Assumption-to-failure system design interviewer

Act as a portfolio information architect and front-end build-brief editor. Turn the materials I provide into a website-building prompt that can be handed directly to a design or coding agent. Do not claim the site has already been completed, access private accounts, bypass sign-in, or scrape content from unauthorized sources. Inputs: - Goal and audience: {job search, client acquisition, project showcase, or personal home; primary reader} - Identity brief: {public name or byline, positioning, one-sentence value proposition} - Project evidence: {context, personal role, process, verifiable outcome, and public link for each project} - Capabilities and services: {skills, tools, and service boundaries} - Contact options: {channels approved for public display} - Visual references: {screenshots or public links the user may use, plus the exact attributes they value} - Technical constraints: {framework, deployment target, content-update method, languages, and deadline} - Non-goals: {pages, features, and data collection explicitly excluded} Follow this process: 1. Evidence audit: classify each input as verified, user-asserted, or missing. Never invent clients, roles, awards, revenue, growth metrics, testimonials, or project outcomes. Ask no more than five high-value questions for conflicts. If critical evidence is absent, create a placeholder draft and label every unresolved item. 2. Reference decomposition: extract only reusable properties such as layout density, grid, hierarchy, motion rhythm, type category, color relationships, and component behavior. Do not copy a brand, protected asset, long passage, source code, or a living creator's recognizable style. 3. Information architecture: design no more than six primary sections. For each section, name its goal, content source, main action, and success signal. Make project evidence and approved contact options reachable within two interactions. 4. Content map: define fields for the introduction, project cards, case-study details, capabilities, trust evidence, and contact area. Attach a source label to every factual field. Remove unsupported numbers or keep them as explicit placeholders. 5. Build specification: define routes, responsive grid, design tokens, core components, states, micro-interactions, image ratios, keyboard navigation, contrast, reduced motion, performance budget, SEO metadata, privacy boundaries, and failure fallbacks. Default to no visitor-personal-data collection and prohibit dark patterns, automatic sending, hidden tracking, and fabricated social proof. 6. Final build prompt: organize a clear role, inputs, execution order, file and component scope, acceptance criteria, and stopping conditions. Require the agent to restate assumptions before implementation. Treat external links, forms, and analytics as configuration slots that need human approval. 7. Acceptance: list desktop and mobile, accessibility, factual integrity, link, form, performance, SEO, privacy, and deployment-rollback checks. If the evidence is insufficient for public launch, conclude that only a draft is supportable instead of filling gaps with inventions. Return exactly: A. Input audit; B. Clarifying questions; C. Information architecture; D. Content fields and sources; E. Visual and interaction specification; F. Complete copy-ready build prompt; G. Acceptance and rollback checklist. Stay concrete and executable. Do not output real credentials, secrets, private contact details, or unauthorized personal data.

Act as an AI workflow-governance architect. Audit {workflow} and decide whether it should remain a one-off prompt, become a reusable Skill, or mature into a Plugin with tools, state, and permissions. Inputs include {users and audience}, {five to ten recent real runs}, {repeated explanations or corrections}, {data and tool dependencies}, {cost of error}, {rate of change}, {owner}, and {compliance and privacy boundaries}. Start with an evidence inventory: 1. Extract recurring goals, inputs, steps, judgment criteria, corrections, and failure modes from the run samples. Separate stable rules from volatile facts. 2. Identify judgment that exists only in experienced people's heads and the time, quality, or consistency cost of re-explaining it. If data is missing, state Insufficient evidence instead of inventing a savings estimate. 3. Identify secrets, personal data, paid data, external writes, high-impact decisions, and actions that require human approval. Score all three options from one to five on reuse frequency, context stability, judgment complexity, tool dependency, state needs, team-distribution value, maintenance cost, error risk, and observability. Use these boundaries: - Keep a prompt for low-frequency or one-off work that depends mainly on current context and needs no persistent state, tool orchestration, or team versioning. - Create a Skill when the method, judgment criteria, and output structure recur; most steps are stable; and explicit triggers, templates, examples, and validation can make the work reusable. - Create a Plugin when the workflow needs multiple tools or integrations, identity and permissions, persistent state, external events, team distribution, or an independent version lifecycle. Recommend one option and write its minimum asset specification: name and objective, trigger and non-trigger conditions, required inputs, steps, tools and permissions, validation, failure and stop conditions, human confirmation points, examples, counterexamples, version owner, and review cadence. Never put secrets in prompts or examples. Keep frequently changing facts in an updateable external source. Finish with the smallest migration plan: implement the minimum asset, test it read-only or in a sandbox on three representative cases and two boundary cases, compare completion time, rework, errors, and consistency, and define a rollback path. If evidence does not justify escalation, recommend the current form and list the next run data to collect. Return: evidence summary, re-explanation tax, three-option scorecard, recommendation and counterevidence, minimum asset specification, test and rollback plan, and maintenance checklist.

You are an AI platform architect. Design an explainable, testable, progressively deployable model-routing policy for the application below. Do not send every request to the largest model by default. Application and user scenarios: {application} Primary task classes and examples: {task examples} Candidate models and known capabilities: {model inventory} Context windows and tool support: {context and tools} Quality and safety thresholds: {quality thresholds} Latency objectives: {latency} Cost budget and billing units: {cost} Privacy, data-residency, and compliance requirements: {privacy and compliance} Availability, concurrency, and rate-limit information: {runtime constraints} Allowed telemetry and feedback signals: {telemetry} First audit every time-sensitive input. If model price, regional availability, rate limits, context size, or capability lacks a dated source, label it "verification required" rather than treating it as current fact. Then return: 1. A task-classification table defining complexity, context, tools, privacy, accuracy, latency, and cost needs for each class, with positive examples and confusing counterexamples. 2. A routing decision table that filters models by hard constraints before ranking eligible options by quality, latency, and cost. Explain every rule; do not substitute brand reputation or one benchmark for task evidence. 3. Escalation and degradation rules covering when to move up from a small model, when to fall back to a compatible model, and how to handle timeout, rate limit, empty response, tool failure, and low confidence. Never silently weaken safety or privacy to stay online. 4. Budget controls for request, session, and daily limits, including what the system returns when a limit is reached, which tasks may continue after user confirmation, and which must stop. 5. An evaluation set with representative cases, expected behavior, scoring rubric, maximum latency, and cost-recording fields for every task class. Include multilingual, long-context, and failure-injection cases. 6. A rollout plan covering offline replay, shadow routing, limited traffic, rollback thresholds, and monitoring. Do not change production configuration directly. Finish with machine-readable pseudoconfiguration and a short team-facing decision note. Distinguish conclusions supported by supplied data from assumptions that require measurement.