nQuery won opportunities that included the target product:\n\n```\nSELECT op.opportunityid, op.customerid, op.accountid, op.actualvalue,\n       op.actualclosedate, op.salesstage\nFROM opportunity op\nJOIN opportunityproduct opp ON op.opportunityid = opp.opportunityid\nWHERE opp.productid = '[target_product_id]'\nAND op.statecode = 1\n```\n\nNote: A product may appear in multiple opportunityproduct rows per opportunity. Deduplicate opportunityids programmatically after fetching results.\n\n**2.2 Build Success Profile from Winning Accounts**\nFor each winning account, gather firmographic data:\n\n```\nSELECT accountid, name, industrycode, numberofemployees, revenue,\n       customertypecode, address1_stateorprovince, address1_country,\n       ownershipcode, createdon\nFROM account\nWHERE accountid IN ([list of winning account ids])\n```\n\n**2.3 Analyze Success Patterns**\n\n**Firmographic Analysis:**\n```\nCalculate distribution across successful customers:\n- Industry breakdown (industrycode): Which industries buy most?\n- Company size (numberofemployees): What's the typical range?\n- Revenue range: What's the typical revenue bracket?\n- Geography (address1_stateorprovince\u002Fcountry): Regional concentrations?\n- Customer type (customertypecode): Are they customers, partners, etc.?\n```\n\n**Existing Product Ownership:**\n```\nFor each successful customer, identify other products owned:\nSELECT a.accountid, a.name, p.name as product_name\nFROM account a\nJOIN opportunity o ON a.accountid = o.accountid\nJOIN opportunityproduct op ON o.opportunityid = op.opportunityid\nJOIN product p ON op.productid = p.productid\nWHERE o.statecode = 1\nAND a.accountid IN ([winning account ids])\n```\n\n**Buying Pattern Analysis:**\n```\nIdentify patterns in successful deals:\n- Average deal size for Product X\n- Common bundled products\n- Typical sales cycle length\n- Time since becoming customer before purchasing Product X\n```\n\n**Activity Pattern Analysis:**\n```\nReview activities preceding successful deals:\n- Types of engagement (calls, meetings, emails)\n- Number of touchpoints before close\n- Content\u002Fresources shared\n```\n\n#### Step 3: Generate Ideal Customer Profile (ICP)\n\nBased on the analysis, create an Ideal Customer Profile:\n\n```\nIDEAL CUSTOMER PROFILE FOR [PRODUCT X]\n════════════════════════════════════════════════════\n\nFIRMOGRAPHIC CHARACTERISTICS:\n- Industry: [Top 3 industries, e.g., \"Financial Services (35%), Healthcare (28%), Technology (22%)\"]\n- Company Size: [Employee range, e.g., \"100-500 employees (sweet spot)\"]\n- Revenue: [Revenue range, e.g., \"$10M-$100M annual revenue\"]\n- Geography: [Regional patterns, e.g., \"Primarily US, expanding to UK\u002FEU\"]\n\nBEHAVIORAL INDICATORS:\n- Already Owns: [Products commonly owned first, e.g., \"80% have Product Y\"]\n- Customer Tenure: [Time as customer, e.g., \"Typically 6-18 months as customer\"]\n- Recent Activity: [Engagement patterns, e.g., \"High engagement with support\u002Fsuccess\"]\n- Expansion Signals: [Growth indicators, e.g., \"Recent hiring, new funding\"]\n\nBUYING PATTERNS:\n- Average Deal Size: [$X]\n- Typical Bundle: [Product X + Y + Z]\n- Sales Cycle: [X days average]\n- Common Champion: [Job title patterns]\n\nSUCCESS INDICATORS FROM NOTES:\n- Pain Points: [Common challenges mentioned]\n- Use Cases: [How they use the product]\n- Trigger Events: [What prompted purchase]\n```\n\n#### Step 4: Query Customer Base for Matches\n\n**4.1 Find Non-Owners of Product X**\n\nNote: Dataverse SQL does not support subqueries. Run two separate queries and exclude owners programmatically.\n\nFirst, get all accounts that already own Product X (from Step 2.1 results — collect their accountids into a list).\n\nThen query all active accounts:\n```\nSELECT a.accountid, a.name, a.industrycode, a.numberofemployees, a.revenue,\n       a.customertypecode, a.address1_stateorprovince, a.createdon\nFROM account a\nWHERE a.statecode = 0\n```\n\nFilter out accounts whose accountid appears in the owner list programmatically after fetching.