AI Native PM Agent

An AI product coach that asks "Is this direction really worth pursuing?" — with structured methodology backing every decision, from spark of inspiration to production deployment.

📖 中文版 README

---

Why Do You Need This?

90% of AI product teams die in the same traps:

• Direction Trap: Spend 3 months building an AI feature, only to find users won't pay for it
• Needs Trap: Fake needs look too much like real ones — AI makes prototyping near-free, but also lets you build the wrong thing faster
• Boundary Trap: AI crosses the line and does things it shouldn't, triggering compliance risks
• Hallucination Trap: Pre-launch accuracy looks like 95%, post-launch reality says otherwise
• Cost Trap: Token bills explode, business model falls apart

This Agent doesn't write code for you — it makes you pause at every critical decision point and verify with structured methods.

Why Not Just Use Traditional Product Methodology?

Traditional product frameworks (Lean Startup, Jobs-to-be-Done, Design Thinking) were built for a world where prototyping was expensive and AI didn't exist. They break down in the AI era because:

Traditional Assumption	AI Era Reality
Build-Measure-Learn takes weeks	AI prototypes are near-free — you can build the wrong thing faster
User needs are relatively stable	AI creates new needs and makes old ones obsolete overnight
Product boundaries are clear	AI crosses lines you didn't draw — compliance, ethics, autonomy
Cost scales with features	Token costs scale with usage — business model can invert
Launch is a milestone	AI products degrade post-launch (hallucinations, drift, adversarial inputs)

This methodology is AI Native from the ground up: it starts with boundary design before capability design, validates with certainty rather than confidence, and prices on risk reduction rather than feature count. Every stage assumes AI is in the loop — and designs for what happens when it goes wrong.

---

See What It Does in 30 Seconds

▶ Click to play the 30s introduction video (2.3 MB MP4)

Core Capabilities: 80 executable Skills across 8 stages + Stage auto-routing + Conflict detection + Evidence chain tracking

---

A Concrete Example

Scenario: You want to build an "AI Contract Review Assistant"

Needs Discovery (P0)

The Agent validates the need with tool cards:

• Micro-Needs Five Questions: Lawyers review contract clauses daily — the pain is small but constant
• Real-Needs Validation: Long-standing problem + compensation behaviors (manual annotation) + structural root cause (liability risk)
• Four-Layer Decomposition: Surface: "automate review" → Situation: "lawyers bear liability risk" → Cost: "$200/hour per review"
• Agent Boundary Checklist: AI can flag risky clauses, but cannot determine contract validity

Output: Needs validated + Agent boundary design

Direction Framing (P1)

The Agent asks you:

• Where does contract data come from? (Availability)
• Does it involve client confidentiality? (Desensitization)
• Who is responsible for review results? (Authorization)
• Is the output format standardized? (Structured)
• What happens when new regulations emerge? (Sustained supply)

Output: Direction Brief — clear go/no-go with conditions

Business Model (P6)

The Agent prices using the Certainty Premium formula:

• Fear Level: Lawyers' biggest fear is missing a risky clause → High
• Error Cost: Missing one clause could mean millions in liability → Extremely high
• Substitution Cost: Manual review at $200/hour → Medium
• Recommended Model: Insurance Mode (charge per successful review, compensate for misses)

Output: Pricing strategy — $5/review, 10x compensation for missed clauses

Audit & Release (P9)

The Agent checks:

• Reliability: Identification accuracy, hallucination rate
• Safety: Sensitive information handling
• Boundaries: Which clause types require human review
• Cost: Token cost per review vs. pricing

Output: Release boundary document — Auto-execute zone / Human handoff zone / Disabled zone

---

Quick Start

Option 1: One-Click Install (Recommended)

# Install all 80 skills + orchestrator
curl -fsSL https://raw.githubusercontent.com/gmaxxxie/ai-native-product-agent-skills/main/install.sh | bash

# Start a product project
hermes run "I want to build an AI customer service product, help me start from direction framing"

Option 2: Install from GitHub URL

Install individual skills on demand:

# Orchestrator (entry point)
hermes skills install \
  https://raw.githubusercontent.com/gmaxxxie/ai-native-product-agent-skills/main/orchestrator/SKILL.md \
  --name ai-native-pm-agent

