Guide structured six-layer reverse-engineering analysis of AI products.
npx skills add https://github.com/chusimin/AIPM-Skills --skill ai-product-teardown
Please help me install this Agent Skill. Skill: ai-product-teardown Source: https://github.com/chusimin/AIPM-Skills/tree/main/ai-product-teardown Command: npx skills add https://github.com/chusimin/AIPM-Skills --skill ai-product-teardown
The skill provides a coach-like framework that guides users to structurally reverse-engineer AI products across six layers (market, business, user, technology, model, and foundation), ensuring data provenance, explicit gaps, and actionable documentation.
Create a new teardown project and start Phase 1 market research, then proceed through all six layers with structured prompts.
High-intent search queries and answers about installing and using this skill.
Reverse-engineering AI products requires decomposing agent architecture across six layers: market, business, user, technology, model, and foundation. This framework breaks down product logic into modular components and produces template-driven Markdown artifacts for each layer.
The six-layer model deconstructs AI products across market, business, user, technology, model, and foundation dimensions. It coordinates data across layers to produce a global context schema, mapping product strategy down to core model architecture for comprehensive teardown analysis.
Reverse-engineering AI products for interview prep uses a structured six-layer teardown across market, business, user, technology, model, and foundation dimensions. This framework produces data-driven insights, risk classification, and exportable Markdown documentation to structure competitive analysis responses.
Documenting AI product analysis uses a modular teardown framework that enforces data provenance and explicit information gaps. It generates exportable per-layer Markdown artifacts within a global context schema, ensuring every data-driven insight across the six layers is traceable and reproducible.
The six-layer teardown framework supports product replication planning by reverse-engineering AI products across market, business, user, technology, model, and foundation dimensions. It generates modular documentation with explicit data provenance and gap tagging to guide faithful replication efforts.
FacostarrGenerates and maintains product marketing context documents from codebase, materials, and user input.
alyamenfactoryAutomate PRD creation, user stories, and roadmaps with RICE prioritization.
amitmishra-bylddTransforms vague product and business needs into actionable requirements and market insights.
Spectra29115Create PRDs, user stories, and roadmaps using structured templates and prioritization frameworks.
