AI_Commercialization--Product-Management-skills

Signature Case 02: Why I Did Not Build A Prompt Library

Context

If the goal were simply to help PMs use AI faster, the easiest path would have been obvious:

• collect prompts
• add templates
• publish common scenarios

That would have been enough to make a repository that looked useful.

But I did not stop there.

The Real Problem I Saw

Many AI-for-PM resources have the same weaknesses:

• they look broad but are hard to reuse
• they generate output but not stable judgment
• they sound professional but do not form a real operating model
• they are readable for humans but not reliably usable by agents

So the real problem was not “lack of content.” It was:

lack of a system that can be reused across tools, workflows, and agents.

The Core Judgment

The core judgment behind this repository was:

it is more valuable to systematize PM practice than to keep accumulating prompts.

That meant the repo had to solve at least four things:

1. how tasks should be routed
2. how outputs should be structured
3. how workflows should be broken into phases
4. how reliability should be evaluated across tools

Why The Repository Evolved The Way It Did

That is why the repo expanded beyond skills/ into:

• commands/
• agent/
• adapters/
• evals/
• private/

Each layer answers a real operational question.

skills/

Reusable single capabilities.

commands/

Real PM work is usually multi-step, not single-step.

agent/

Different agents need routing and output discipline.

adapters/

The system should be portable across Codex, Claude Code, and Cursor.

evals/

Good-sounding answers are not the same as reliable outputs.

private/

A general system should be able to grow into a more personal or team-specific one.

Why This Matters

The easiest layer of PM work to imitate in the AI era is shallow output.

The harder layer to imitate is:

• problem framing
• decision discipline
• evidence structure
• workflow design

If AI only makes PMs write faster, that advantage gets commoditized quickly. If AI helps PMs systematize judgment and workflow, that becomes a real multiplier.

Why This Works As A Signature Case

This is not just “I built a repo.”

It demonstrates a product judgment:

• choose systems over content piles
• choose routing and evaluation over isolated output
• choose reusable infrastructure over one-off display value

That is the foundation of a stronger long-term personal brand.

One-Line Version

If AI only helps product managers write faster, its value compresses quickly; if it helps them systematize judgment and workflow, it becomes a real capability multiplier.