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
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*