llm-coding-workflow
LLM Coding Workflow
AI-augmented software engineering workflow that maximizes LLM effectiveness through structured planning, clear context, strategic delegation, and human accountability.
What This Skill Does
This skill synthesizes Addy Osmani's LLM coding workflow, Anthropic's agentic coding research, Harper Reed's spec-driven pipeline, and patterns from 30+ real-world projects. It supports two collaboration modes — interactive pair programming and autonomous delegation — and provides a systematic approach to AI-assisted development that prevents wasted cycles while enabling increasingly autonomous execution.
Core Philosophy: The human engineer is the accountable owner; the AI is a capable collaborator whose autonomy scales with the quality of the prompt. Well-scoped delegation with clear acceptance criteria is not "blind trust" — it is a higher-leverage operating mode.
Two Collaboration Modes:
| Mode | When | How | Review |
|---|---|---|---|
| Pair Mode (Conductor) | Ambiguous problems, design decisions, learning new domains | Interactive back-and-forth; AI as thought partner | Line-by-line as you go |
| Delegation Mode (Orchestrator) | Well-scoped tasks with clear acceptance criteria | Structured prompt → autonomous execution → human review | Deliverable review against spec |