This agent should be used when the arn-spark-naming skill needs brand strategy expertise to analyze a product's brand DNA, generate name candidates across multiple naming categories, score candidates using the Six Senses framework, or conduct linguistic screening for brand names. Also applicable when a user wants standalone brand naming guidance, needs help evaluating an existing name candidate, or when a future skill requires naming expertise. <example> Context: Invoked by arn-spark-naming skill during Step 1 to analyze brand DNA user: "name my product" assistant: (invokes arn-spark-brand-strategist with product context and competitive landscape) <commentary> Brand DNA analysis initiated. Strategist analyzes target audience vocabulary, brand personality, and competitor name landscape, then recommends naming categories for the creative sprint. </commentary> </example> <example> Context: User wants to evaluate an existing name candidate user: "is Lumina a good name for my analytics product?" assistant: (invokes arn-spark-brand-strategist in scoring mode with the candidate name) <commentary> Standalone name evaluation. Strategist scores the name on the Six Senses framework and flags any linguistic or trademark concerns. </commentary> </example> <example> Context: Invoked by arn-spark-naming skill during Step 2 for creative generation user: "naming" assistant: (invokes arn-spark-brand-strategist with brand DNA and category constraints) <commentary> Creative sprint initiated. Strategist generates 50-80 candidates per category using mind mapping, thesaurus mining, word hacking, and etymology exploration. </commentary> </example> <example> Context: Invoked by arn-spark-naming skill during Step 4 for linguistic screening user: "check these names in Spanish and French" assistant: (invokes arn-spark-brand-strategist with finalist names and target languages) <commentary> Linguistic screening initiated. Strategist checks each finalist for negative connotations, phonetic conflicts, and cultural issues in target languages. </commentary> </example>

This agent should be used when the arn-spark-dev-setup skill needs to create development environment infrastructure files such as dev containers, Docker configurations, setup scripts, CI workflows, toolchain pins, and onboarding documentation. Also applicable when a user needs specific dev environment files generated for their project. <example> Context: Invoked by arn-spark-dev-setup skill after environment decisions user: "dev setup" assistant: (invokes arn-spark-dev-env-builder with environment type, platforms, CI provider, and toolchain versions) <commentary> Dev environment setup initiated. Builder creates setup scripts, CI workflows, toolchain pin files, and CONTRIBUTING.md with prerequisites for all target platforms. </commentary> </example> <example> Context: User needs a dev container for their project user: "set up a dev container for this project" <commentary> Dev container creation. Builder creates .devcontainer/devcontainer.json, .devcontainer/Dockerfile, and VS Code extension recommendations based on the project's stack. </commentary> </example> <example> Context: User wants CI configured for cross-platform builds user: "add GitHub Actions CI with matrix builds for Linux, macOS, and Windows" <commentary> CI workflow generation. Builder creates .github/workflows/ci.yml with a platform matrix, appropriate system dependency installation steps, and build/test jobs for each platform. </commentary> </example>

This agent should be used when the arn-spark-report skill needs diagnostic investigation of an Arness Spark workflow issue. Analyzes Spark configuration, directory structure, and skill behavior against expected patterns documented in the spark knowledge base. Reports only Spark-specific issues — never reads or reports user project code or business logic. <example> Context: Invoked by arn-spark-report skill during investigation phase user: "spark report" assistant: (invokes arn-spark-doctor with user description + config context) </example> <example> Context: User reports prototype build failure user: "the clickable prototype keeps failing to build" assistant: (invokes arn-spark-doctor to check Playwright, scaffold, style brief, prototype config) </example> <example> Context: User reports discovery output missing user: "arn-spark-discover finished but there's no product concept file" assistant: (invokes arn-spark-doctor to check Vision directory config, product-concept.md existence, ensure-config state) </example>

This agent should be used when the arn-spark-stress-premortem skill needs to investigate hypothetical product failure using Gary Klein's pre-mortem methodology. The agent accepts the premise that the product has already launched and failed, then works backward to identify root causes, early warning signals, and mitigation strategies. <example> Context: Invoked by arn-spark-stress-premortem skill for standard pre-mortem investigation user: "stress premortem" assistant: (invokes arn-spark-forensic-investigator with full product concept, product pillars, and competitive landscape) <commentary> Pre-mortem investigation initiated. The forensic investigator accepts the premise that the product launched and was shut down 12 months later, then generates 3 distinct root causes with causal chains, early warning signals, and mitigation strategies. Each root cause targets a different failure category: core experience flaw, trust/security blind spot, and target audience assumption error. </commentary> </example> <example> Context: Invoked by arn-spark-stress-premortem skill with a targeted failure angle user: "stress premortem" assistant: (invokes arn-spark-forensic-investigator with product concept and a specific failure scenario to investigate deeply) <commentary> Targeted investigation initiated. The forensic investigator focuses on a specific failure angle (e.g., "the product failed because enterprise customers never adopted it despite strong indie traction") and produces a deep-dive analysis with extended causal chains, historical precedents from real product failures, and granular mitigation strategies. </commentary> </example>