# Any individual skill
hermes skills install \
  https://raw.githubusercontent.com/gmaxxxie/ai-native-product-agent-skills/main/skills/p1-direction-framing/SKILL.md \
  --name p1-direction-framing

Option 3: Clone & Local Install

git clone https://github.com/gmaxxxie/ai-native-product-agent-skills.git
cd ai-native-product-agent-skills
bash install.sh   # copies all skills to ~/.hermes/skills/ai-native-pm/

Option 4: Use with Other AI Agents

These skills work as structured prompts — they're not tied to any specific agent framework.

The simplest way: just tell your AI agent to install from this repo.

Agent	Install Command
Hermes Agent	hermes skills install https://github.com/gmaxxxie/ai-native-product-agent-skills
Claude Code	claude "Install all skills from https://github.com/gmaxxxie/ai-native-product-agent-skills into this project"
OpenAI Codex	codex "Clone and set up https://github.com/gmaxxxie/ai-native-product-agent-skills — read all SKILL.md files and make them available as product methodology tools"
OpenCode	opencdoe run "Install AI Native PM Agent from https://github.com/gmaxxxie/ai-native-product-agent-skills"
Any LLM	Just paste: "Read the skills from https://github.com/gmaxxxie/ai-native-product-agent-skills and apply the methodology to my product idea"

💡 Tip: Claude Code, Codex, and OpenCode can all git clone the repo and read SKILL.md files directly. Just give them the repo URL and tell them to install — they'll figure out the rest.

Per-Stage Usage

Each stage is an independent Skill you can call individually:

Stage	Trigger Phrase	Output
P0 Needs Discovery	"I have a pain point…"	Needs validation report
P0a Micro-Needs	"Is this problem too small to matter?"	Micro-needs list
P0b Real Needs	"Is this need real or fake?"	Real/fake verdict
P0c Decomposition	"Help me decompose this need"	Four-layer breakdown
P0d Archaeology	"What's the deep need?"	Deep needs report
P1 Direction Framing	"I have an idea…"	Direction Brief
P2 Experiment Engine	"Help me design experiments…"	Experiment plan + Rubric
P3 System Building	"How to go from experiment to product…"	System architecture
P5 Business Model	"How to price this…"	Pricing strategy
P6 Growth Strategy	"How to get cold start…"	Growth plan
P8 UX Design	"How should this AI feel to use?"	UX design + trust tiers
P9 Audit & Release	"Ready to launch, check it…"	Release boundary document
P10 Production Ops	"It's live, how do I keep it healthy?"	Monitoring + feedback loops
P11 Product Team	"How should humans and AI collaborate?"	Team structure + roles
P12 Contemplation	"Am I even asking the right question?"	View correction + prerequisite check
P13 Judgment & Intuition	"How do I make better decisions?"	Nine-step decision framework
P14 Aesthetic Authority	"What makes this feel premium?"	Aesthetic system + selection criteria

Cross-Stage Combos (One-Stop)

Combo	Trigger Phrase	Output
Needs → Direction	"Take me from pain point to direction framing"	Direction Brief
Business → Growth	"How should pricing and growth align?"	Pricing-growth alignment
UX → Audit	"Is this UX design safe to release?"	UX audit report + release recommendation

---

Complete Skill List (80 Skills)

P0 — Needs Discovery Layer (17 Skills)

ID	Name	What It Does
p0-needs-orchestrator	Needs Discovery Orchestrator	Coordinates six tool cards for systematic needs discovery
p0-product-needs	AI Native Product Needs	Unified needs discovery + fake needs detection
p0a-micro-needs-detector	Micro-Needs Five Questions	Detects overlooked micro-needs
p0b-real-needs-validator	Real-Needs Five Questions	Distinguishes real needs from fake ones
p0c-needs-decomposer	Needs Four-Layer Decomposition	Expression / Scenario / Situation / Cost layers
p0d-needs-archaeologist	Needs Archaeology Five Steps	Uncovers deep needs and historical constraints
p0e-good-question-generator	Good Questions Six Dimensions	Discovers good questions from six perspectives
p0f-agent-boundary-designer	Agent Boundary Checklist	Defines AI permission boundaries
p0g-diverse-recommendation-rewriter	Diverse Recommendation Rewrite	From "guess what you like" to "help you discover"
p0g-diversity-rewrite-checklist	Diversity Rewrite Checklist	Validates diversity rewrite quality
p0h-ai-product-triple-balance	AI Product Triple Balance	Business / Humanity / Technology balance
p0h-triple-balance-assessor	Triple Balance Assessor	Evaluates product triple balance state

