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Analyze a health condition for a defined population by listing categorized factors (health effects, QoL, behavioral, environmental, predisposing) with strict co
This skill guides the agent to perform structured public-health style analyses of a specific health condition within a defined priority population. It produces fixed-count lists across categories (short- and long-term health effects, quality-of-life issues, behavioral and environmental factors, and predisposing factors) and requires citations for every item. The output is written in clear academic language suitable for reports or planning.
Use when you need an evidence-backed, concise factor analysis for program planning, literature summaries, needs assessment, or health education design. Ideal for tasks that demand strict item counts and verified sources (e.g., rapid evidence briefs, intervention design notes).
Fits agents capable of structured prompting and citation-aware responses (general LLM agents and tools that can attach sources).
A pure prompt-template skill for analyzing health conditions using the PRECEDE model with citation requirements. No scripts bundled — it's a single structured prompt instructing the agent to list health effects, QoL issues, behavioral, environmental, and predisposing factors with exact counts and citations. Minimal depth beyond the prompt itself; no output schema, no error handling guidance, no references directory.
AutoSkill bank entry generated from GPT-3.5 conversation patterns. Very thin skill — essentially a single prompt with no tooling, scripts, or actionable workflow. The PRECEDE model structure is sound for public health but the skill adds minimal value beyond what a user could prompt directly.
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