\n\n**4.2 Score Each Potential Target**\n\nFor each non-owner account, calculate fit score:\n\n**Firmographic Fit (40%):**\n| Factor | Points | Scoring Logic |\n|--------|--------|---------------|\n| Industry Match | 0-15 | Exact match to top ICP industry = 15, Adjacent = 10, Other = 0 |\n| Size Match | 0-15 | Within ICP range = 15, Close = 10, Outside = 5, Way off = 0 |\n| Revenue Match | 0-10 | Within ICP range = 10, Close = 5, Outside = 0 |\n\n**Behavioral Fit (35%):**\n| Factor | Points | Scoring Logic |\n|--------|--------|---------------|\n| Owns Prerequisite Products | 0-15 | Has common prerequisite = 15, Related product = 10 |\n| Customer Tenure | 0-10 | In ICP tenure range = 10, Close = 5 |\n| Recent Engagement | 0-10 | High recent activity = 10, Moderate = 5, Low = 0 |\n\n**Buying Signals (25%):**\n| Factor | Points | Scoring Logic |\n|--------|--------|---------------|\n| Recent Purchases | 0-10 | Bought something in last 6 months = 10 |\n| Expansion Behavior | 0-10 | Added users, upgraded = 10, Stable = 5 |\n| Strategic Initiative Signals | 0-5 | Mentioned in notes\u002Factivities = 5 |\n\n#### Step 5: Analyze Buying Signals\n\n**Important: Dataverse SQL Limitations**\nDataverse SQL does NOT support: subqueries, DATEADD(), GETUTCDATE(), HAVING, DISTINCT, UNION, CASE statements, AVG on sentiment.\nCalculate date filters programmatically before querying (e.g., '2025-09-01' for 6 months ago).\n\n**5.1 Recent Purchase Activity**\n```\nSELECT a.accountid, a.name, o.opportunityid, o.name, o.actualclosedate, o.actualvalue\nFROM account a\nJOIN opportunity o ON a.accountid = o.accountid\nWHERE o.statecode = 1\nAND o.actualclosedate > '2025-09-01'\n```\n\n**5.2 Expansion Indicators**\nLook for signals in activities and notes:\n```\nSELECT annotationid, objectid, subject, notetext, createdon\nFROM annotation\nWHERE objecttypecode = 'account'\nAND createdon > '2025-09-01'\n```\n\n**Keywords to Detect:**\n- Expansion: \"growing\", \"scaling\", \"expanding\", \"new offices\", \"hiring\"\n- Strategic: \"initiative\", \"project\", \"transformation\", \"migration\"\n- Pain: \"struggling\", \"challenge\", \"problem\", \"need\"\n- Competition: \"evaluating\", \"considering\", \"looking at\"\n\n**5.3 Recent Cases (Support Indicators)**\nQuery cases per account, then aggregate programmatically:\n```\nSELECT incidentid, customerid, createdon, prioritycode, msdyn_casesentiment\nFROM incident\nWHERE createdon >= '[6_months_ago]'\n```\n\nGroup and count by customerid programmatically after fetching results.\n\nHigh case volume could indicate:\n- Active usage (good for expansion)\n- Frustration (may need resolution first)\n- Product limitations (potential for upsell to better solution)\n\n#### Step 6: Rank and Present Target Accounts\n\n**Output Format:**\n```\nCROSS-SELL TARGETS FOR [PRODUCT X]\n════════════════════════════════════════════════════\nAnalysis Date: [Date]\nMethodology: Compared against [N] successful Product X customers\n\nSUMMARY:\n- Total Eligible Accounts: [N]\n- High Fit (Score 80+): [N] accounts\n- Medium Fit (Score 60-79): [N] accounts\n- Low Fit (Score \u003C60): [N] accounts\n\n════════════════════════════════════════════════════\nTOP 10 CROSS-SELL TARGETS\n════════════════════════════════════════════════════\n\n1. CONTOSO CORPORATION\n   Fit Score: 92\u002F100\n   ────────────────────────────────────────────────\n   \n   WHY THEY'RE A FIT:\n   ✓ Industry: Financial Services (top ICP industry)\n   ✓ Size: 350 employees (in sweet spot 100-500)\n   ✓ Already Owns: Product Y, Product Z (common prerequisite)\n   ✓ Customer Since: 14 months (optimal tenure range)\n   ✓ Recent Activity: 8 touchpoints in last 30 days\n   \n   BUYING SIGNALS DETECTED:\n   • Mentioned \"scaling operations\" in recent meeting notes\n   • Purchased add-on licenses last month (expansion behavior)\n   • Attended Product X webinar 2 weeks ago\n   \n   RECOMMENDED APPROACH:\n   • Lead with [specific value prop based on industry]\n   • Reference success story from [similar customer]\n   • Contact: [Primary contact name and role]\n   \n   ESTIMATED DEAL SIZE: $45,000 (based on similar deals)\n\n2. FABRIKAM INDUSTRIES\n   Fit Score: 87\u002F100\n   ────────────────────────────────────────────────\n   \n   WHY THEY'RE A FIT:\n   ✓ Industry: Manufacturing (adjacent to ICP)\n   ✓ Size: 800 employees (slightly above sweet spot)\n   ✓ Already Owns: Product Y\n   ✓ High engagement with Customer Success\n   \n   BUYING SIGNALS DETECTED:\n   • New CTO joined 3 months ago (leadership change)\n   • Mentioned \"digital transformation\" in discovery call\n   \n   POTENTIAL CONCERNS:\n   ⚠ Above typical company size - may need enterprise approach\n   ⚠ No activity with Sales in last 60 days\n   \n   RECOMMENDED APPROACH:\n   • Re-engage through Customer Success warm intro\n   • Position as part of transformation initiative\n   • Consider executive sponsor engagement\n\n[Continue for top 10...]