P1–P2 — Direction & Experiment Layer (8 Skills)

ID	Name	What It Does
p1-direction-framing	Direction Framing	Five-dimension judgment, Direction Brief
p2-experiment-engine	Experiment Engine (Overview)	Capability / Product / Business three-layer experiments
p2a-experiment-overview	Experiment Overview	Materials prep, three-layer design, evaluation Rubric
p2b-product-form-exploration	Product Form Exploration	Capability boundary, interaction prototype, form judgment
p2c-process-redesign	Process Redesign	Task decomposition, human-AI collaboration mode
p2d-convergence-decision	Convergence Decision	Experiment records, convergence signals, continue/pause/stop
p2e-shadow-validation	Shadow Validation	Shadow system, parallel run, human comparison, audit evidence

P3–P4 — System Building Layer (5 Skills)

ID	Name	What It Does
p3-system-building	System Building	From experiments to product
p4-agent-skill-design	Agent & Skill Unit Design	Agent/Skill unit design
p5-memory-system	Memory System Design	AI product memory architecture
p6-context-engineering	Context Engineering	Context management system
p7-knowledge-rag	RAG & Knowledge System	Knowledge management + RAG design

P5–P6 — Business Model Layer (5 Skills)

ID	Name	What It Does
p6-business-model	AI Native Business Model (Overview)	Certainty Premium business model design
p6a-certainty-premium-calculator	Certainty Premium Calculator	Calculates certainty premium
p6b-arbiter-mode-designer	Arbiter Mode Designer	"Truth-as-a-Service" business model
p6c-insurance-mode-designer	Insurance Mode Designer	"Result Guarantee" business model
p6d-prediction-arbitrage-designer	Prediction Arbitrage Designer	"Time Arbitrage" business model

P7 — Growth Strategy Layer (6 Skills)

ID	Name	What It Does
p7-marketing-growth	AI Native Marketing & Growth (Overview)	Growth flywheel & marketing strategy
p7a-data-flywheel-builder	Data Flywheel Builder	Assesses and builds self-reinforcing data flywheels
p7b-intent-prediction-designer	Intent Prediction Designer	From audience targeting to individual foresight
p7c-predictive-retention-designer	Predictive Retention Designer	From post-churn recovery to pre-churn prevention
p7d-marketing-productizer	Marketing-as-Product Designer	Turns marketing activities into product features
p7e-customer-loop	Customer Loop	Early customer filtering, co-creation boundaries, feedback loops

P8 — User Experience Layer (4 Skills)

ID	Name	What It Does
p8-ux-design	AI Native UX Design (Overview)	UX design methodology
p8a-rax-risk-assessor	RAX Risk Assessor	Risk / Ambiguity / eXposure assessment
p8b-trust-tier-designer	Trust Tier Designer	Progressive trust system design
p8c-progressive-disclosure	Progressive Disclosure Checklist	Feature reveal pacing design

P9–P11 — Audit, Operations & Team Layer (9 Skills)

ID	Name	What It Does
p9-audit-release	Audit & Release	Go/no-go decision
p10-production-ops	Production Operations	Monitoring & feedback loops
p10a-value-discovery-loop	Value Discovery Loop	From value signal to direction correction闭环
p10b-aiops-case	AIOps Case Template	Complete methodology path for high-risk scenarios
p10c-customer-service-case	AI Customer Service Case	Service collaboration, Copilot, experience leverage
p10d-saas-case	AI Native SaaS Case	Semantic layer, capability moats, data flywheels
p11-product-team	AI Native Product Team	Human-AI division, capability gaps, team roles

P12 — Contemplation Layer (10 Skills)

From: Contemplation — Product Judgment, User Understanding, and Decision Correction in the AI Era