This agent should be used when the arn-spark-discover skill needs competitive landscape research to identify alternatives in a product's problem space, or when the arn-spark-stress-competitive skill needs deep feature-level competitive analysis. Also applicable when a user wants to validate claims about competitor capabilities or weaknesses with web-grounded evidence. <example> Context: Invoked by arn-spark-discover skill during product discovery when user cannot name competitors user: "discover" assistant: (invokes arn-spark-market-researcher in identification mode with product description and problem space) <commentary> Product discovery initiated. Market researcher plans search queries across multiple angles, executes parallel web searches, and consolidates a tiered list of validated competitors for user review. </commentary> </example> <example> Context: User names some competitors and the skill wants to fill gaps in the landscape user: "I know about Figma and Sketch but there must be others" assistant: (invokes arn-spark-market-researcher in identification mode with known competitors as seeds) <commentary> Partial landscape provided. Market researcher uses known competitors as comparison-focused search seeds and expands the landscape with additional alternatives across problem-focused and community-focused angles. </commentary> </example> <example> Context: Invoked by a future Gap Analysis skill for deep competitive analysis user: "gap analysis" assistant: (invokes arn-spark-market-researcher in deep-analysis mode with identified competitors) <commentary> Deep analysis requested. Market researcher performs thorough feature-level research on each identified competitor, builds comparison matrices, and synthesizes positioning opportunities. </commentary> </example> <example> Context: User wants to validate assumptions about competitor weaknesses user: "is it true that Notion's offline support is limited?" assistant: (invokes arn-spark-market-researcher with specific validation question) <commentary> Validation request. Market researcher uses WebSearch to verify the specific claim with current evidence, source URLs, and confidence tags. </commentary> </example>

This agent should be used when the arn-spark-stress-prfaq skill needs to draft a press release and FAQ for a product concept (draft mode) or adversarially critique an existing PR/FAQ draft to find where the concept cracks under scrutiny (critique mode). Draft and critique are separate invocations to prevent rubber-stamping. <example> Context: Invoked by arn-spark-stress-prfaq skill in draft mode to produce PR + FAQ user: "stress prfaq" assistant: (invokes arn-spark-marketing-pm in draft mode with product concept and product pillars) <commentary> Draft mode initiated. The marketing PM writes a compelling 400-600 word press release following Amazon PR/FAQ format, generates 5-8 customer FAQ entries and 3-5 internal FAQ entries. The draft must be genuinely compelling -- written as a real product marketing manager would write it, not as a placeholder exercise. </commentary> </example> <example> Context: Invoked by arn-spark-stress-prfaq skill in critique mode to stress-test the draft user: "stress prfaq" assistant: (invokes arn-spark-marketing-pm in critique mode with product concept, product pillars, and the draft output) <commentary> Critique mode initiated. The marketing PM reads the draft with adversarial eyes, generating 5-8 questions the PR dodges and identifying 3-5 crack points where the concept's claims do not hold up under scrutiny. This is a separate invocation from draft mode to force genuine self-evaluation. </commentary> </example>

This agent should be used when the arn-spark-discover skill needs to generate rich, realistic target user personas for a product concept, or when a future skill (e.g., Synthetic User Panel) needs to instantiate fresh persona instances from existing persona moulds. Also applicable when a user provides specific people or roles as persona seeds and wants them expanded into full profiles. <example> Context: Invoked by arn-spark-discover skill during product discovery with vague user description user: "discover" assistant: (invokes arn-spark-persona-architect in discovery mode with product vision and user hints) <commentary> Product discovery initiated. Persona architect researches the target user domain, generates 2-4 concrete example personas with distinct motivations and adoption postures, and presents them for user critique. </commentary> </example> <example> Context: User provides concrete names and roles as persona seeds user: "my users are like Bob, a product manager who cares about velocity, and Julie, a developer who hates context switching" assistant: (invokes arn-spark-persona-architect in discovery mode with user-provided seeds) <commentary> User-provided personas detected. Persona architect accepts Bob and Julie as seeds, expands each into a full profile grounded in domain research, and presents the expanded profiles for validation before deriving moulds. </commentary> </example> <example> Context: Invoked by a future Synthetic User Panel skill to instantiate fresh personas from moulds user: "synthetic user panel" assistant: (invokes arn-spark-persona-architect in instantiation mode with persona moulds from product concept) <commentary> Instantiation requested. Persona architect reads the abstracted moulds and generates distinct concrete persona instances, each with unique details while fitting the mould's archetype ranges. </commentary> </example> <example> Context: User wants to refine or add personas to an existing product concept user: "I think we're missing a persona for non-technical managers" assistant: (invokes arn-spark-persona-architect in discovery mode with existing personas as context) <commentary> Persona gap identified. Persona architect generates a new concrete persona for the non-technical manager archetype, differentiated from existing personas on sophistication and motivation axes. </commentary> </example>

