Health AI Application Reference Library
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About this skill
What It Solves
Big-health content, packaging, video, customer support, and courseware tasks are often constrained by compliance, professional wording, and platform adaptation. Direct LLM generation can miss legal boundaries, asset structures, and delivery formats. @efficient-technology/health-ai-applications turns these needs into reusable task directories and component checklists instead of a single ad-hoc prompt.
How It Works
The skill acts as a domain payload for Universal Task OS and provides:
- 62 task entries across nine domains, including copywriting, posters, video, packaging design, website optimization, automated after-sales replies, and courseware.
- Five-field component lists covering required components, optional components, assembly order, compliance constraints, and output format.
- Domain exemplars for analyzing structure, style, logic, and format before producing similar assets.
- A workflow questionnaire that captures current manual processes and helps generate a more tailored AI plan.
At runtime, it reads references/health-ai-catalog.md, then loads references/health-ai-requirements.md or references/exemplars.md as needed. Task lists feed UTOS’s content axis, dependency topology feeds the execution axis, and delivery inserts compliance checkpoints for advertising rules, medical advertising rules, and platform policies.
Boundaries
It strongly depends on universal-task-os. If UTOS is missing or fails to install, the skill remains useful for browsing task lists, component requirements, and exemplar indexes, but refuses solution generation, pipeline orchestration, and content production. It is suitable for standardizing big-health delivery templates, not for replacing medical diagnosis, legal review, or regulatory decisions.
Use Cases
- A content team needs to draft a health-food post for Xiaohongshu and break it into audience, selling points, platform rules, and ad-law checks.
- A design team needs to plan a packaging series and list brand identity, materials, container shape, compliance review, and cost evaluation.
- A support lead needs to structure FAQ, intent recognition, keyword matching, and escalation tasks for an automated reply workflow.
- A training team needs to design courseware by mapping learning goals, knowledge layers, terminology, and compliance checks.
Best For
- Big-health content leads who need to turn copy, posters, and video into compliance-checked delivery checklists.
- AI solution consultants who need to use a workflow questionnaire and turn customer processes into actionable AI plans.
- UTOS developers who need to feed domain task lists and dependency topology into the content and execution axes.
- Health product operators who need consistent terminology and platform rules across automated replies, websites, and courseware.
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