Mobile Instinct v2 - Observational Learning

Cross-session observational learning that extracts patterns from your development workflow over time.

Overview

V2 instincts observe your sessions and extract patterns that emerge across multiple development activities. Unlike V1's immediate capture, V2 looks for:

Recurring architectural decisions
Problem-solving approaches
Code organization patterns
Testing strategies

Session Analysis

At session end, V2 analyzes:

Code changes: What was modified
Problem context: What issue was being solved
Solution approach: How it was resolved
Dependencies: What libraries/techniques were used

Pattern Categories

Architectural Patterns

Pattern	Detected By	Example
`layer-separation`	Consistent data/ui/domain separation	Repository + ViewModel + Composable
`dependency-injection`	Koin module patterns	factoryOf, viewModel
`navigation-pattern`	Compose Navigation usage	NavHost with routes
`state-management`	MVI/MVVM consistency	StateFlow + sealed classes

Problem-Solution Patterns

Pattern	Detected By	Example
`error-boundary`	Try-catch with UI feedback	Error state in Composable
`loading-state`	isLoading + Content pattern	Box with progress
`pagination`	LazyColumn with Pager	Paging 3 integration
`caching-strategy`	Repository layer caching	Cached repository pattern

Code Organization Patterns

Pattern	Detected By	Example
`feature-module`	Self-contained feature folders	feature/auth/ structure
`shared-UI`	Reusable Composables	ui/components/
`test-mirroring`	Test structure matching src	Parallel test folders
`naming-convention`	Consistent naming patterns	XxxViewModel, XxxScreen

Observation Windows

V2 uses sliding windows for pattern detection:

Window 1 (Current Session):    Immediate patterns
Window 2 (Last 5 Sessions):    Emerging patterns
Window 3 (Last 20 Sessions):   Established patterns
Window 4 (All Time):           Core patterns

Confidence Evolution

Session 1-3:    Experimental (0.1-0.3)
Session 4-10:   Validating (0.3-0.6)
Session 11-20:  Established (0.6-0.8)
Session 20+:    Best Practice (0.8-1.0)

Commands

View Observations

/instinct-status --v2
/instinct-status --observations

Shows:

Recent session observations
Emerging patterns (low confidence)
Established patterns (high confidence)
Pattern clusters by domain

Manual Observation

/instinct-observe "Used Ktor with retry pattern for API calls"

Manually add an observation for pattern learning.

Integration

V2 instincts are evaluated by:

Session hooks: hooks/instinct-hooks.json Stop event
Pattern extractor: agents/mobile-pattern-extractor.md
Pre-compact preservation: Maintains learning during context compression

Difference from V1

Aspect	V1	V2
Trigger	Code write	Session observation
Scope	Single file	Cross-file patterns
Timing	Immediate	End of session
Focus	Code patterns	Architectural patterns

Remember: V2 needs multiple sessions to build confidence. The more you develop, the smarter it gets.