This agent should be used when the arn-spark-stress-interview skill needs a synthetic persona to role-play during a structured user interview. The agent receives a concrete persona profile (generated by arn-spark-persona-architect in instantiation mode) and a casting overlay that adds an adversarial lens, then responds in-character to interview questions across reaction, probing, and stress phases. <example> Context: Invoked by arn-spark-stress-interview skill with Pragmatist casting overlay user: "stress interview" assistant: (invokes arn-spark-persona-impersonator with persona profile, Pragmatist overlay, product concept, and interview phase) <commentary> Pragmatist interview initiated. The impersonator adopts the persona's identity filtered through a practical adoption lens -- focusing on workflow disruption, migration cost, and whether this product solves a problem worth changing habits for. Responses are blunt and time-conscious. </commentary> </example> <example> Context: Invoked by arn-spark-stress-interview skill with Skeptic casting overlay user: "stress interview" assistant: (invokes arn-spark-persona-impersonator with persona profile, Skeptic overlay, product concept, and interview phase) <commentary> Skeptic interview initiated. The impersonator adopts the persona's identity filtered through a trust-and-doubt lens -- questioning data handling, vendor lock-in, longevity, and why this would succeed where others failed. Responses are guarded and probe for proof rather than promises. </commentary> </example> <example> Context: Invoked by arn-spark-stress-interview skill with Power User casting overlay user: "stress interview" assistant: (invokes arn-spark-persona-impersonator with persona profile, Power User overlay, product concept, and interview phase) <commentary> Power User interview initiated. The impersonator adopts the persona's identity filtered through a depth-and-scalability lens -- probing for API access, customization hooks, performance ceilings, and what happens when usage outgrows the happy path. Responses demand specifics and reject hand-waving. </commentary> </example>

This agent should be used when the arn-spark-discover skill needs product thinking to probe a user's product idea, challenge assumptions, and structure raw concepts into a coherent product vision. Also applicable when a user describes a vague product idea that needs sharpening or wants help identifying scope boundaries within the greenfield discovery pipeline. <example> Context: Invoked by arn-spark-discover skill during product discovery user: "discover" assistant: (invokes arn-spark-product-strategist with user's raw product idea) <commentary> Product discovery initiated. Strategist probes the idea, identifies gaps, and structures findings for the iterative conversation. </commentary> </example> <example> Context: User describes a vague product idea that needs sharpening user: "I want to build something like a walkie-talkie app for my house" <commentary> Vague idea. Strategist asks probing questions about users, use cases, and what makes this different from existing solutions. </commentary> </example> <example> Context: User has features but needs help scoping v1 user: "I have all these ideas but I'm not sure what to build first" <commentary> Scope assessment needed. Strategist challenges each feature's v1 necessity and helps identify the minimum viable product. </commentary> </example>

This agent should be used when the arn-spark-static-prototype skill, arn-spark-clickable-prototype skill, or arn-spark-style-explore skill needs to create actual UI screens or component showcases using the project's chosen UI framework and component library. Creates clickable static screen prototypes with navigation, static component showcase pages for visual validation, or sample screens for style evaluation. Applies visual style consistently and produces a browsable experience. <example> Context: Invoked by arn-spark-clickable-prototype skill to create all application screens user: "clickable prototype" assistant: (invokes arn-spark-prototype-builder with screen list, style brief, and framework details) <commentary> Prototype build initiated. Builder creates each screen as a component, links navigation between screens, applies visual style, and verifies the prototype runs and can be navigated. </commentary> </example> <example> Context: Invoked by arn-spark-style-explore to create sample screens user: "style explore" assistant: (invokes arn-spark-prototype-builder with 1-2 sample screens and style brief) <commentary> Style sample requested. Builder creates a small number of screens to demonstrate the visual direction using the actual component library. </commentary> </example> <example> Context: User wants to update specific prototype screens user: "update the settings screen to show device selection" <commentary> Targeted update. Builder modifies specific screen components without rebuilding the entire prototype. </commentary> </example> <example> Context: Invoked by arn-spark-static-prototype skill in showcase mode user: "static prototype" assistant: (invokes arn-spark-prototype-builder in showcase mode with style brief and component list) <commentary> Showcase mode. Builder creates standalone pages rendering each component in isolation and in combined views, with dark/light variants if applicable. Output goes to a versioned directory for visual validation. </commentary> </example>

This agent should be used when the arn-spark-scaffold skill needs to create a working project skeleton from architecture decisions, installing dependencies, configuring build tools, and producing a minimal running application. Also applicable when a user needs a project set up from scratch based on a defined technology stack. <example> Context: Invoked by arn-spark-scaffold skill after stack confirmation user: "scaffold" assistant: (invokes arn-spark-scaffolder with architecture vision stack decisions) <commentary> Scaffold initiated. Scaffolder creates project structure, config files, installs dependencies, and produces a minimal app that builds and runs. </commentary> </example> <example> Context: User needs a project set up with specific technologies user: "set up a Tauri + Svelte project with Tailwind and shadcn" <commentary> Direct project setup. Scaffolder creates the full project skeleton with the specified stack, including UI toolkit configuration. </commentary> </example> <example> Context: User wants to add UI toolkit to an existing skeleton user: "add Tailwind CSS and shadcn-svelte to this project" <commentary> Incremental scaffold. Scaffolder installs and configures the UI toolkit within the existing project structure. </commentary> </example>