\n```\n\n#### Step 7: Create Action Items\n\n**Generate Follow-up Tasks:**\n```\nFor each top target, offer to create:\n\nUse create_record with tablename: task\n{\n  \"subject\": \"Cross-sell outreach: [Product X] to [Account Name]\",\n  \"description\": \"Target identified as high fit for [Product X].\\n\\nFit Score: [X]\u002F100\\n\\nKey talking points:\\n- [Point 1]\\n- [Point 2]\\n\\nContact: [Recommended contact]\",\n  \"regardingobjectid\": \"[accountid]\",\n  \"scheduledend\": \"[appropriate date]\",\n  \"prioritycode\": [based on fit score]\n}\n```\n\n**Update Account with Cross-Sell Flag:**\n```\nConsider adding notes to account:\n\nUse create_record with tablename: annotation\n{\n  \"subject\": \"Cross-sell opportunity identified: [Product X]\",\n  \"notetext\": \"[Summary of why they're a fit and recommended approach]\",\n  \"objectid\": \"[accountid]\",\n  \"objecttypecode\": \"account\"\n}\n```\n\n### Dataverse Tables Used\n| Table | Purpose |\n|-------|---------|\n| `product` | Identify target product |\n| `opportunity` | Find won deals with target product |\n| `opportunityproduct` | Link opportunities to products |\n| `account` | Customer firmographic data |\n| `contact` | Stakeholder information |\n| `activitypointer` | Engagement history |\n| `annotation` | Notes containing buying signals |\n| `incident` | Support case patterns |\n| `task` | Create follow-up tasks |\n\n### Key Fields Reference\n**product:**\n- `productid` (GUID) - Unique identifier\n- `name` (NVARCHAR) - Product name\n- `productnumber` (NVARCHAR) - SKU\u002Fproduct number\n- `producttypecode` (CHOICE) - Sales Inventory(1), Misc Charges(2), Services(3), Flat Fees(4)\n- `productstructure` (CHOICE) - Product(1), Family(2), Bundle(3)\n- `statecode` (STATE) - Active(0), Retired(1), Draft(2), Under Revision(3)\n\n**opportunity:**\n- `accountid` (LOOKUP → account) - Related account\n- `statecode` (STATE) - Open(0), Won(1), Lost(2)\n- `statuscode` (STATUS) - In Progress(1), On Hold(2) [Open]; Won(3) [Won]; Canceled(4), Out-Sold(5) [Lost]\n- `actualvalue` (MONEY) - Won deal value\n- `actualclosedate` (DATE) - When deal closed\n- `originatingleadid` (LOOKUP → lead) - Source lead\n\n**opportunityproduct:**\n- `opportunityid` (LOOKUP → opportunity) - Parent opportunity\n- `productid` (LOOKUP → product) - Product in the deal\n- `quantity` (DECIMAL) - Units sold\n- `priceperunit` (MONEY) - Unit price\n- `extendedamount` (MONEY) - Line total (calculated)\n- `manualdiscountamount` (MONEY) - Line discount\n\n**account:**\n- `industrycode` (CHOICE) - Accounting(1), Agriculture(2), Broadcasting(3), Brokers(4), Building Supply(5), Business Services(6), Consulting(7), Consumer Services(8), etc.\n- `numberofemployees` (INT) - Company size\n- `revenue` (MONEY) - Annual revenue\n- `customertypecode` (CHOICE) - Customer classification\n- `openrevenue` (MONEY) - Total open pipeline value *(rollup field; availability depends on org configuration — query opportunity table directly if not present)*\n- `opendeals` (INT) - Number of open opportunities *(rollup field; availability depends on org configuration)*\n\n### Cross-Sell Best Practices\n\n1. **Start with success:** Always analyze existing successful customers first\n2. **Multi-factor matching:** Don't rely on single criteria for fit\n3. **Watch for timing:** Recent purchases indicate budget availability\n4. **Leverage relationships:** Warm introductions beat cold outreach\n5. **Segment recommendations:** Different approaches for different segments\n6. **Track results:** Monitor conversion rates to refine the model\n\n## Examples\n\n### Example 1: Find Cross-Sell Targets for New Product\n\n**User says:** \"Who should I pitch our new Analytics Pro product to?