Frame the target and learner intent:
- What is being built: software project, event, product, workflow, or other artifact.
- Current stack, target stack, domain, users, deployment or delivery model, and hard constraints.
- What the user wants to learn or re-learn while building.
Select evidence:
- Use 3-5 high-fit comparable repositories or artifacts.
- Include official language/framework guidance and durable written references when relevant.
- Record exact source paths, docs, or examples for every practice worth borrowing.
Distill the fragrance:
- Naming, boundaries, entrypoints, file layout, and dependency direction.
- Error handling, configuration, state, logging, testing, docs, release, and operations habits.
- What the work feels like: compact, explicit, boring, sharp, playful, layered, or deliberate.
- What the comparable refuses to do, not just what it does.
Translate instead of clone:
- For each practice, mark it as adopt, adapt, defer, or avoid.
- Explain why the target can carry it, or why the comparable's scale makes it a bad fit.
- Copy code, names, or structures only when the license and attribution expectations allow it.
- Bind every recommendation to a likely local file, module, ritual, or next slice.
Build the learning loop:
- Start with one tracer-bullet slice that exercises the borrowed style end to end.
- Turn unfamiliar idioms into small exercises, review prompts, or code-reading checkpoints.
- Ask the agent to explain language-specific choices before implementing them when the user is learning.
Produce working rules:
- A short project-specific style guide.
- A first-slice implementation plan.
- AI collaboration rules that preserve the borrowed taste during future coding sessions.

Output

Always return:

Fragrance brief: 5-10 principles that capture the target style.
Evidence map: source paths or references behind the principles.
Adoption map: adopt, adapt, defer, and avoid decisions.
First slice: the smallest buildable path that teaches and proves the style.
AI rules: prompts or constraints to keep future implementation aligned.

Guardrails

Do not praise famous projects without exact evidence.
Do not import enterprise-scale patterns unless the target has enterprise-scale forces.
Do not flatten the result into generic best practices.
For Go, prefer simple packages, clear ownership, explicit errors, table-driven tests, narrow interfaces near consumers, and official Go guidance over framework fashion.

defragance

Defragance

Workflow