gmaxxxie

gmaxxxie / ai-native-product-agent-skills

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AI Native Product Methodology — 80 executable skills across P0-P14 stages, covering needs discovery to aesthetic authority. From 8 books.

agent-skills ai-agent ai-native ai-product methodology
15
0
100% credibility
Found May 01, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A set of structured coaching tools for AI assistants that guide users through validating, designing, and launching AI products using a 15-stage methodology focused on safety, boundaries, and real needs.

How It Works

1
🔍 Discover the Coach

You stumble upon this AI product coach while searching for help to build your dream AI tool, promising to guide you safely from idea to launch.

2
🛠️ Set It Up Easily

With one simple click, you add all the coaching tools to your favorite AI helper, and everything is ready to go.

3
💭 Share Your Idea

You chat with your AI coach, saying something like 'I want to build an AI helper for reviewing contracts,' and it starts guiding you right away.

4
✅ Check if It's Worth It

The coach asks smart questions to validate your idea, spotting real pains, setting safe limits for what the AI can do, and giving you a clear go-ahead report.

5
📈 Build Step by Step

Together, you map out direction, experiments, business plans, user feel, and growth strategies, pausing at each big decision to double-check.

6
🔍 Final Safety Check

Before launch, the coach audits everything for risks, costs, and reliability, handing you a release plan with safe zones and human handoffs.

🚀 Launch Confidently

Your AI product is ready to shine, built with proven steps that avoid common pitfalls, feeling solid and exciting to share with the world.

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AI-Generated Review

What is ai-native-product-agent-skills?

This Python repo delivers 80 executable skills for AI agents like Hermes, implementing a full AI-native product methodology across 15 stages from needs discovery to aesthetic authority. Drawn from eight books, it helps teams avoid AI pitfalls like fake needs, boundary oversteps, and post-launch drift by pausing at decision points with structured prompts and outputs like direction briefs or pricing strategies. Install with a one-click bash script, then run via `hermes run "build an AI contract reviewer"` for guided workflows.

Why is it gaining traction?

It stands out with auto-routing between stages, conflict detection, and compatibility across Hermes, Claude, or any LLM—no lock-in to one framework. Developers hook on the one-liner install and combo skills like needs-to-direction, delivering concrete artifacts like release boundaries faster than ad-hoc prompting. Even without github native automerge enabled, the skill registry and scripts make native product exports straightforward for iterative AI PM.

Who should use this?

AI product managers validating ideas before coding, indie devs prototyping AI tools in legal, healthcare, or e-commerce, and teams doing native products review across github native lab setups. Ideal for those tired of hallucination traps in customer service bots or SaaS agents, needing quick business model calcs or UX audits.

Verdict

Try it if you're building AI products and use Hermes—solid docs and video demos make the 1.0% credibility score and 15 stars forgivable for an alpha repo. Maturity lags with minimal tests, but the methodology shines for structured validation over raw ideation.

(198 words)

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