\"\n\n**Actions:**\n1. Search product table for \"Analytics Pro\"\n2. Find accounts that have purchased Analytics Pro\n3. Build ideal customer profile from successful accounts\n4. Query non-owners matching the profile\n5. Rank by fit score and provide recommendations\n\n**Result:**\n```\nIDEAL CUSTOMER PROFILE FOR ANALYTICS PRO:\n- Industry: Financial Services (45%), Healthcare (30%)\n- Size: 200-1000 employees\n- Already owns: Platform Basic (80% correlation)\n\nTOP 10 CROSS-SELL TARGETS:\n1. Northwind Bank (92% fit) - Financial Services, 450 employees, owns Platform Basic\n2. Alpine Health (87% fit) - Healthcare, 800 employees, recent support engagement\n```\n\n### Example 2: Upsell Existing Customers\n\n**User says:** \"Which customers should upgrade to Enterprise tier?\"\n\n**Actions:**\n1. Identify customers on lower tiers\n2. Analyze Enterprise customers for common traits\n3. Find Standard tier customers matching Enterprise profile\n4. Factor in engagement and growth signals\n\n**Result:**\n```\nUPGRADE CANDIDATES (Standard → Enterprise):\n1. Contoso Ltd - Growing usage, added 50 users last quarter\n2. Fabrikam Inc - Multiple support cases about feature limits\n3. Tailspin Toys - Recent funding, headcount doubling\n```\n\n### Example 3: Product Bundle Opportunity\n\n**User says:** \"Who bought Product A but not Product B?\"\n\n**Actions:**\n1. Find accounts with Product A in won opportunities\n2. Exclude accounts with Product B in won opportunities\n3. Rank by recency and relationship strength\n\n**Result:**\n```\nPRODUCT A CUSTOMERS WITHOUT PRODUCT B:\n- 23 accounts identified\n- Average fit score: 78%\n- Top 5 recommendations with outreach scripts provided\n```\n\n## Troubleshooting\n\n### Error: Product not found\n**Cause:** Product name doesn't match exactly or product is inactive\n**Solution:**\n- Search with partial name match (LIKE '%name%')\n- Check statecode to include active products only\n- List available products for user to select\n\n### Error: No customers found for target product\n**Cause:** New product with no sales history yet\n**Solution:**\n- Use similar product's customer base as proxy\n- Define ideal customer profile manually\n- Start with industry\u002Fsize matching only\n\n### Error: Too few differentiation signals\n**Cause:** Winning customers too diverse for clear pattern\n**Solution:**\n- Increase sample size (longer date range)\n- Focus on top-performing accounts only\n- Add firmographic filters (industry, size)\n",{"data":37,"body":41},{"name":4,"description":6,"metadata":38},{"author":24,"version":39,"category":40},"1.0.0","sales-analytics",{"type":42,"children":43},"root",[44,52,58,65,72,77,91,104,116,123,133,142,147,157,166,174,182,191,199,208,216,225,233,242,248,253,262,268,276,281,286,291,300,305,313,318,326,412,420,495,503,579,585,595,603,612,622,631,639,663,673,682,687,692,710,716,724,733,739,747,756,764,773,779,954,960,968,1037,1045,1133,1141,1209,1217,1297,1303,1366,1372,1378,1388,1396,1424,1432,1441,1447,1456,1463,1486,1493,1502,1508,1517,1524,1542,1549,1558,1564,1570,1585,1603,1609,1622,1640,1646,1659],{"type":45,"tag":46,"props":47,"children":48},"element","h1",{"id":4},[49],{"type":50,"value":51},"text","Cross-Sell Target Identifier",{"type":45,"tag":53,"props":54,"children":55},"p",{},[56],{"type":50,"value":57},"When launching a new product or looking to expand product adoption, sales teams need to identify which existing customers are most likely to purchase. This skill analyzes the characteristics of successful customers for a given product, finds similar customers who don't own it yet, and provides prioritized recommendations with justification.",{"type":45,"tag":59,"props":60,"children":62},"h2",{"id":61},"instructions",[63],{"type":50,"value":64},"Instructions",{"type":45,"tag":66,"props":67,"children":69},"h3",{"id":68},"step-1-identify-the-target-product",[70],{"type":50,"value":71},"Step 1: Identify the Target Product",{"type":45,"tag":53,"props":73,"children":74},{},[75],{"type":50,"value":76},"When user asks \"Which customers should I pitch Product X to?