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".
Analyzes an opportunity's current state and engagement history, compares against won deals, and recommends the most effective next action with reasoning. Use when user asks "what should I do next on this deal", "next steps for this opportunity", "how do I move this deal forward", "what's the best action for this opp", "deal coaching", or "opportunity next action".
Full research pass — reads project documents, identifies agents, researches MCS components, designs architecture, enriches brief.json + generates evals. Uses Agent Teams for quality.
Capture and classify learnings from a build/eval/fix session. Compares discoveries against existing knowledge, presents a classified table, and writes approved items to the learnings system.
Identifies potential duplicate Accounts, Contacts, or Leads in Dataverse using intelligent fuzzy matching that catches nicknames, abbreviations, phone format variations, and address similarities. Use when user says "find duplicates", "check for duplicate accounts", "are there any duplicate contacts", "duplicate detection", "clean up duplicates", "merge duplicates", or "data quality check".
Report a bug conversationally — Claude gathers details, previews, and creates a GitHub issue via gh CLI.
Run evaluation tests using eval sets. Two-mode execution: Direct Line API (auto) or MCS Native Eval via Gateway API upload + run (manual). Results written per-test to evalSets[].tests[].lastResult.
Build agent(s) in Copilot Studio using the fully API-native build stack with user-guided manual steps for OAuth connections. Reads brief.json for architecture mode (single/multi-agent).
Generates meeting briefings by aggregating account info, contacts, opportunities, cases, and activity history into structured prep documents with talking points and discovery questions. Use when user says "prep me for my call", "brief me on this account", "meeting prep", "I have a meeting with [company]", "account briefing", "customer briefing", or "prepare for customer meeting".
Submit a feature suggestion conversationally — Claude gathers details, previews, and creates a GitHub issue via gh CLI.
Use the official Microsoft Dataverse Python SDK for data operations. USE WHEN: "use the SDK", "query records", "create records", "bulk operations", "upsert", "Python script for Dataverse", "read data", "write data", "upload file", "bulk import", "CSV import", "load data", "data profiling", "data quality", "analyze data", "Jupyter notebook", "pandas", "notebook". DO NOT USE WHEN: creating forms/views (use dataverse-metadata with Web API), exporting solutions (use dataverse-solution with PAC CLI).
Guides sales reps through structured lead qualification using BANT+ methodology. Evaluates leads, suggests discovery questions, scores quality, and recommends next steps. Use when user says "qualify this lead", "help me qualify", "BANT analysis", "score this lead", "should I convert this lead", "lead qualification questions", or "assess lead quality".
Analyzes closed opportunities to identify patterns between won and lost deals. Compares sales cycles, activities, stakeholder engagement, and deal characteristics for actionable playbook insights. Use when user asks "why are we losing deals", "win loss analysis", "what makes deals win", "analyze lost opportunities", "sales pattern analysis", "compare won vs lost deals", or "improve win rate".
Post-deployment fix: analyze eval set failures, classify root causes, apply targeted fixes, and re-evaluate. For initial build iteration, use /mcs-build (which has an internal fix loop). This skill handles post-deployment edge cases and regressions.
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".
Identifies accounts showing warning signs of churn by analyzing activity trends, support cases, and engagement signals. Scores risk and prioritizes intervention targets. 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".
Creates comprehensive handoff documentation for sales reps going on vacation or transitioning deals. Generates structured briefs with deal status, activities, next steps, contacts, and risks. Use when user says "I'm going on vacation", "create handoff brief", "coverage brief for my deals", "vacation handoff", "out of office brief", "transition my deals", or "backup documentation".
Analyzes opportunity data and activity notes to generate competitive intelligence including win/loss 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".
Deploy agents from dev to prod environments. Two modes: agent-level (fast, replicate-agent.js) and solution-level (PAC CLI export/import, ALM-ready). Includes pre-deploy validation, connection mapping, post-deploy smoke test.
Initialize a new MCS agent project with folder structure and template files.