Los objetos deben sugerir cómo se usan. Use cuando diseñe botones, controles interactivos, o cualquier elemento que invite a la acción.

Output in the specified format when a review of specific markdown is requested.

Full research pass — reads project documents, identifies agents, researches MCS components, designs architecture, enriches brief.json + generates evals. Uses Agent Teams for quality.

Generate an llms.txt file for any project or website following the llmstxt.org specification. Use when asked to create llms.txt, generate LLM-friendly documentation, make a project AI-readable, or prepare documentation for language models.

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).

Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales

Guide for creating, updating, or removing Mason bricks with corresponding tests and CI workflow (project)

ALWAYS LOAD THIS SKILL FIRST for any Dataverse task. Contains hard rules that override all other skills. USE WHEN: ANY request involving Dataverse, Dynamics 365, Power Platform, tables, columns, solutions, records, queries, CRM, metadata, plugins, SDK, Web API, PAC CLI, or environment operations. Also use for: "how do I", "what tool", "which skill", "where do I start", "help with Dataverse", "create table", "create column", "build solution", "query data", "bulk import", "sample data", "support agent", "customer table", "ticket table". This skill MUST be loaded before any other Dataverse skill. Read the Hard Rules section first.

Run dotnet test, capture failed test cases, and generate a rerun filter plus a markdown failure summary. Use when test runs fail and you need a focused rerun command or a compact failure report.

Automated Railway deployment and configuration management. Handles environment variables, service deployment, health checks, and rollback procedures.

Generate new MCP tools for GitLab operations following the project's standardized pattern. Creates complete TypeScript files with imports, registration functions, Zod schemas, error handling, and format options. Supports simple CRUD operations, complex multi-action tools, and advanced patterns like discussion management. Use when "create mcp tool", "generate gitlab tool", "new tool for", "add tool to gitlab", or building new GitLab integration features.

Implements user stories by reading requirements, following coding standards, and executing tasks with comprehensive testing. Updates story files with implementation details and test results.

Analyze failed GitHub Action jobs for a pull request.

Provides Go standard testing package expertise and best practices. Ensures proper table-driven test patterns, subtests organization, benchmark implementation, and test coverage strategies. Specializes in testing.T/testing.B usage, test helpers, mock interfaces, race detection, and integration testing patterns following Go conventions. Use when: writing Go test files (_test.go), implementing table-driven tests with subtests (t.Run), creating benchmark tests (testing.B), writing example tests for documentation, implementing test helpers and fixtures, mocking interfaces for unit tests, measuring code coverage with go test -cover, detecting race conditions with -race flag, or organizing integration tests in Go projects.

Evaluates and selects the best solution from multiple developer implementations using a comprehensive scoring system. Use this skill when you need to compare competing solutions, score them objectively across multiple dimensions, or select a winning implementation for integration.

Strategic automation architecture advisor. Use when users want to plan automation solutions, evaluate their tech stack (Shopify, Zoho, HubSpot, etc.), decide between n8n vs Python/Claude Code, or need guidance on production-ready automation design. Invokes plan mode for complex architectural decisions.

Create and manage specialized Claude Code subagents for task-specific workflows. Use when delegating work to specialized agents, configuring agent permissions, or understanding subagent architecture and best practices.

When working with Streamlit web apps, data dashboards, ML/AI app UIs, interactive Python visualizations, or building data science applications with Python

Reviews opportunities in the sales pipeline to identify stuck deals, missing next steps, weak qualification, and unbalanced stages. Use when user asks "how is my pipeline looking", "analyze pipeline health", "which deals are stuck", "pipeline review", "find at-risk deals", "identify stalled opportunities", or "audit my open deals".

Create or enhance frontend tasks with detailed implementation steps. Supports TWO modes: MODE A: Enhance existing task (add detailed steps to task in file) MODE B: Create new standalone task file (can contain single or multiple tasks) IMPORTANT: This skill creates MICRO-LEVEL atomic steps, NOT macro architecture plans. For architecture planning or feature scoping, use EnterPlanMode instead. Use this skill when: - Running /frontend.plan command - User asks for "atomic steps", "step-by-step plan with verification" - Have a specific task and need implementation steps - User wants to create a standalone task file (not from story) - Need granular steps: Setup (create files) → Implementation (write code) → Test (verify) DO NOT use for: - Architecture planning (use EnterPlanMode) - General development planning - Feature scoping or estimation Output: - MODE A: Update specific task section in file with Implementation Steps - MODE B: Create new standalone-{name}.md file with one or multiple tasks