\":",{"type":45,"tag":78,"props":79,"children":80},"ol",{},[81],{"type":45,"tag":82,"props":83,"children":84},"li",{},[85],{"type":45,"tag":86,"props":87,"children":88},"strong",{},[89],{"type":50,"value":90},"Identify the Product:",{"type":45,"tag":92,"props":93,"children":97},"pre",{"className":94,"code":96,"language":50},[95],"language-text","SELECT productid, name, description, producttypecode, productstructure\nFROM product\nWHERE name LIKE '%[product name]%'\nAND statecode = 0\n",[98],{"type":45,"tag":99,"props":100,"children":102},"code",{"__ignoreMap":101},"",[103],{"type":50,"value":96},{"type":45,"tag":78,"props":105,"children":107},{"start":106},2,[108],{"type":45,"tag":82,"props":109,"children":110},{},[111],{"type":45,"tag":86,"props":112,"children":113},{},[114],{"type":50,"value":115},"Confirm with user if multiple matches",{"type":45,"tag":117,"props":118,"children":120},"h4",{"id":119},"step-2-analyze-successful-product-x-customers",[121],{"type":50,"value":122},"Step 2: Analyze Successful Product X Customers",{"type":45,"tag":53,"props":124,"children":125},{},[126,131],{"type":45,"tag":86,"props":127,"children":128},{},[129],{"type":50,"value":130},"2.1 Find Customers Who Own Product X",{"type":50,"value":132},"\nQuery won opportunities that included the target product:",{"type":45,"tag":92,"props":134,"children":137},{"className":135,"code":136,"language":50},[95],"SELECT op.opportunityid, op.customerid, op.accountid, op.actualvalue,\n       op.actualclosedate, op.salesstage\nFROM opportunity op\nJOIN opportunityproduct opp ON op.opportunityid = opp.opportunityid\nWHERE opp.productid = '[target_product_id]'\nAND op.statecode = 1\n",[138],{"type":45,"tag":99,"props":139,"children":140},{"__ignoreMap":101},[141],{"type":50,"value":136},{"type":45,"tag":53,"props":143,"children":144},{},[145],{"type":50,"value":146},"Note: A product may appear in multiple opportunityproduct rows per opportunity. Deduplicate opportunityids programmatically after fetching results.",{"type":45,"tag":53,"props":148,"children":149},{},[150,155],{"type":45,"tag":86,"props":151,"children":152},{},[153],{"type":50,"value":154},"2.2 Build Success Profile from Winning Accounts",{"type":50,"value":156},"\nFor each winning account, gather firmographic data:",{"type":45,"tag":92,"props":158,"children":161},{"className":159,"code":160,"language":50},[95],"SELECT accountid, name, industrycode, numberofemployees, revenue,\n       customertypecode, address1_stateorprovince, address1_country,\n       ownershipcode, createdon\nFROM account\nWHERE accountid IN ([list of winning account ids])\n",[162],{"type":45,"tag":99,"props":163,"children":164},{"__ignoreMap":101},[165],{"type":50,"value":160},{"type":45,"tag":53,"props":167,"children":168},{},[169],{"type":45,"tag":86,"props":170,"children":171},{},[172],{"type":50,"value":173},"2.3 Analyze Success Patterns",{"type":45,"tag":53,"props":175,"children":176},{},[177],{"type":45,"tag":86,"props":178,"children":179},{},[180],{"type":50,"value":181},"Firmographic Analysis:",{"type":45,"tag":92,"props":183,"children":186},{"className":184,"code":185,"language":50},[95],"Calculate distribution across successful customers:\n- Industry breakdown (industrycode): Which industries buy most?\n- Company size (numberofemployees): What's the typical range?\n- Revenue range: What's the typical revenue bracket?\n- Geography (address1_stateorprovince\u002Fcountry): Regional concentrations?\n- Customer type (customertypecode): Are they customers, partners, etc.?\n",[187],{"type":45,"tag":99,"props":188,"children":189},{"__ignoreMap":101},[190],{"type":50,"value":185},{"type":45,"tag":53,"props":192,"children":193},{},[194],{"type":45,"tag":86,"props":195,"children":196},{},[197],{"type":50,"value":198},"Existing Product 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Use when user asks \"which accounts are at risk\", \"churn risk analysis\", \"find accounts that might leave\", \"customer health check\", \"at-risk customers\", \"retention warning signs\", or \"account health score\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1702,1703,1704,1705,1708],{"name":15,"slug":16,"type":13},{"name":24,"slug":25,"type":13},{"name":9,"slug":8,"type":13},{"name":1706,"slug":1707,"type":13},"Risk