ID	Name	What It Does
p12-contemplation-orchestrator	Contemplation Orchestrator	Routes to correct chapter skill, chains full decision-correction flow
p12a-contemplation-right-view	Right View	Three-layer problem framing: phenomenon / situation / relationship
p12a-contemplation-view-correction	View Correction	Default checks, evidence validation, consequence inquiry, eight correction angles
p12a-contemplation-prerequisite-check	Prerequisite Check	Identifies situational changes, validates assumptions, reassigns methods
p12a-contemplation-right-thinking	Right Thinking	Dissect judgment chain, distinguish premise/evidence/reasoning/emotion
p12a-contemplation-right-speech	Right Speech	Language cleaning, meeting health check, honest expression practice
p12a-contemplation-right-action	Right Action	Value/cost/emotion/exit-right quadruple check before execution
p12a-contemplation-right-livelihood	Right Livelihood	Revenue source review and incentive bias check
p12a-contemplation-right-effort	Right Effort	Zero-based analysis, pause strategy, stop-loss decision
p12a-contemplation-right-mindfulness	Right Mindfulness	Establish personal and team decision awareness

P13 — Judgment & Intuition Layer (12 Skills)

From: Intuition — Judgment and Intuition in the AI Era

ID	Name	What It Does
p13-intuition-orchestrator	Intuition Orchestrator	Nine-step closed-loop decision roadmap router
p13a-judgment-metacognition	Judgment Metacognition	Understanding judgment, identifying judgment scenarios
p13b-systemic-thinker	Systemic Thinking	Structural analysis, relationship mapping, feedback loop identification
p13c-product-psychology	Product Psychology	User mental models, behavior design, motivation analysis
p13d-intuition-training	Intuition Training	Compress intuition into cognitive models and pattern recognition
p13e-nine-step-framework	Nine-Step Framework Overview	Complete framework from "what to do" to feedback loop
p13f-first-half-judgment	First Half — What & Worth	"What to do, is it worth it, should we use AI" judgment
p13g-mid-judgment	Mid — Form & Trust	"What form, how much trust, how to do it" judgment
p13h-validation-market	Second Half — Validation	"How to validate, how to enter market, feedback loop"
p13i-judgment-traps	Judgment Traps	Common judgment errors and cognitive bias defenses
p13j-organizational-judgment	Organizational Judgment	Translating personal judgment into team judgment capability
p13k-intuition-evolution	Intuition Evolution	Continuous judgment training, standard improvement mechanism

P14 — Aesthetic Authority Layer (9 Skills)

From: AI Beaty — Aesthetic Authority in the Age of AI

ID	Name	What It Does
p14-beauty-orchestrator	Beauty Orchestrator	Routes to aesthetic training and aesthetic authority system
p14a-beauty-redefinition	Aesthetic Redefinition	Generation anxiety, six aesthetic dimensions, dual-axis model
p14b-beauty-ai-roles	AI's Role in Aesthetics	Amplifier / sparring partner / collaborator, not aesthetic itself
p14c-beauty-selection	Selection Over Generation	Selection is the new core skill — Context determines output ceiling
p14d-beauty-narrative	Narrative as Aesthetic	Story structure, emotional rhythm, information architecture aesthetics
p14e-beauty-human-edge	Human Indispensability	Aesthetic as moat, standard evolution, human core advantage
p14f-beauty-commercial	Commercial Value of Aesthetics	Market acceptance, aesthetic premium, experiential aesthetics
p14g-beauty-system	Aesthetic Training System	Systematic aesthetic standard accumulation and calibration
p14h-beauty-preface	Preface & Core Proposition	Aesthetic authority as core competitive advantage when everything can be generated

Cross-Book Combo Skills (3 Skills)

ID	Name	What It Does
combo-needs-to-direction	Needs → Direction	Pain point to Direction Brief in one pass
combo-business-to-growth	Business → Growth	Pricing-flywheel alignment design
combo-ux-to-audit	UX → Audit	RAX assessment + trust tiers + release recommendation

