Individuation Scoring Individuation scoring measures your profile’s maturity across the memory system. Named after Carl Jung’s concept of individuation — the process of becoming a complete, integrated self — this subsystem tracks four components that together paint a picture of how effectively you use memory, learning, collaboration, and self-reflection.
Individuation scores are calculated via pure SQL queries with results cached in-memory for 60 minutes (configurable). No LLM calls are made during score calculation.
The Four Components Each component is scored from 0 to 25, for a total score of 0 to 100.
What it measures : How diverse and durable your memory store is.Sub-components :Sub-component Points Calculation Ring diversity 0-10 Number of distinct ring types used (x 2.5, max 4 types = 10) Decay survival 0-10 Ratio of memories past the compressed stage (x 20) Shock recovery 0-5 Ratio of shock memories with success_score > 0.5
How to improve :
Use different tool types to build memory across all ring types (procedures, habits, long-term, shocks)
Build high-quality memories that survive the decay engine
Learn from failures (shock memories with eventual recovery)
What it measures : How quickly you acquire new patterns and how confidently they are classified.Sub-components :Sub-component Points Calculation Weekly rate 0-10 Fresh memories per week (10+ = max) Classification confidence 0-10 Average auto-classification confidence (x 10) Reinforcement rate 0-5 Ratio of classified observations (x 5)
How to improve :
Record more observations during sessions using pluggedin_memory_observe
Provide clear, well-structured observations that classify with high confidence
Reinforce patterns by using tools consistently
What it measures : How much you give back to the collective knowledge base (CBP).Sub-components :Sub-component Points Calculation CBP patterns promoted 0-10 Patterns promoted to collective pool (max 10) Pattern diversity 0-10 Distinct ring types contributed (x 2.5) Feedback given 0-5 CBP feedback submissions (max 5)
How to improve :
Build successful workflows that get promoted to collective patterns
Contribute across different memory ring types
Rate CBP suggestions using pluggedin_cbp_feedback (confirm or reject)
What it measures : How actively you reflect on and use your memory system.Sub-components :Sub-component Points Calculation Memory search usage 0-10 Total access count across memories (log2 scale, max at 1024 accesses) Memory engagement 0-10 Average access count per memory (x2, capped at 10). Higher averages indicate more active re-use of stored knowledge. Dream consolidation 0-5 Number of dream consolidations performed
How to improve :
Search your memories regularly using pluggedin_memory_search
Access specific memories with pluggedin_memory_details
Allow dream processing to run (keep DREAM_ENABLED=true)
Five Maturity Levels Your total score maps to one of five maturity levels:
Level Score Range Description Analogy Nascent 0-20 Just getting started. Few memories, minimal interaction with the system. A seed planted but not yet sprouted Developing 21-40 Building initial patterns. Starting to use memory search and observe outcomes. First leaves appearing Established 41-60 Consistent memory usage. Contributing to collective patterns and using diverse tools. A young tree with branches Mature 61-80 Deep, diverse knowledge base. Active in collective learning and self-reflection. A full canopy providing shade Individuated 81-100 Full integration. Rich memory across all rings, active community contributor, high self-awareness. An ancient tree in a forest ecosystem
Weekly Trends Individuation scores are snapshot daily (automatically at session start) and compared over two-week windows:
Trend Condition Meaning Accelerating Recent week average > previous week by 3+ points Score is growing Stable Difference within 3 points Score is holding steady Decelerating Recent week average < previous week by 3+ points Score is declining
Actionable Tips The API returns a contextual tip based on your weakest component:
Weakest Component Tip Memory Depth ”Try using different tool types to build diverse memory across all rings.” Learning Velocity ”Record more observations during sessions to accelerate learning.” Collective Contribution ”Rate collective patterns and your successful workflows will help others.” Self-Awareness ”Search your memories more often — self-reflection strengthens understanding.”
API Reference Get Individuation Score GET /api/memory/individuation Authorization: Bearer < api_ke y >
Response :{ "success" : true , "data" : { "total" : 63 , "level" : "mature" , "weeklyTrend" : "accelerating" , "tip" : "Rate collective patterns and your successful workflows will help others." , "components" : { "memoryDepth" : 19 , "learningVelocity" : 17 , "collectiveContribution" : 8 , "selfAwareness" : 19 } } }
Get Score History GET /api/memory/individuation?history= true & days = 30 Authorization: Bearer < api_ke y >
Response :{ "success" : true , "data" : [ { "total" : 45 , "memoryDepth" : 12 , "learningVelocity" : 14 , "collectiveContribution" : 5 , "selfAwareness" : 14 , "maturityLevel" : "established" , "profileUuid" : "prof-123" , "snapshotDate" : "2026-02-01" }, { "total" : 63 , "memoryDepth" : 19 , "learningVelocity" : 17 , "collectiveContribution" : 8 , "selfAwareness" : 19 , "maturityLevel" : "mature" , "profileUuid" : "prof-123" , "snapshotDate" : "2026-03-01" } ] }
SDK Usage import { PluggedInClient } from 'pluggedinkit-js' ; const client = new PluggedInClient ({ apiKey: 'your-api-key' }); // Get current score const score = await client . jungian . getIndividuationScore (); console . log ( `Level: ${ score . level } ( ${ score . total } /100)` ); console . log ( `Trend: ${ score . weeklyTrend } ` ); console . log ( `Tip: ${ score . tip } ` ); console . log ( `Components:` ); console . log ( ` Memory Depth: ${ score . components . memoryDepth } /25` ); console . log ( ` Learning Velocity: ${ score . components . learningVelocity } /25` ); console . log ( ` Collective Contribution: ${ score . components . collectiveContribution } /25` ); console . log ( ` Self-Awareness: ${ score . components . selfAwareness } /25` ); // Get history for charting const history = await client . jungian . getIndividuationHistory ( 30 ); for ( const snapshot of history ) { console . log ( ` ${ snapshot . snapshotDate } : ${ snapshot . total } ( ${ snapshot . maturityLevel } )` ); }
The pluggedin_memory_individuation MCP tool returns your score in a format optimized for AI agent consumption:
Individuation Score: 63/100 (Mature) Trend: Accelerating Components: Memory Depth: 19/25 Learning Velocity: 17/25 Collective Contribution: 8/25 Self-Awareness: 19/25 Tip: Rate collective patterns and your successful workflows will help others.
Caching Individuation scores are cached in-memory with a configurable TTL (default 60 minutes). This means:
The first request after cache expiry triggers a fresh SQL calculation
Subsequent requests within the TTL return the cached result instantly
Session start automatically calculates and caches the score
Daily snapshots use the freshly calculated score (not the cache)
Configuration Variable Default Description INDIVIDUATION_ENABLEDtrueEnable/disable individuation scoring INDIVIDUATION_CACHE_TTL_MINUTES60In-memory cache TTL for scores INDIVIDUATION_HISTORY_DAYS90Lookback window for component calculation
Next Steps
Archetype System How patterns are classified by archetype
Jungian Intelligence Overview See how individuation ties into the full system