Assessment","risk-assessment",{"name":18,"slug":19,"type":13},"2026-04-06T18:36:28.462732",{"slug":1711,"name":1711,"fn":1712,"description":1713,"org":1714,"tags":1715,"stars":26,"repoUrl":27,"updatedAt":1722},"competitive-intelligence","generate competitive intelligence from sales data","Analyzes opportunity data and activity notes to generate competitive intelligence including win\u002Floss rates by competitor, patterns, and rep-ready battlecard talking points. Use when user asks \"how are we doing against [competitor]\", \"competitive analysis\", \"competitor win rate\", \"battlecard for [competitor]\", \"competitive landscape\", \"why are we losing to [competitor]\", or \"competitor intelligence\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1716,1718,1719,1720,1721],{"name":1717,"slug":1711,"type":13},"Competitive Intelligence",{"name":15,"slug":16,"type":13},{"name":24,"slug":25,"type":13},{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},"2026-04-06T18:36:23.280253",{"slug":1724,"name":1724,"fn":1725,"description":1726,"org":1727,"tags":1728,"stars":26,"repoUrl":27,"updatedAt":1738},"create-an-asset","generate customized sales assets from Dataverse","Generates customized sales assets including one-pagers, proposals, executive summaries, ROI summaries, and mutual action plans from Dataverse context. Use when user says \"create a one-pager\", \"draft a proposal\", \"generate executive summary\", \"build ROI summary\", \"create mutual action plan\", \"sales asset for [account]\", \"proposal outline\", or \"customer-facing document\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1729,1732,1733,1736,1737],{"name":1730,"slug":1731,"type":13},"Content Creation","content-creation",{"name":24,"slug":25,"type":13},{"name":1734,"slug":1735,"type":13},"Marketing","marketing",{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},"2026-04-06T18:36:24.562421",{"slug":4,"name":4,"fn":5,"description":6,"org":1740,"tags":1741,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1742,1743,1744,1745,1746],{"name":21,"slug":22,"type":13},{"name":15,"slug":16,"type":13},{"name":24,"slug":25,"type":13},{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"slug":1748,"name":1748,"fn":1749,"description":1750,"org":1751,"tags":1752,"stars":26,"repoUrl":27,"updatedAt":1758},"daily-briefing","prepare daily business briefings from Dataverse","Delivers a prioritized morning summary covering today's meetings, overdue tasks, pipeline alerts, and recommended actions from Dataverse. Use when user says \"what's on my plate today\", \"daily briefing\", \"morning summary\", \"what do I need to focus on today\", \"start my day\", \"daily digest\", \"today's priorities\", or \"what's happening today\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1753,1754,1755,1756,1757],{"name":15,"slug":16,"type":13},{"name":24,"slug":25,"type":13},{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"name":1693,"slug":1694,"type":13},"2026-04-06T18:36:31.028078",{"slug":1760,"name":1760,"fn":1761,"description":1762,"org":1763,"tags":1764,"stars":26,"repoUrl":27,"updatedAt":1775},"draft-outreach","draft personalized sales outreach from Dataverse","Generates personalized outreach messages by pulling context from Dataverse records. Creates tailored emails for new prospects, re-engagement, follow-ups, or cross-sell. Use when user says \"draft an email to [contact]\", \"write outreach for\", \"help me reach out to\", \"compose email\", \"re-engage this contact\", \"follow-up email\", \"prospecting email\", or \"outreach message for\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1765,1766,1767,1770,1771,1774],{"name":15,"slug":16,"type":13},{"name":24,"slug":25,"type":13},{"name":1768,"slug":1769,"type":13},"Email","email",{"name":9,"slug":8,"type":13},{"name":1772,"slug":1773,"type":13},"Outreach","outreach",{"name":18,"slug":19,"type":13},"2026-04-06T18:36:36.103544",16,{"items":1778,"total":1971},[1779,1801,1822,1841,1856,1873,1884,1897,1912,1927,1946,1959],{"slug":1780,"name":1780,"fn":1781,"description":1782,"org":1783,"tags":1784,"stars":1798,"repoUrl":1799,"updatedAt":1800},"rushstack-best-practices","manage Rush monorepos with best practices","Provides best practices and guidance for working