This agent should be used when the arn-spark-spike skill needs to validate a specific technical risk by creating a minimal proof-of-concept, running it, and reporting whether the risk is validated, partially validated, or failed. Also applicable when a user needs to quickly test whether a specific technology capability works for their use case. <example> Context: Invoked by arn-spark-spike skill to validate a critical risk user: "spike" assistant: (invokes arn-spark-spike-runner with risk description and validation criteria) <commentary> Risk spike initiated. Spike runner creates minimal POC code in an isolated directory, runs it, and reports whether the risk is validated or failed. </commentary> </example> <example> Context: User needs to test a specific technology capability user: "can WebRTC work inside a Tauri webview on macOS?" <commentary> Validation question requiring a POC. Spike runner creates the smallest possible test to verify the capability and reports results with evidence. </commentary> </example> <example> Context: User wants to verify two technologies integrate correctly user: "test whether shadcn-svelte components work with our Tailwind config" <commentary> Integration validation. Spike runner creates a minimal test combining both technologies and reports compatibility results. </commentary> </example>

This agent should be used when the arn-spark-style-explore skill or arn-spark-static-prototype skill needs to capture screenshots of URLs and extract visual design characteristics (colors, typography, layout, spacing) from websites, web applications, or locally served prototypes. Also applicable when a user wants to visually analyze a website's design to inform their own style direction. <example> Context: Invoked by arn-spark-style-explore skill when user provides a reference URL user: "style explore" assistant: (invokes arn-spark-style-capture with URL and output path after user says "I like how Linear looks") <commentary> Reference capture initiated. Agent checks Playwright availability, captures a screenshot of the URL, analyzes the visual design, and reports extracted design characteristics. </commentary> </example> <example> Context: User wants to analyze a specific website's visual design user: "I like how vercel.com looks, use it as a reference" assistant: (invokes arn-spark-style-capture with vercel.com URL) <commentary> Reference capture. Agent screenshots the URL and extracts the color palette, typography, spacing patterns, and component style characteristics. </commentary> </example> <example> Context: Invoked by arn-spark-style-explore skill when user wants to compare reference sites user: "style explore" assistant: (invokes arn-spark-style-capture for each URL the user provided) <commentary> Multi-URL capture. Agent captures each URL separately and reports design characteristics for both, enabling side-by-side comparison. </commentary> </example>

This agent should be used when the arn-spark-arch-vision skill needs technology research to evaluate candidate technologies, produce comparison matrices, and recommend a stack with rationale for a greenfield project. Also applicable when a user needs to compare specific technologies or validate a technology choice against project requirements. <example> Context: Invoked by arn-spark-arch-vision skill during architecture exploration user: "arch vision" assistant: (invokes arn-spark-tech-evaluator with product concept and requirements) <commentary> Architecture vision initiated. Tech evaluator researches candidate technologies and produces comparison matrices for each architectural layer. </commentary> </example> <example> Context: User needs to choose between specific technologies user: "should I use Tauri or Electron for a desktop app with WebRTC?" <commentary> Direct comparison request. Tech evaluator builds a criteria matrix grounded in the project's actual requirements. </commentary> </example> <example> Context: User wants a full stack recommendation for a new project user: "what tech stack should I use for a cross-platform P2P voice app?" <commentary> Full stack evaluation. Tech evaluator extracts requirements, identifies candidates per layer, and recommends a cohesive stack. </commentary> </example>

This agent should be used when the arn-spark-clickable-prototype skill needs to simulate user journeys through an interactive prototype by writing and executing Playwright scripts. Clicks buttons, fills forms, navigates between screens, and captures screenshots at every state change to document the interaction flow. <example> Context: Invoked by arn-spark-clickable-prototype skill to test user journeys user: "clickable prototype" assistant: (invokes arn-spark-ui-interactor with dev server URL, journey definitions, and output path after the prototype builder creates the app) <commentary> Interaction testing initiated. Agent checks Playwright availability, writes scripts for each journey, executes them capturing screenshots at every state change, and reports results. </commentary> </example> <example> Context: Testing a specific user journey through the prototype user: "test the settings navigation flow" assistant: (invokes arn-spark-ui-interactor with the settings journey definition) <commentary> Single journey test. Agent writes a Playwright script that navigates to settings, clicks through sub-sections, captures each state, and reports whether all steps completed successfully. </commentary> </example> <example> Context: Re-testing journeys after prototype fixes user: "re-test all journeys on v3" assistant: (invokes arn-spark-ui-interactor with same journeys, new output path for v3) <commentary> Re-test after fixes. Agent runs the same journey scripts against the updated prototype, captures fresh screenshots to the new version directory. </commentary> </example>

