GraphQL Services Architecture Decision Assistant System
A structured way to make and document architecture calls for GraphQL services. Built for agency dev shops working in GraphQL services, tuned for Lovable. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
Preview
You are a pragmatic tech lead specialized in GraphQL services.
Inputs you will receive:
Stack: {{STACK}}
Constraints: {{CONSTRAINTS}}
Feature: {{FEATURE}}
Codebase context: {{CODEBASE_CONTEXT}}
… [purchase to unlock the full framework]Screenshots
Example outputs
Queue-based sync: +resilient to spikes / −adds 200-400ms p95 and a new infra dependency to own…
Ran this for a GraphQL services project with only the placeholders filled — the structure held and the output needed light edits only. (Lovable)
Version history
- v1Initial releaseJul 10, 2026
Buyers get every future update free.
Buyers also bought
Paste a slow query and schema — get back an optimized version, index recommendations, and a plain-English explanation of why it was slow.
Safely refactor gnarly legacy code: characterization tests first, seams identification, strangler-fig migration plan. Based on real enterprise migration experience.
A disciplined build-a-feature workflow for REST APIs: plan first, tests first, small diffs. Built for agency dev shops working in REST APIs, tuned for Cursor. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
A safe refactor plan for machine-learning services: characterization tests, seams, and steps that keep CI green. Built for platform teams working in machine-learning services, tuned for Windsurf. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
More from Aoife Berg
A systematic debugging workflow for machine-learning services that finds root causes instead of symptoms. Built for technical founders working in machine-learning services, tuned for Lovable. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
A thorough review pass tuned for browser extensions — correctness first, style last. Built for technical founders working in browser extensions, tuned for Lovable. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
Meaningful test coverage for serverless functions — behavior tests, edge cases and fixtures that stay maintainable. Built for technical founders working in serverless functions, tuned for Lovable. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
A disciplined build-a-feature workflow for mobile apps: plan first, tests first, small diffs. Built for startup engineering teams working in mobile apps, tuned for Lovable. Fill in the placeholders, paste, and get a structured, ready-to-use result — with clear rules so the output stays specific to your situation instead of generic.
Reviews
No reviews yet.