with Rush monorepos. Use when the user is working in a Rush-based repository, asks about Rush commands (install, update, build, rebuild), needs help with project selection, dependency management, build caching, subspace configuration, or troubleshooting Rush-specific issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1785,1788,1791,1792,1795],{"name":1786,"slug":1787,"type":13},"Engineering","engineering",{"name":1789,"slug":1790,"type":13},"Local Development","local-development",{"name":9,"slug":8,"type":13},{"name":1793,"slug":1794,"type":13},"Project Management","project-management",{"name":1796,"slug":1797,"type":13},"Rush","rush",6484,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Frushstack","2026-04-06T18:34:44.965032",{"slug":1802,"name":1802,"fn":1803,"description":1804,"org":1805,"tags":1806,"stars":1819,"repoUrl":1820,"updatedAt":1821},"azure-ai-agents-persistent-dotnet","build AI agents with Azure .NET SDK","Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: \"PersistentAgentsClient\", \"persistent agents\", \"agent threads\", \"agent runs\", \"streaming agents\", \"function calling agents .NET\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1807,1810,1813,1816],{"name":1808,"slug":1809,"type":13},".NET","net",{"name":1811,"slug":1812,"type":13},"Agents","agents",{"name":1814,"slug":1815,"type":13},"Azure","azure",{"name":1817,"slug":1818,"type":13},"LLM","llm",2804,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fskills","2026-07-03T16:32:10.297433",{"slug":1823,"name":1823,"fn":1824,"description":1825,"org":1826,"tags":1827,"stars":1819,"repoUrl":1820,"updatedAt":1840},"azure-ai-anomalydetector-java","build anomaly detection applications with Java","Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate\u002Fmultivariate anomaly detection, time-series analysis, or AI-powered monitoring.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1828,1829,1830,1833,1836,1837],{"name":21,"slug":22,"type":13},{"name":1814,"slug":1815,"type":13},{"name":1831,"slug":1832,"type":13},"Data Analysis","data-analysis",{"name":1834,"slug":1835,"type":13},"Java","java",{"name":9,"slug":8,"type":13},{"name":1838,"slug":1839,"type":13},"Monitoring","monitoring","2026-05-13T06:14:16.261754",{"slug":1842,"name":1842,"fn":1843,"description":1844,"org":1845,"tags":1846,"stars":1819,"repoUrl":1820,"updatedAt":1855},"azure-ai-contentsafety-java","build content moderation applications with Azure AI","Build content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text\u002Fimage analysis, blocklist management, or harm detection for hate, violence, sexual content, and self-harm.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1847,1850,1851,1852],{"name":1848,"slug":1849,"type":13},"AI Infrastructure","ai-infrastructure",{"name":1814,"slug":1815,"type":13},{"name":1834,"slug":1835,"type":13},{"name":1853,"slug":1854,"type":13},"Security","security","2026-07-07T06:53:31.293235",{"slug":1857,"name":1857,"fn":1858,"description":1859,"org":1860,"tags":1861,"stars":1819,"repoUrl":1820,"updatedAt":1872},"azure-ai-contentsafety-py","detect harmful content with Azure AI Content Safety","Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.\nTriggers: \"azure-ai-contentsafety\", \"ContentSafetyClient\", \"content moderation\", \"harmful content\", \"text analysis\", \"image analysis\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1862,1863,1866,1867,1868,1871],{"name":1814,"slug":1815,"type":13},{"name":1864,"slug":1865,"type":13},"Compliance","compliance",{"name":1817,"slug":1818,"type":13},{"name":9,"slug":8,"type":13},{"name":1869,"slug":1870,"type":13},"Python","python",{"name":1853,"slug":1854,"type":13},"2026-07-18T05:14:23.017504",{"slug":1874,"name":1874,"fn":1875,"description":1876,"org":1877,"tags":1878,"stars":1819,"repoUrl":1820,"updatedAt":1883},"azure-ai-language-conversations-py","implement conversational language understanding with Python","Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1879,1880,1881,1882],{"name":21,"slug":22,"type":13},{"name":1814,"slug":1815,"type":13},{"name":1817,"slug":1818,"type":13},{"name":1869,"slug":1870,"type":13},"2026-07-31T05:54:29.068751",{"slug":1885,"name":1885,"fn":1886,"description":1887,"org":1888,"tags":1889,"stars":1819,"repoUrl":1820,"updatedAt":1896},"azure-ai-translation-text-py","translate text using Azure AI services","Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.