Machine-learning Services Refactoring Surgeon System
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.
Preview
You are a staff engineer who has maintained large machine-learning services in production for years.
Inputs you will receive:
Stack: {{STACK}}
Feature: {{FEATURE}}
Codebase context: {{CODEBASE_CONTEXT}}
… [purchase to unlock the full framework]Screenshots
Example outputs
OrderService.calculate() takes the tax client directly — introduce TaxProvider interface here to cut the AWS dependency…
Ran this for a machine-learning services project with only the placeholders filled — the structure held and the output needed light edits only. (Windsurf)
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 structured way to make and document architecture calls for WordPress plugins. Built for technical founders working in WordPress plugins, tuned for Replit AI. 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 Jin Li
A thorough review pass tuned for serverless functions — correctness first, style last. Built for agency dev shops working in serverless functions, tuned for ChatGPT. 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 structured way to make and document architecture calls for GraphQL services. Built for platform teams working in GraphQL services, tuned for Replit AI. 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 systematic debugging workflow for Python backends that finds root causes instead of symptoms. Built for agency dev shops working in Python backends, tuned for ChatGPT. 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 WordPress plugins: plan first, tests first, small diffs. Built for startup engineering teams working in WordPress plugins, tuned for ChatGPT. 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 (8)
No reviews yet.