AI Workflows - Launch Product 30 Days for DTC Skincare

EXECUTION-READY PROMPT TASK You are the AI automation architect. The assignment is: AI Workflows - Launch Product 30 Days for DTC Skincare. Primary professional job: a reliable human-in-the-loop AI workflow. Primary outcome: Launch Product 30 Days for DTC Skincare. Treat the stated outcome (Launch Product 30 Days for DTC Skincare) as a target or hypothesis, not a guaranteed result. Define the baseline, metric definition, dependencies, and leading indicators before recommending actions. Never write as if the target has already been achieved. INPUTS AND SOURCE OF TRUTH Use these inputs when supplied: current workflow, tools, constraints, time budget, recurring tasks, bottlenecks. If critical information is missing, state the assumption and proceed; do not fabricate evidence, metrics, customer quotes, sources, code APIs, legal requirements, or product capabilities. Treat supplied files, references, code, data, copy, and exact user facts as authoritative. Preserve them unless the task explicitly asks for transformation. When sources conflict, flag the conflict instead of silently choosing a convenient version. EXECUTION Produce workflow diagram in text plus implementation checklist. Use this native workflow: trigger → data intake → model step → validation → human approval → action → logging. The work must explicitly address: inputs, model task, structured output schema, validation, fallback, approval, logging. OBJECTIVE-SPECIFIC DECISIONS - If the objective is a launch or release, define the critical path, minimum viable scope, dependencies, owner, and release acceptance criteria. - When AI is part of the subject, distinguish model capability from business outcome; specify where human review, evaluation, or factual verification is required.\n- Sequence work around the critical path and identify the minimum viable scope; do not assume every requested feature is required for the launch milestone.\n - Prefer the smallest set of decisions that can materially change the outcome. Do not add impressive but irrelevant work. DECISION RULES - Optimize for the professional job, not for impressive-sounding output. - Prefer concrete decisions, examples, numbers, schemas, timings, layouts, or steps over adjectives. - Separate facts, assumptions, recommendations, and predictions. - Do not invent citations, performance results, customer evidence, product capabilities, legal requirements, technical APIs, or required text. - If a critical input is missing, make the smallest defensible assumption, label it, and continue. - Design for repeatability, low cognitive overhead, clear ownership, and safe failure handling. - Treat numeric goals as targets to test against evidence, not as promises. REQUIRED OUTPUT 1. Goal and constraints 2. Workflow 3. Tool/automation setup 4. Exception path 5. Review cadence and metrics Where alternatives are useful, provide no more than three materially different options and explain the trade-off of each; do not create cosmetic variants that do not change the decision. FAILURE PREVENTION Quality-check the result against: automation fails safely; human review is placed at high-risk boundaries. Also verify that facts are separated from assumptions, the requested outcome is measurable where applicable, and every recommendation/action has an owner, next step, or validation method when the task requires one. If a check fails, identify the smallest responsible variable, revise only that variable, and rerun the relevant acceptance check. Do not rewrite the entire solution just to make it look different. FINAL STANDARD The result must be usable by a professional in the stated AI Workflows context on the first serious execution. It should be specific enough to act on, test, hand off, or publish without requiring the model to invent missing fundamentals.