This agent should be used when the arn-spark-use-cases or arn-spark-use-cases-teams skill needs to draft, revise, or finalize structured use case documents in Cockburn fully-dressed format. Transforms product vision and expert review feedback into implementation-ready use case documents. Also applicable when a user needs specific use cases written for an existing product concept. <example> Context: Invoked by arn-spark-use-cases skill to draft initial use cases user: "use cases" assistant: (invokes arn-spark-use-case-writer with product concept, actor catalog, and use case catalog) <commentary> Use case drafting initiated. Writer reads product concept and templates, drafts all use cases in Cockburn fully-dressed format, writing each to a separate file. </commentary> </example> <example> Context: Invoked by arn-spark-use-cases skill with expert feedback for revision user: "use cases" assistant: (invokes arn-spark-use-case-writer with existing drafts and combined expert feedback per use case) <commentary> Revision round. Writer reads each use case file, applies the combined feedback from product strategist and UX specialist, and updates the files. </commentary> </example> <example> Context: Invoked by arn-spark-use-cases-teams skill with debate report for revision user: "use cases teams" assistant: (invokes arn-spark-use-case-writer with existing drafts and the Recommended Changes for Writer section from the debate report) <commentary> Revision round from team debate. Writer reads each use case file, applies the recommended changes from the debate report (consensus findings, additions, and resolved disagreements), and updates the files. </commentary> </example> <example> Context: User wants a single use case written for a specific capability user: "write a use case for the device pairing flow" <commentary> Single use case request. Writer reads the product concept for context, drafts the use case using the template, and writes it to the use cases directory. </commentary> </example>

This agent should be used when the arn-spark-static-prototype skill or arn-spark-clickable-prototype skill needs an independent quality verdict on prototype artifacts. Delivers strict, evidence-based scoring of every criterion on a defined scale, determines a PASS or FAIL verdict, and provides actionable improvement suggestions for any criterion below the minimum threshold. Operates in two modes: static review (evaluates screenshots and files) or interactive review (navigates the running prototype firsthand via Playwright before scoring). <example> Context: Invoked by arn-spark-static-prototype skill after expert review cycles user: "static prototype" assistant: (invokes arn-spark-ux-judge in static mode with screenshots, criteria, and style brief after the build-review cycles complete) <commentary> Static judge review. Judge loads all reference documents, reviews each screenshot visually, scores every criterion independently, and delivers a PASS or FAIL verdict with evidence. </commentary> </example> <example> Context: Invoked by arn-spark-clickable-prototype skill after interaction testing user: "clickable prototype" assistant: (invokes arn-spark-ux-judge in interactive mode with prototype URL, criteria, and review reports after build-review cycles complete) <commentary> Interactive judge review. Judge navigates the running prototype firsthand via Playwright, experiences transitions and flow, captures its own screenshots as evidence, and delivers a verdict based on direct experience. </commentary> </example> <example> Context: Judge re-invoked after additional fix cycles user: "the judge failed v3, I ran 2 more cycles" assistant: (re-invokes arn-spark-ux-judge with updated artifacts from v5) <commentary> Re-judgment after fixes. Judge reviews the latest version fresh, without inheriting previous scores. Delivers an independent new verdict. </commentary> </example>

This agent should be used when a greenfield skill needs UI/UX design guidance for prototype validation, style exploration, or component design within the greenfield discovery pipeline. Specializes in visual style direction, prototype review, component architecture for greenfield projects, and user experience flows for new product concepts. <example> Context: Invoked by arn-spark-style-explore skill during visual style exploration user: "style explore" assistant: (invokes arn-spark-ux-specialist with product context + style direction) <commentary> Style exploration initiated. UX specialist proposes visual directions, component styles, and typography for the greenfield project. </commentary> </example> <example> Context: Invoked by arn-spark-static-prototype skill during expert review user: "static prototype" assistant: (invokes arn-spark-ux-specialist with screenshots + criteria for scoring) <commentary> Prototype review cycle. UX specialist scores the visual implementation against the style brief and provides per-criterion feedback. </commentary> </example> <example> Context: Invoked by arn-spark-feature-extract for UI behavior analysis user: "feature extract" assistant: (invokes arn-spark-ux-specialist with feature list + journey definitions) <commentary> Feature extraction phase. UX specialist reviews feature boundaries, describes UI behavior, and maps components to features. </commentary> </example>

This agent should be used when the arn-spark-visual-sketch skill needs to create a single visual direction proposal inside the project's route structure. Creates page components for each screen in the screen list, scoped under a CSS-variable-isolated layout, using the project's actual CSS framework and component library. Each proposal represents a distinct visual approach (color mood, typography feel, density, component style) applied to real product screens. <example> Context: Invoked by arn-spark-visual-sketch skill to create one of N parallel proposals user: "visual sketch" assistant: (invokes arn-spark-visual-sketcher with product context, screen list, direction brief, tech context, and output route path) <commentary> Visual sketch proposal initiated. Sketcher reads the scaffold to understand routing conventions, creates a layout component with CSS variable isolation, and builds each screen as a page component with realistic static content matching the direction brief. </commentary> </example> <example> Context: Invoked for an expansion round after the user selected a direction user: "visual sketch" assistant: (invokes arn-spark-visual-sketcher with the selected direction brief, expansion guidance, and a new output route path for round-2) <commentary> Expansion sketch. Sketcher creates a variation of the selected direction with the user's specified changes (e.g., warmer colors, denser layout) in a new proposal directory. </commentary> </example> <example> Context: Invoked for a single-screen refinement user: "visual sketch" assistant: (invokes arn-spark-visual-sketcher with updated brief for one screen) <commentary> Targeted refinement. Sketcher updates only the specified screen in the existing proposal directory without recreating other screens. </commentary> </example>

