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.

Context Discovery

Auto-Investigation

Detect existing product operations artifacts:

Signal	How to Check	What It Tells Us
Backlog	`Glob("*/.aidocs/backlog/")` or `Glob("*/backlog/")`	Feature backlog exists
Prioritization	`Grep("RICE\	MoSCoW\
Analytics	`Grep("analytics\	metrics\
Feedback	`Glob("*/feedback/")` or `Grep("user feedback")`	Feedback process exists
Tech debt	`Glob("*/tech-debt/")` or `Grep("technical debt")`	Tech debt managed
Experiments	`Grep("A/B test\	experiment")`
Lifecycle docs	`Grep("lifecycle\	maturity\
Stakeholder map	`Grep("stakeholder")`	Stakeholder management

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"

Decision Tree

If you need...	Use	File
Prioritize (data-driven)	feature-prioritization-rice	feature-prioritization-rice.md
Prioritize (quick)	feature-prioritization-moscow	feature-prioritization-moscow.md
Manage backlog	backlog-management	backlog-management.md
Track metrics	product-analytics	product-analytics.md
Manage feedback	feedback-management	feedback-management.md
Lifecycle stage	product-lifecycle	product-lifecycle.md
Ops processes	product-operations	product-operations.md
Stakeholder alignment	stakeholder-management	stakeholder-management.md
Technical debt	technical-debt-management	technical-debt-management.md
Growth strategy	product-led-growth	product-led-growth.md
A/B testing	experimentation-at-scale	experimentation-at-scale.md
Learning velocity	learning-speed-competitive-moat	learning-speed-competitive-moat.md
AI products	ai-native-product-development	ai-native-product-development.md
AI agents	agentic-ai-product-development	agentic-ai-product-development.md
Explainability	product-explainability	product-explainability.md
Team evolution	blurred-roles-team-evolution	blurred-roles-team-evolution.md

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

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

Related Skills

Skill	Relationship
faion-product-manager	Parent orchestrator
faion-product-manager:planning	Sibling (MVP/roadmaps)
faion-project-manager	Execution coordination
faion-business-analyst	Requirements analysis

Product Operations Sub-Skill v1.0 16 Methodologies

faion-product-operations