> ## Documentation Index
> Fetch the complete documentation index at: https://docs.plugged.in/llms.txt
> Use this file to discover all available pages before exploring further.

# Individuation Scoring

> Per-profile maturity metrics with four components, five maturity levels, and trend tracking

# 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.

<Info>
  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.
</Info>

## The Four Components

Each component is scored from 0 to 25, for a total score of 0 to 100.

<Tabs>
  <Tab title="Memory Depth (0-25)">
    **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)
  </Tab>

  <Tab title="Learning Velocity (0-25)">
    **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
  </Tab>

  <Tab title="Collective Contribution (0-25)">
    **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)
  </Tab>

  <Tab title="Self-Awareness (0-25)">
    **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`)
  </Tab>
</Tabs>

## 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

```bash theme={null}
GET /api/memory/individuation
Authorization: Bearer <api_key>
```

**Response**:

```json theme={null}
{
  "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

```bash theme={null}
GET /api/memory/individuation?history=true&days=30
Authorization: Bearer <api_key>
```

**Response**:

```json theme={null}
{
  "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

<CodeGroup>
  ```typescript JavaScript theme={null}
  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})`);
  }
  ```

  ```python Python theme={null}
  from pluggedinkit import PluggedInClient

  client = PluggedInClient(api_key="your-api-key")

  # Get current score
  score = client.jungian.get_individuation_score()
  print(f"Level: {score.level} ({score.total}/100)")
  print(f"Trend: {score.weekly_trend}")
  print(f"Tip: {score.tip}")
  print(f"Components:")
  print(f"  Memory Depth: {score.components.memory_depth}/25")
  print(f"  Learning Velocity: {score.components.learning_velocity}/25")
  print(f"  Collective Contribution: {score.components.collective_contribution}/25")
  print(f"  Self-Awareness: {score.components.self_awareness}/25")

  # Get history for charting
  history = client.jungian.get_individuation_history(days=30)
  for snapshot in history:
      print(f"{snapshot.snapshot_date}: {snapshot.total} ({snapshot.maturity_level})")
  ```

  ```go Go theme={null}
  client := pluggedinkit.NewClient("your-api-key")
  ctx := context.Background()

  // Get current score
  score, err := client.Jungian.GetIndividuationScore(ctx)
  if err != nil {
      log.Fatal(err)
  }
  fmt.Printf("Level: %s (%d/100)\n", score.Level, score.Total)
  fmt.Printf("Trend: %s\n", score.WeeklyTrend)
  fmt.Printf("Tip: %s\n", score.Tip)
  fmt.Printf("Components:\n")
  fmt.Printf("  Memory Depth: %d/25\n", score.Components.MemoryDepth)
  fmt.Printf("  Learning Velocity: %d/25\n", score.Components.LearningVelocity)
  fmt.Printf("  Collective Contribution: %d/25\n", score.Components.CollectiveContribution)
  fmt.Printf("  Self-Awareness: %d/25\n", score.Components.SelfAwareness)

  // Get history for charting
  history, err := client.Jungian.GetIndividuationHistory(ctx, 30)
  if err != nil {
      log.Fatal(err)
  }
  for _, s := range history {
      fmt.Printf("%s: %d (%s)\n", s.SnapshotDate, s.Total, s.MaturityLevel)
  }
  ```
</CodeGroup>

### MCP Tool

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_ENABLED`           | `true`  | Enable/disable individuation scoring      |
| `INDIVIDUATION_CACHE_TTL_MINUTES` | `60`    | In-memory cache TTL for scores            |
| `INDIVIDUATION_HISTORY_DAYS`      | `90`    | Lookback window for component calculation |

## Next Steps

<CardGroup cols={2}>
  <Card title="Archetype System" icon="masks-theater" href="/guides/archetype-system">
    How patterns are classified by archetype
  </Card>

  <Card title="Jungian Intelligence Overview" icon="brain" href="/platform/jungian-intelligence">
    See how individuation ties into the full system
  </Card>
</CardGroup>