Client Methods. Reference when using this Prisma feature.
prisma db pull. Reference when using this Prisma feature.
ESM Support. Reference when using this Prisma feature.
Migrates an MCP server with interactive widgets from the OpenAI Apps SDK (window.openai, text/html+skybridge) to the MCP Apps standard (@modelcontextprotocol/ext-apps), covering server-side and client-side changes.
Migrates an MCP server with interactive widgets from the OpenAI Apps SDK (window.openai, text/html+skybridge) to the MCP Apps standard (@modelcontextprotocol/ext-apps), covering server-side and client-side changes.
Complete reference tables for Superpowers modes, agents, recipes, and anti-patterns
Delegate complex tasks to Amplifier's multi-agent framework. Use when: (1) research/comparison needing multiple perspectives, (2) multi-file code projects, (3) architecture/design reviews, (4) user asks for deep/thorough work. NOT for: simple Q&A, quick edits, casual chat, anything needing <5s response. CLI: amplifier-openclaw.
4 principles for E2E testing discipline — observe first, fix in batches, expect long durations, check container state directly. NO FIXES DURING OBSERVATION RUNS.
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".
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".
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.
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".
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.
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".
Set up a machine for Dataverse development — install tools and authenticate. USE WHEN: "install PAC CLI", "install tools", "command not found", "authenticate", "pac auth", "az login", "gh auth", "winget install", "setup machine", "missing tools", "new machine setup". DO NOT USE WHEN: initializing a workspace/repo (use dataverse-init).
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".
Create or modify Dataverse tables, columns, relationships, forms, and views. USE WHEN: "add column", "create table", "add relationship", "lookup column", "create form", "create view", "modify form", "FormXml", "SavedQuery", "option set", "picklist", "MetadataService", "EntityDefinitions". DO NOT USE WHEN: reading/writing data records (use dataverse-python-sdk), exporting solutions (use dataverse-solution).
Configure an MCP server for GitHub Copilot or Claude with your Dataverse environment. USE WHEN: "configure MCP", "set up MCP server", "MCP not working", "connect MCP to Dataverse", "add Dataverse to Copilot", "add Dataverse to Claude", "connect via MCP", "use MCP", "MCP tools not available", "no MCP tools", "MCP not configured". DO NOT USE WHEN: workspace not initialized (use dataverse-init first), installing tools (use dataverse-setup).