Results for “structure”

Create CodeTour `.tour` files — persona-targeted, step-by-step walkthroughs with real file and line anchors. Use for onboarding tours, architecture walkthroughs, PR tours, RCA tours, and structured "explain how this works" requests.

Use when evaluating potential risks before making decisions, starting initiatives, or implementing changes. This skill provides a structured risk assessment framework for identifying, analyzing, and responding to risks across all domains.

Multi-source search for agents across web, scholar, Tavily, and Perplexity Sonar endpoints. Use when you need structured retrieval, citation-backed answers, or broad research coverage from one AIsa API key.

Scaffolds new agent skills for the dotnet/skills repository. Use when creating a new skill, generating SKILL.md files, or setting up skill directory structures. Handles frontmatter generation, section templates, and validation guidance.

Runs a structured clarifying interview for new project requests before building. Instead of writing code, it outputs a fully specified prompt.md for a fresh agent session to execute, preventing expensive mistakes.

Use this skill when the user is designing any visual system, UI, graphic, layout, brand asset, or interface and needs structured, high-quality, communication-first design decisions across typography, layout, color, imagery, and UX.

Use this when the project needs Python-specific backend architecture for APIs, services, workers, scripts, or internal tools. Trigger on requests about Python runtime structure, framework fit, module boundaries, or service organization.

Use when designing PostgreSQL tables, enums, relationships, migrations, storage metadata, or tenant-aware schema structures for a Supabase-backed app. Trigger on requests about Supabase schema planning or SQL-first data design.

Adapt and integrate a specific pattern from your references into your current codebase. Use when you've identified a pattern worth adopting and need a structured plan to integrate it with minimal disruption.

Design GraphQL APIs with well-structured schemas, efficient resolvers, pagination, and performance patterns like DataLoader and federation. Use when the user requests graphql api design or provides relevant inputs for this workflow.

Foundational .NET patterns covering runtime, project structure, dependency injection, configuration, metaprogramming, and cross-platform development. Use when working with .NET projects or CLI tools. This is the entry point for .NET development.

Generate a Lean Canvas with problem, solution, metrics, cost structure, UVP, unfair advantage, channels, segments, and revenue. Use when exploring a lean startup canvas, testing a business hypothesis, or modeling a new venture.

Profile a target (script, process, GPU, memory, interconnect) using external tools and code instrumentation. Produces structured performance reports with actionable recommendations. Use when user says "profile", "benchmark", "bottleneck", or wants performance analysis.

Select alternative execution paths when a plan step fails — branch on outcomes instead of blind retry. Load when a structured plan step fails, an unexpected observation arrives, or the user asks what to try next after an error. Also triggers on "try another approach", "route around this failure", "replan on failure", "if X fails try Y", or outcome-based branching during multi-step work. Pairs with structured-planning and debug-and-fix. Does not replace root-cause debugging — adds plan-level path selection.

Decompose tasks into structured, outcome-defined process entries with complexity triage. Load when user says "decompose this", "break this down", "what steps do I need", "plan this out", "what's the process for", "how do I approach this", or when any complex task needs structured execution planning. Includes a problem-understanding pass before complexity triage. Routes to `problem-to-plan` when the user needs planning deliverables (spec + plan + TODO). Does NOT replace brainstorming — brainstorming is design approval (upstream), this is execution planning (downstream).

Extract text, tables, key-value pairs, and structured fields from documents, receipts, invoices, and IDs using Azure AI Document Intelligence SDK for Java.

Persists project context across Claude Code sessions. Auto-loads project summaries on startup, tracks session progress, and saves structured state via deterministic Node.js scripts.

Convene a four-voice council of advisors to surface structured disagreement and tradeoffs for ambiguous decisions, go/no-go calls, and multi-path choices.

Provides a structured workflow for unit, integration, E2E, and performance testing, including browser automation, code review, and quality gates.

Analyze content sequences within a single section of a page for AEM Edge Delivery Services, providing neutral descriptions without assigning block names.

Provides structured prompts for analyzing, planning, and executing .NET framework upgrades, covering project assessment, dependency management, code modernization, CI/CD updates, testing, and documentation.

Audits iOS app source code and metadata to identify App Store rejection risks and optimization opportunities, producing a structured compliance report.

Generate a complete Go MCP server project with proper structure, dependencies, and implementation using the official github.com/modelcontextprotocol/go-sdk.

Choose pricing models, set freemium thresholds, and structure enterprise pricing conversations for technical products.

Creates structured bug reports for defects found during Oracle-to-PostgreSQL migration, documenting behavioral differences with severity, root cause, and remediation steps.

Extract structured job listing data from Indeed search results, including job titles, companies, salaries, descriptions, benefits, and apply links.

Build empowered product teams that own continuous discovery and delivery, shifting from feature factories to outcome-driven innovation.

Guide product managers through structured PRD creation by orchestrating problem framing, user research synthesis, solution definition, and success criteria into a cohesive document.

Evaluate feature investments using revenue impact, cost structure, ROI, and strategic value to make data-driven build/don't-build decisions.

Drafts structured goal-loop prompts for long-running agent work with verifiable stop conditions, validation commands, and documentation requirements.

Optimize post-signup onboarding, user activation, first-run experience, and time-to-value with structured audits, flow design, and measurement plans.

Coordinates multiple research agents to gather information on a topic across GitHub, HuggingFace, papers, competitors, and best practices, then compiles findings into a structured report.

Generates a structured 6-minute two-person English product presentation script, split into unique features/target customers and market comparison/competitive advantages, with time allocations and full dialogue.

Runs a structured three-question lazy-check on significant output before submission, forcing root-cause analysis, alternative consideration, and outcome verification.

Validate SKILL.md files against a quality rubric, scoring schema and instruction structure out of 100 and reporting a pass/fail verdict with specific fixes.

Provides regulatory compliance assessments across data privacy, AI ethics, and localization laws, producing structured outputs with risk scores and remediation status.