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

# Learning Adapter

> Skill development and progress tracking

# Learning Adapter

The Learning adapter helps users develop skills with progress tracking and stuck detection.

## Installation

```python theme={null}
from sxth_mind import Mind
from sxth_mind.adapters import LearningAdapter

mind = Mind(adapter=LearningAdapter())
```

## Identity Types

| Type         | Description               | Teaching Style                   |
| ------------ | ------------------------- | -------------------------------- |
| `conceptual` | Needs to understand "why" | Explain theory first             |
| `hands_on`   | Learns by doing           | Jump to examples                 |
| `structured` | Follows curriculum        | Step-by-step progression         |
| `explorer`   | Self-directed discovery   | Provide resources, let them lead |

## Journey Stages

```
exploring → foundations → practicing → applying → deepening
              ↓                           ↓
           stuck ←────────────────────────┘
```

| Stage         | Tone          | Focus                          |
| ------------- | ------------- | ------------------------------ |
| `exploring`   | Welcoming     | Understand goals, assess level |
| `foundations` | Patient       | Core concepts, fundamentals    |
| `practicing`  | Encouraging   | Exercises, repetition          |
| `applying`    | Challenging   | Projects, real-world use       |
| `deepening`   | Collaborative | Advanced topics, edge cases    |
| `stuck`       | Supportive    | Identify blocker, simplify     |

## Pattern Detection

The Learning adapter tracks:

* **Exercises completed** — Practice volume
* **Projects completed** — Applied learning
* **Stuck indicators** — Repeated questions, frustration signals
* **Learning velocity** — Progress rate
* **Preferred format** — Examples vs. theory

## Nudge Templates

| Nudge               | Priority | Trigger                               |
| ------------------- | -------- | ------------------------------------- |
| `practice_reminder` | 5        | No practice in 3+ days                |
| `stuck_support`     | 8        | Detected frustration or repeated asks |
| `milestone`         | 4        | Completed exercise set or project     |
| `next_level`        | 5        | Ready to advance                      |

## Example Usage

```python theme={null}
import asyncio
from sxth_mind import Mind
from sxth_mind.adapters import LearningAdapter

async def main():
    mind = Mind(adapter=LearningAdapter())

    # Learning Python
    await mind.chat("user_1", "I want to learn Python", project_id="learn_python")
    await mind.chat("user_1", "What are list comprehensions?", project_id="learn_python")
    await mind.chat("user_1", "I don't understand, can you show an example?", project_id="learn_python")

    # The adapter notices preference for examples
    state = await mind.get_state("user_1", project_id="learn_python")
    print(f"Identity type: {state['user_mind'].get('identity_type')}")
    # → Likely "hands_on" based on asking for examples

asyncio.run(main())
```

## Multi-Topic Support

Track multiple learning topics:

```python theme={null}
await mind.chat("user_1", "Learning Python basics", project_id="learn_python")
await mind.chat("user_1", "Started a React course", project_id="learn_react")
await mind.chat("user_1", "Practicing SQL queries", project_id="learn_sql")

# Each topic has its own progress and stage
```

## Stuck Detection

The adapter detects when users are stuck:

```python theme={null}
# These signals trigger "stuck" stage:
# - Repeated similar questions
# - Phrases like "I don't understand", "confused", "stuck"
# - Long gaps in progress
# - Decreasing momentum

# Response adapts:
# - Simplify explanations
# - Offer alternative approaches
# - Break down into smaller steps
```

## Context Data

The adapter stores in `project_mind.context_data`:

```python theme={null}
{
    "topic": "python",
    "current_module": "loops",
    "exercises_completed": 12,
    "projects_completed": 1,
    "stuck_count": 2,
    "preferred_format": "examples",
}
```