\nTriggers: \"text translation\", \"translator\", \"translate text\", \"transliterate\", \"TextTranslationClient\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1890,1893,1894,1895],{"name":1891,"slug":1892,"type":13},"API Development","api-development",{"name":1814,"slug":1815,"type":13},{"name":9,"slug":8,"type":13},{"name":1869,"slug":1870,"type":13},"2026-07-18T05:14:16.988376",{"slug":1898,"name":1898,"fn":1899,"description":1900,"org":1901,"tags":1902,"stars":1819,"repoUrl":1820,"updatedAt":1911},"azure-ai-vision-imageanalysis-py","analyze images with Azure AI Vision","Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.\nTriggers: \"image analysis\", \"computer vision\", \"OCR\", \"object detection\", \"ImageAnalysisClient\", \"image caption\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1903,1904,1907,1910],{"name":1814,"slug":1815,"type":13},{"name":1905,"slug":1906,"type":13},"Computer Vision","computer-vision",{"name":1908,"slug":1909,"type":13},"Images","images",{"name":1869,"slug":1870,"type":13},"2026-07-18T05:14:18.007737",{"slug":1913,"name":1913,"fn":1914,"description":1915,"org":1916,"tags":1917,"stars":1819,"repoUrl":1820,"updatedAt":1926},"azure-appconfiguration-java","manage configuration with Azure App Configuration","Azure App Configuration SDK for Java. Centralized application configuration management with key-value settings, feature flags, and snapshots.\nTriggers: \"ConfigurationClient java\", \"app configuration java\", \"feature flag java\", \"configuration setting java\", \"azure config java\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1918,1919,1922,1925],{"name":1814,"slug":1815,"type":13},{"name":1920,"slug":1921,"type":13},"Configuration","configuration",{"name":1923,"slug":1924,"type":13},"Feature Flags","feature-flags",{"name":1834,"slug":1835,"type":13},"2026-07-03T16:32:01.278468",{"slug":1928,"name":1928,"fn":1929,"description":1930,"org":1931,"tags":1932,"stars":1819,"repoUrl":1820,"updatedAt":1945},"azure-cosmos-rust","build applications with Azure Cosmos DB","Azure Cosmos DB library for Rust (NoSQL API). Document CRUD, containers, and globally distributed data.\nTriggers: \"cosmos db rust\", \"CosmosClient rust\", \"document crud rust\", \"NoSQL rust\", \"partition key rust\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1933,1936,1939,1942],{"name":1934,"slug":1935,"type":13},"Cosmos DB","cosmos-db",{"name":1937,"slug":1938,"type":13},"Database","database",{"name":1940,"slug":1941,"type":13},"NoSQL","nosql",{"name":1943,"slug":1944,"type":13},"Rust","rust","2026-07-31T05:54:27.021432",{"slug":1947,"name":1947,"fn":1929,"description":1948,"org":1949,"tags":1950,"stars":1819,"repoUrl":1820,"updatedAt":1958},"azure-cosmos-ts","Azure Cosmos DB JavaScript\u002FTypeScript SDK (@azure\u002Fcosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management. Triggers: \"Cosmos DB\", \"@azure\u002Fcosmos\", \"CosmosClient\", \"document CRUD\", \"NoSQL queries\", \"bulk operations\", \"partition key\", \"container.items\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1951,1952,1953,1954,1955],{"name":1934,"slug":1935,"type":13},{"name":1937,"slug":1938,"type":13},{"name":9,"slug":8,"type":13},{"name":1940,"slug":1941,"type":13},{"name":1956,"slug":1957,"type":13},"TypeScript","typescript","2026-07-03T16:31:19.368382",{"slug":1960,"name":1960,"fn":1961,"description":1962,"org":1963,"tags":1964,"stars":1819,"repoUrl":1820,"updatedAt":1970},"azure-data-tables-java","build table storage applications with Java","Build table storage applications with Azure Tables SDK for Java. Use when working with Azure Table Storage or Cosmos DB Table API for NoSQL key-value data, schemaless storage, or structured data at scale.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1965,1966,1967,1968,1969],{"name":1814,"slug":1815,"type":13},{"name":1934,"slug":1935,"type":13},{"name":1937,"slug":1938,"type":13},{"name":1834,"slug":1835,"type":13},{"name":1940,"slug":1941,"type":13},"2026-05-13T06:14:17.582229",267]