Product Operations Sub-Skill

Entry point: /​faion-net — invoke this skill for automatic routing to the appropriate domain.

Product Operations Sub-Skill

Communication: User's language. Docs: English.

Purpose

Day-to-day product operations: prioritization, backlog, analytics, feedback, lifecycle management.

Parent: faion-product-manager

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Context Discovery

Auto-Investigation

Detect existing product operations artifacts:

Read existing operations artifacts:

• Backlog structure and feature prioritization
• Analytics dashboards or KPI definitions
• Feedback collection process
• Tech debt register

Discovery Questions

Q1: Operations Focus

question: "What product operations area do you need help with?"
header: "Focus"
multiSelect: false
options:
  - label: "Prioritization (choose what to build)"
    description: "RICE, MoSCoW, or other prioritization framework"
  - label: "Backlog management (grooming)"
    description: "Organize features, epics, technical debt"
  - label: "Analytics and metrics (track performance)"
    description: "Define KPIs, setup dashboards, data analysis"
  - label: "Feedback management (user input)"
    description: "Collect, organize, and act on feedback"

Q2: Data Maturity

question: "How data-driven is your product process?"
header: "Data"
multiSelect: false
options:
  - label: "No metrics yet"
    description: "Need to define KPIs and setup analytics"
  - label: "Basic analytics (usage, retention)"
    description: "Have metrics but need better insights"
  - label: "A/​B testing and experiments"
    description: "Running experiments, optimizing based on data"
  - label: "Advanced (cohorts, funnels, ML)"
    description: "Sophisticated analytics, predictive models"

Q3: Product Type

question: "What type of product are you managing?"
header: "Product"
multiSelect: false
options:
  - label: "SaaS B2B"
    description: "Enterprise or SMB software product"
  - label: "SaaS B2C"
    description: "Consumer software, focus on growth"
  - label: "AI-native or AI agent product"
    description: "LLM-powered, agentic AI considerations"
  - label: "Traditional software (non-SaaS)"
    description: "On-premise, desktop, or mobile app"

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Decision Tree

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Core Methodologies (16)

Prioritization

• feature-prioritization-rice - RICE scoring (Reach × Impact × Confidence / Effort)
• feature-prioritization-moscow - Must/​Should/​Could/​Won't

Operations

• backlog-management - DEEP principles, Definition of Ready
• product-analytics - Metrics and data analysis
• feedback-management - User feedback collection and processing
• product-operations - Operational processes and workflows
• stakeholder-management - Goal → Actor → Impact → Deliverable
• technical-debt-management - Debt tracking and paydown

Lifecycle & Growth

• product-lifecycle - Intro/​Growth/​Maturity/​Decline stages
• product-led-growth - PLG strategies and tactics
• experimentation-at-scale - A/​B testing and experiments
• learning-speed-competitive-moat - Learning as competitive advantage

AI & Modern Product

• ai-native-product-development - AI-first product design
• agentic-ai-product-development - AI agent products
• product-explainability - Making products understandable
• blurred-roles-team-evolution - Modern team structures

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Common Sequences

• Backlog grooming: backlog-management → feature-prioritization-rice → technical-debt-management
• Metrics review: product-analytics → feedback-management → experimentation-at-scale
• Stakeholder sync: stakeholder-management → product-lifecycle → product-operations

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Related Skills

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Product Operations Sub-Skill v1.0 16 Methodologies