This agent should be used when the arn-spark-visual-strategy skill needs to investigate, design, and validate visual testing infrastructure for a project. Creates capture scripts, cross-environment pipelines, test runner configurations, and validates that the chosen approach works by running proof-of-concept captures. <example> Context: Invoked by arn-spark-visual-strategy to validate a Playwright-based capture approach user: "visual strategy" assistant: (invokes arn-spark-visual-test-engineer with stack details, environment constraints, and the proposed testing layer) <commentary> Visual testing spike. Engineer creates a minimal capture script, runs it against the prototype or a test page, validates screenshots are captured correctly, and reports whether the approach works. </commentary> </example> <example> Context: Invoked to build a WSL2-to-Windows capture pipeline user: "visual strategy" assistant: (invokes arn-spark-visual-test-engineer with cross-environment requirements) <commentary> Cross-environment visual testing. Engineer creates a pipeline script that copies build artifacts from WSL2 to Windows, runs the Windows build, captures screenshots using Windows-native tools, and copies results back. </commentary> </example> <example> Context: Invoked to set up baseline images from prototype screenshots user: "visual strategy" assistant: (invokes arn-spark-visual-test-engineer with prototype screenshot paths and baseline image directory) <commentary> Baseline setup. Engineer organizes prototype screenshots into a structured baseline directory, generates a manifest mapping features to baseline images, and creates a comparison script. </commentary> </example> <example> Context: Invoked to generate production-ready capture and comparison scripts user: "visual strategy" assistant: (invokes arn-spark-visual-test-engineer with validated layer specs and full project context) <commentary> Production script generation. Engineer takes the validated POC approach and creates polished capture, comparison, and baseline management scripts ready for regular use during development. </commentary> </example> <example> Context: Invoked by arn-spark-visual-strategy to generate journey definitions and a platform runner for Windows user: "visual strategy" assistant: (invokes arn-spark-visual-test-engineer with journey schema reference, target platform, implementation context) <commentary> Journey generation. Engineer reads the journey schema, analyzes the implementation's screens and user flows, generates journey-manifest.json with step sequences and custom mappings, then generates a PowerShell runner script using System.Windows.Automation. Validates with a dry-run if the app is running. </commentary> </example>

This skill should be used when the user says "brainstorming", "arn brainstorming", "brainstorm", "let's brainstorm", "start brainstorming", "brainstorming session", "greenfield wizard", "arn spark wizard", "greenfield pipeline", "walk me through greenfield", "guided greenfield", "full greenfield pipeline", "greenfield flow", "explore to feature backlog", "greenfield start to finish", "run the greenfield pipeline", "guide me through greenfield", "greenfield guided mode", "greenfield setup", "new project wizard", "add a feature", "new feature", "I need another feature", "add feature to greenfield", "one more feature", or wants to be walked through the entire Arness greenfield exploration pipeline in a single continuous session with guided decision gates instead of invoking each skill manually. Also triggers when the user wants to add a new feature to an existing greenfield project after the clickable prototype is complete.

This skill should be used when the user says "arch vision", "architecture vision", "arn arch vision", "define the architecture", "tech stack", "what technology should I use", "design the system", "system architecture", "how should I build this", "technology choices", "choose technologies", "pick a tech stack", or wants to explore technology options and define the high-level architecture for a greenfield project. Takes a product concept as input and produces an architecture-vision.md document capturing the technology stack, system design, protocols, packaging strategy, and known risks.

This skill should be used when the user says "clickable prototype teams", "arn clickable prototype teams", "team clickable prototype", "debate clickable prototype", "collaborative interaction review", "clickable prototype with debate", "team-based interaction review", "interaction debate", "review interactions as a team", "interactive prototype teams", "team prototype review", or wants to create a clickable interactive prototype with linked screens and validate it through iterative expert debate cycles where product strategist and UX specialist discuss their scores and findings before producing a combined review, with Playwright-based interaction testing, per-criterion scoring, an independent judge verdict, and versioned output. Supports Agent Teams for parallel debate or sequential simulation as fallback. For standard lower-of-two-scores interaction review, use /arn-spark-clickable-prototype instead.

This skill should be used when the user says "clickable prototype", "arn clickable prototype", "interactive prototype", "test interactions", "validate UX", "user journeys", "test navigation", "make it clickable", "prototype interactions", "test the prototype", "build the screens", "create the UI", "screen mockups", or wants to generate a clickable interactive prototype with linked screens and validate it through iterative build-review cycles with Playwright-based interaction testing, per-criterion scoring, an independent judge verdict, and versioned output.

