slm-show-patterns

# SuperLocalMemory: Show Patterns

Show what SuperLocalMemory has learned about your preferences, workflow, and coding identity.

## Usage

```bash
slm patterns list [threshold]
slm learning status
slm engagement
```

## Example Output

### Learned Patterns
```bash
$ slm patterns list 0.5
```

**Output:**
```
Learned Patterns (confidence >= 0.5)
=====================================

TECH PREFERENCES (cross-project):
  #1  preferred_framework: React        confidence: 0.92  (seen in 3 profiles)
  #2  preferred_language: Python         confidence: 0.88  (seen in 2 profiles)
  #3  preferred_backend: FastAPI         confidence: 0.85  (seen in 2 profiles)
  #4  testing_style: pytest              confidence: 0.78  (seen in 1 profile)
  #5  preferred_db: PostgreSQL           confidence: 0.71  (seen in 2 profiles)

WORKFLOW PATTERNS:
  #6  morning_sequence: recall -> code -> remember    frequency: 34
  #7  debug_sequence: recall -> recall -> remember    frequency: 18
  #8  review_sequence: list -> recall -> remember     frequency: 12

PROJECT CONTEXT:
  #9  active_project: superlocalmemory    last_seen: 2 hours ago
  #10 active_project: client-dashboard      last_seen: 1 day ago

Total: 10 patterns (7 high confidence)
```

### Learning Status
```bash
$ slm learning status
```

**Output:**
```
SuperLocalMemory V3 -- Learning System Status
==================================================
LightGBM:  installed (4.5.0)
SciPy:     installed (1.14.1)
ML Ranking:  available
Full Learning: available

Feedback signals: 247
Unique queries:   89
Patterns learned: 34 (12 high confidence)
Workflow patterns: 8
Sources tracked:  4
Models trained:   2
Learning DB size: 128 KB
```

### Engagement Metrics
```bash
$ slm engagement
```

**Output:**
```
SuperLocalMemory -- Engagement Health
======================================
Status: HEALTHY

This Week:
  Memories saved:    12
  Recalls performed: 28
  Memories marked useful: 8
  Feedback ratio:    28.6%

Trends:
  Recall frequency:  increasing (up 15% from last week)
  Save frequency:    stable
  Useful feedback:   increasing (up 40% from last week)

Streaks:
  Current daily streak: 5 days
  Longest streak:       14 days
```

## What the Patterns Mean

### Tech Preferences
Cross-project patterns that transfer between profiles. These represent your coding identity -- which frameworks, languages, and tools you consistently choose.

**How they are learned:**
- Extracted from memory content (mentions of frameworks, tools)
- Weighted by recency and frequency
- Confidence increases when the same preference appears across multiple profiles

**How they help:**
- When you `recall`, results matching your preferred stack rank higher
- Your AI tools can reference these to tailor suggestions

### Workflow Patterns
Sequences of actions you repeat. The system learns your work rhythm.

**Examples:**
- `recall -> code -> remember` = "Research, build, document" workflow
- `recall -> recall -> remember` = "Deep investigation" workflow

**How they help:**
- System predicts what you will need next
- Can pre-load relevant context based on your current workflow stage

### Engagement Health
Overall system usage metrics (fully local, zero telemetry).

**Healthy indicators:**
- Regular daily usage (streaks)
- Balanced save/recall ratio
- Increasing useful feedback

**Warning signs:**
- No recalls for 7+ days = stale memories
- No saves for 7+ days = not capturing knowledge
- Zero feedback = system cannot learn your preferences

## Correcting Patterns

If the system learned something wrong, correct it:

```bash
# See all patterns
slm patterns list

# Correct pattern #3 from "FastAPI" to "Django"
slm patterns correct 3 Django
```

The correction increases confidence to 1.0 and records the change history.

## Options

| Command | Description | Use Case |
|---------|-------------|----------|
| `slm patterns list` | All patterns (no threshold) | See everything learned |
| `slm patterns list 0.7` | High confidence only | See reliable patterns |
| `slm patterns correct <id> <value>` | Fix a wrong pattern | Override incorrect learning |
| `slm learning status` | System health | Check deps and stats |
| `slm learning retrain` | Force model retrain | After bulk feedback |
| `slm learning reset` | Delete all learning data | Fresh start (memories preserved) |
| `slm engagement` | