Translate goals into scoped tasks, acceptance criteria, risks, and constraints.
AI: clarify / decompose / plan
> Rebuilding the full development workflow with AI
My main work is backend systems and user interfaces, while I also keep participating in frontend, CI/CD, product, design, and delivery workflows. I am continuously learning AI and integrating it into my workflow so requirement breakdown, solution design, coding, integration, deployment, and review become an iterative Agent-driven loop.
> role
Loop Engineering treats development as repeated cycles: define intent, load context, let agents execute, verify the output, and feed findings back into the next iteration.
Translate goals into scoped tasks, acceptance criteria, risks, and constraints.
AI: clarify / decompose / plan
Provide codebase, docs, APIs, design references, data, and project rules.
AI: retrieve / summarize / align
Generate code, UI, tests, scripts, docs, migration drafts, and implementation options.
AI: implement / refactor / document
Run tests, review diffs, inspect UI, and check security, performance, and edge cases.
AI: test / review / diagnose
Turn findings into fixes, prompts, specs, checklists, and reusable Agent skills.
AI: iterate / learn / automate
An online experiment field for model calls, knowledge bases, toolchains, and frontend/backend integration.
> launch
An MCP service for managing an Obsidian knowledge base inside Copilot, Cursor, Claude Code, and similar environments.
> launch
A self-built collection of Agent Skills that turns recurring tasks into reusable capability packages and execution rules.
> launch
A management backend for maintaining service data, operations, deployment, and platform administration.
> launch
A personal project collection entrance that organizes distributed small systems and service capabilities into one interface.
> launch
An intelligent assistant direction exploring conversation, knowledge management, and personal automation.
> link_pending