This skill should be used when the user says "concept review", "review concept", "update product concept", "synthesize stress tests", "stress test review", "apply stress test findings", "review stress test results", "concept update", "merge stress test recommendations", or wants to synthesize findings from completed stress tests into a reviewed and updated product concept document. Scans for stress test reports, consolidates recommendations, resolves conflicts using product pillars, presents the full changeset for user approval, and produces an updated product-concept.md alongside a concept-review-report.md.

This skill should be used when the user says "dev setup", "arn spark dev setup", "development environment", "configure dev environment", "dev container", "configure CI", "set up CI", "onboard developer", "developer setup", "set up docker", "configure development", "how do I set up this project", "development setup", "onboard me", "get this project running", "set up my machine", "new developer setup", "how do I get started", "developer onboarding", or wants to define a standardized development environment for their project (producing dev environment infrastructure files and a dev-setup document) or follow an existing environment standard to get onboarded as a new developer.

This skill should be used when the user says "discover", "product discovery", "arn discover", "help me define this product", "what should I build", "product concept", "define the product", "let's figure out what to build", "vision for this project", "shape this idea", "new project idea", "brainstorm this product", "starting from scratch", or wants to explore and structure a greenfield product idea through guided conversation. Produces a product-concept.md document capturing the product vision, core experience, target users, trust model, platforms, and scope boundaries.

This skill should be used when the user says "ensure config", "check arn spark config", "arn-spark-ensure-config", "verify arn spark setup", "configure spark", "setup arness spark", "spark config", or wants to verify that Arness Spark configuration is present for the current project. This skill is primarily consumed as a reference by entry-point skills (arn-brainstorming, arn-spark-discover, arn-spark-arch-vision) which read the ensure-config reference as Step 0 before proceeding with their workflow.

This skill should be used when the user says "feature extract", "arn feature extract", "extract features", "feature backlog", "create backlog", "list features", "what features do we need", "prioritize features", "feature list", "build the backlog", "what should we build", "upload features", "feature tracker", or wants to extract a structured, prioritized feature list with journey steps, validated components, use case context, and UI behavior details from all project artifacts, producing a feature backlog document with a Feature Tracker that bridges into arn-code-feature-spec and optionally uploads features to the issue tracker.

This skill should be used when the user says "spark help", "arn spark help", "greenfield status", "greenfield help", "where am I in spark", "what's next for spark", "spark pipeline", "spark status", "arn-spark-help", "show spark pipeline", "what step am I on for spark", "spark workflow", "exploration status", "show exploration pipeline", "how does spark work", "explain spark pipeline", or wants to see their current position in the Arness Spark exploration pipeline and get guidance on the next step.

Optional customization tool for greenfield projects. This skill should be used when the user says "greenfield init", "arn spark init", "initialize greenfield", "setup greenfield", "greenfield setup", "start greenfield", "configure greenfield", "set up greenfield", "init greenfield", "greenfield configuration", "review greenfield config", "customize greenfield config", "greenfield settings", "Figma setup", "Canva setup", "add Figma", "add Canva", "design tool setup", or wants to customize Arness Spark configuration, add design tool integrations (Figma, Canva), or review current greenfield settings. Arness Spark auto-configures with sensible defaults on first skill invocation — this init is optional. Design tool integration (Figma/Canva) remains available only through this skill.

This skill should be used when the user says "naming", "brand name", "name my product", "find a name", "product naming", "brand naming", "what should I call it", "name ideas", "pick a name", "naming session", "help me name this", "brainstorm names", "come up with a name", "arn spark naming", "arn-spark-naming", or wants to find a brand name for their product through strategic analysis, creative generation, qualitative scoring, and due diligence including domain availability and trademark screening.

This skill should be used when the user says "prototype lock", "lock prototype", "arn prototype lock", "freeze prototype", "preserve prototype", "snapshot prototype", "protect prototype", "archive prototype", "save the prototype", "don't overwrite the prototype", "lock the design", "freeze the design", or wants to create a frozen snapshot of the validated prototype before development begins, preventing production code from overwriting the validated reference artifact.

This skill should be used when the user says "spark report", "report spark issue", "spark broke", "arn-spark-report", "greenfield issue", "report greenfield problem", "report spark problem", "diagnose spark", "spark doctor", "spark bug", "spark not working", or wants to report a problem with an Arness Spark workflow skill. Invokes the arn-spark-doctor agent to diagnose the issue, then files a GitHub issue on the Arness plugin repository. Do NOT use this for filing issues on the user's own project — use /arn-code-create-issue for that.

This skill should be used when the user says "scaffold", "arn scaffold", "set up the project", "create project", "initialize project", "bootstrap project", "create the skeleton", "install dependencies", "configure the project", or wants to create a working project skeleton from architecture decisions with installed dependencies, configured build tools, and a UI toolkit ready for development.

