Automate construction of 5-layer AI property analysis pipelines.
npx skills add https://github.com/toobutta/maptelli --skill ai-pipeline-toobutta
Please help me install this Agent Skill. Skill: ai-pipeline Source: https://github.com/toobutta/maptelli/tree/main/.claude/skills/ai-pipeline Command: npx skills add https://github.com/toobutta/maptelli --skill ai-pipeline-toobutta
This Skill provides a repeatable blueprint to design, extend, and maintain AI-powered property analysis pipelines by enforcing a consistent 5-layer architecture (model, schema, Celery task, FastAPI endpoint, React Query hook) while allowing product-specific extensions for RoofIQ, SolarFit, StormShield, DrivewayPro, PermitScope, LotWatch, and TradeZone.
Provide a product name (e.g., RoofIQ) to scaffold or extend its AI pipeline following the 5-layer pattern.
High-intent search queries and answers about installing and using this skill.
You can build an AI property analysis pipeline by applying a standardized 5-layer pattern that wires SQLAlchemy models, Pydantic schemas, Celery tasks, FastAPI endpoints, and React Query hooks together. This enforces consistent architecture across products like RoofIQ and SolarFit.
The best way to structure multiple AI analytics pipelines is using a unified 5-layer architecture with product-specific extension points. This allows products like StormShield and DrivewayPro to share a standardized backend and frontend pattern while adapting to unique requirements.
Yes, you can use this 5-layer architecture pattern for PermitScope and LotWatch. The template provides specific extension points for these products, enabling adaptations within the unified model, schema, Celery task, FastAPI endpoint, and React Query hook structure.
To scaffold a FastAPI and React Query hook pipeline, provide a product name to generate the 5-layer structure. This creates backend SQLAlchemy models, Pydantic schemas, Celery tasks, APIs, and frontend hooks, with optional directories for scripts, references, and assets.
No, this AI pipeline template does not require existing SQLAlchemy models to generate Celery tasks. It automates the construction of the entire 5-layer pipeline, creating the models, schemas, tasks, APIs, and hooks from scratch or extending existing ones.
You should not use a unified 5-layer pattern if your AI property analysis project requires a fundamentally different architecture than SQLAlchemy models, Pydantic schemas, Celery tasks, FastAPI endpoints, and React Query hooks, or if you do not need standardized pipeline templates.
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