01 · The story
The missing layer between an idea and its implementation.
AI coding agents can move quickly, but speed alone does not create shared understanding.
Product intent, MVP boundaries, technical constraints, design decisions, and working rules are often scattered across temporary chats. Every new session begins with another attempt to reconstruct the same project.
Traditional PRDs only solve part of that problem. Builders still need agent instructions, design direction, execution rules, and an actionable sequence of work before an agent can operate with confidence.
markdown-agent treats product context as infrastructure: explicit, modular, reviewable, and close to the code it guides.
02 · The approach
A deliberate path from uncertainty to useful context.
- 01
Interview the idea
Focused questions uncover the product, audience, MVP boundary, stack, design direction, and definition of done.
- 02
Clarify the decisions
Readiness and conflict checks expose ambiguity while it is still inexpensive to resolve.
- 03
Compile the context
Confirmed decisions become modular documents that builders can review and coding agents can keep using.
A starter kit, not a codebase
03 · Principles
Useful clarity, not AI spectacle.
- 01
Context before code
A fast start only matters when the direction, constraints, and definition of done are clear.
- 02
Clarity over volume
Useful context is concise, modular, and specific. More documentation is not automatically better.
- 03
Conversation over interrogation
Defining a product should feel like working with a thoughtful partner, not completing a disguised form.
- 04
Honest capability
markdown-agent prepares the context. It does not pretend to replace the builder or generate a finished product.
04 · The founder
