Enrich product mechanism mappings with biological, evidence, and ingredient data.
npx skills add https://github.com/Largo2z9/phantomos --skill map-mechanisms
Please help me install this Agent Skill. Skill: map-mechanisms Source: https://github.com/Largo2z9/phantomos/tree/main/.skills/skills/map-mechanisms Command: npx skills add https://github.com/Largo2z9/phantomos --skill map-mechanisms
This Skill solves the challenge of turning shallow product mechanism descriptions into structured, evidence-aware mappings that separate verified science from assumptions.
Ask the skill to deep dive the mechanisms of a product and enrich them with evidence-backed fields.
High-intent search queries and answers about installing and using this skill.
Mapping product mechanisms with scientific validation requires enriching existing mechanism descriptions with biological targets, evidence strength, and ingredient relationships to separate verified clinical data from mechanistic hypotheses. This approach structures shallow product claims into evidence-aware DTC marketing assets.
Evidence analysis for ingredient mapping is the process of linking biological targets and action modes to specific ingredients while categorizing support by clinical evidence, regulatory backing, or unknowns. It prevents DTC operators from building paid acquisition angles on unverified mechanistic hypotheses.
To investigate product claims using evidence-based mechanism analysis, you expand existing product specifications with structured fields for action timing, duration, and market maturity. This distinguishes clinically proven effects from assumptions before refining marketing claims.
Yes, you need existing product specifications, mechanism mappings, brand context, and pain point data to perform mechanism deep enrichment. The process builds upon these foundational inputs to stage structured biological and evidence validations with proper safeguards.
The best way to separate clinical evidence from assumptions in DTC product research is to apply structured evidence investigation that categorizes claims into clinical proof, regulatory support, mechanistic hypotheses, and unknowns while preserving operator trust through validation safeguards.
coleschafferGenerates RMBC-structured VSL scripts with timing markers from product and audience inputs.
Largo2z9Decompose marketing angles into structured strategic components for DTC paid acquisition analysis.
coleschafferCompiles per-ingredient and cross-ingredient research summaries from clinical evidence and metadata for dietary supplements and skincare products.
koki3141Calibrate claim strength against evidence levels for accurate conclusions.