---

Project Structure

ai-native-pm-agent-skills/
├── README.md / README_CN.md         # This document (EN / 中文)
├── ARCHITECTURE.md                  # System architecture design
├── skill-registry.yaml              # Skill registry (80 skills registered)
├── orchestrator/SKILL.md            # Main orchestrator: stage routing + conflict detection
├── install.sh                       # One-click install script
├── assets/                          # Hero banner, pipeline flow, industry matrix, methodology books
├── skills/
│   ├── p0-needs-orchestrator/        # P0 Needs Discovery orchestrator
│   ├── p0-product-needs/             # P0 unified needs discovery
│   ├── p0a-micro-needs-detector/     # P0a micro-needs detection
│   ├── p0b-real-needs-validator/     # P0b real vs fake needs
│   ├── p0c-needs-decomposer/        # P0c four-layer decomposition
│   ├── p0d-needs-archaeologist/     # P0d deep needs archaeology
│   ├── p0e-good-question-generator/ # P0e good questions six dimensions
│   ├── p0f-agent-boundary-designer/ # P0f AI boundary design
│   ├── p0g-diverse-recommendation-rewriter/    # P0g diversity rewrite
│   ├── p0g-diversity-rewrite-checklist/       # P0g diversity checklist
│   ├── p0h-ai-product-triple-balance/         # P0h triple balance
│   ├── p0h-triple-balance-assessor/           # P0h balance assessor
│   ├── p1-direction-framing/        # P1 direction framing
│   ├── p2-experiment-engine/        # P2 experiment overview
│   ├── p2a-experiment-overview/     # P2a experiment setup
│   ├── p2b-product-form-exploration/ # P2b product form
│   ├── p2c-process-redesign/       # P2c process redesign
│   ├── p2d-convergence-decision/    # P2d convergence decision
│   ├── p2e-shadow-validation/       # P2e shadow validation
│   ├── p3-system-building/          # P3 system building
│   ├── p4-agent-skill-design/       # P4 agent & skill design
│   ├── p5-memory-system/           # P5 memory system
│   ├── p6-context-engineering/      # P6 context engineering
│   ├── p7-knowledge-rag/            # P7 RAG & knowledge
│   ├── p6-business-model/           # P6 business model overview
│   ├── p6a-certainty-premium-calculator/    # P6a certainty premium
│   ├── p6b-arbiter-mode-designer/  # P6b arbiter mode
│   ├── p6c-insurance-mode-designer/ # P6c insurance mode
│   ├── p6d-prediction-arbitrage-designer/   # P6d prediction arbitrage
│   ├── p7-marketing-growth/         # P7 marketing overview
│   ├── p7a-data-flywheel-builder/   # P7a data flywheel
│   ├── p7b-intent-prediction-designer/      # P7b intent prediction
│   ├── p7c-predictive-retention-designer/   # P7c predictive retention
│   ├── p7d-marketing-productizer/   # P7d marketing productizer
│   ├── p7e-customer-loop/           # P7e customer loop
│   ├── p8-ux-design/               # P8 UX design overview
│   ├── p8a-rax-risk-assessor/       # P8a RAX risk assessment
│   ├── p8b-trust-tier-designer/     # P8b trust tier design
│   ├── p8c-progressive-disclosure/  # P8c progressive disclosure
│   ├── p9-audit-release/           # P9 audit & release
│   ├── p10-production-ops/          # P10 production operations
│   ├── p10a-value-discovery-loop/  # P10a value discovery loop
│   ├── p10b-aiops-case/            # P10b AIOps case
│   ├── p10c-customer-service-case/  # P10c AI customer service case
│   ├── p10d-saas-case/              # P10d SaaS case
│   ├── p11-product-team/           # P11 product team design
│   ├── p12-contemplation-orchestrator/     # P12 Contemplation orchestrator
│   ├── p12a-contemplation-right-view/      # P12 right view
│   ├── p12a-contemplation-view-correction/ # P12 view correction
│   ├── p12a-contemplation-prerequisite-check/   # P12 prerequisite
│   ├── p12a-contemplation-right-thinking/    # P12 right thinking
│   ├── p12a-contemplation-right-speech/      # P12 right speech
│   ├── p12a-contemplation-right-action/      # P12 right action
│   ├── p12a-contemplation-right-livelihood/   # P12 right livelihood
│   ├──