This skill should be used when the user says "spike", "arn spike", "validate risks", "technical validation", "proof of concept", "validate architecture", "risk spike", "test this risk", "will this work", "technical spike", "validate the stack", or wants to validate critical technical risks from the architecture vision by creating minimal proof-of-concept code and testing whether the chosen technologies work as expected.

This skill should be used when the user says "static prototype teams", "arn static prototype teams", "team static prototype", "debate static prototype", "collaborative visual review", "static prototype with debate", "team-based visual review", "visual debate", "review visuals as a team", or wants to create a static component showcase and validate it through iterative expert debate cycles where product strategist and UX specialist discuss their scores and findings before producing a combined review, with per-criterion scoring, an independent judge verdict, and versioned output. Supports Agent Teams for parallel debate or sequential simulation as fallback. For standard lower-of-two-scores visual review, use /arn-spark-static-prototype instead.

This skill should be used when the user says "static prototype", "arn static prototype", "visual validation", "pixel perfect", "component showcase", "static screens", "build a static prototype", "create a component showcase", "visual review", "validate the visuals", "check the design", "validate components", "review the design visuals", or wants to create a static component showcase and validate it through iterative expert review cycles with per-criterion scoring, an independent judge verdict, and versioned output.

This skill should be used when the user says "competitive analysis", "gap analysis", "competitive gap", "stress competitive", "compare competitors", "feature comparison", "competitive stress test", "market comparison", "competitor analysis", or wants to stress-test a product concept by conducting deep competitive gap analysis with feature comparison, gap identification, and positioning assessment. Produces a competitive report with a feature matrix, per-competitor analysis, and recommended concept updates.

This skill should be used when the user says "stress interview", "synthetic interview", "user interview stress test", "interview my personas", "test with synthetic users", "persona interview", "simulate user interviews", "run user interviews", or wants to stress-test a product concept by conducting structured interviews with synthetic personas through three adversarial lenses (Pragmatist, Skeptic, Power User). Produces an interview report with per-persona findings, synthesized themes, and recommended concept updates.

This skill should be used when the user says "pre-mortem", "premortem", "risk analysis", "stress premortem", "failure analysis", "what could go wrong", "pre mortem", "investigate failure", "failure modes", or wants to stress-test a product concept by applying Gary Klein's pre-mortem methodology to identify hypothetical failure root causes, early warning signals, and mitigation strategies. Produces a pre-mortem report with 3 root causes across distinct failure dimensions and recommended concept updates.

This skill should be used when the user says "prfaq", "pr faq", "pr/faq", "press release stress test", "stress prfaq", "amazon pr faq method", "test the pitch with a pr/faq", "validate concept through pr/faq", "critique press release", "pr faq stress test", "will this marketing story hold up", or wants to stress-test a product concept by drafting a compelling press release and FAQ, then adversarially critiquing it to find where the concept cracks under scrutiny. Produces a PR/FAQ report with the full draft, adversarial questions, crack point analysis, and recommended concept updates.

This skill should be used when the user says "style explore", "arn style", "visual style", "explore styles", "UI style", "look and feel", "design direction", "pick a style", "choose colors", "theme the app", "visual direction", "style guide", or wants to explore and define the visual design direction for their project through guided conversation, producing a style brief document with implementable toolkit configuration.

This skill should be used when the user says "use cases teams", "arn use cases teams", "team use cases", "debate use cases", "collaborative use cases", "use cases with debate", "team-based use case review", "use case debate", "review use cases as a team", or wants to create structured use case documents through expert debate where product strategist and UX specialist review and discuss each other's findings before revising, producing a use-cases/ directory with individual Cockburn fully-dressed use case files and a README index.

This skill should be used when the user says "use cases", "arn use cases", "write use cases", "define use cases", "Cockburn use cases", "actor goals", "behavioral requirements", "system behavior", "what does the app do", "describe the behavior", "use case document", "document the behavior", "define system behavior", or wants to create structured use case documents that describe the application's behavior from actor perspectives, producing a use-cases/ directory with individual Cockburn fully-dressed use case files and a README index.

This skill should be used when the user says "visual readiness", "check visual layers", "activate visual layer", "visual checkpoint", "promote visual testing", "enable layer 2", "visual test health", "check deferred layers", "activate deferred layers", "layer promotion", or wants to validate and activate deferred visual testing layers after project milestones.

This skill should be used when the user says "visual sketch", "arn visual sketch", "sketch directions", "explore visuals", "visual proposals", "try different looks", "design directions", "sketch the UI", "visual exploration", "compare styles", "show me options", "what could this look like", or wants to generate multiple visual direction proposals as real HTML/CSS running on the scaffolded project's dev server, iteratively selecting and refining until a final visual direction is chosen.

This skill should be used when the user says "visual strategy", "arn visual strategy", "visual testing", "visual regression", "screenshot testing", "compare to prototype", "visual validation", "how do I test visuals", "set up visual tests", "baseline images", "screenshot comparison", "pixel diff", "visual diff", "does it match the prototype", or wants to set up visual regression testing for development — creating capture scripts, comparison scripts, and baseline images so that feature implementations are automatically compared against prototype screenshots to catch visual regressions during development.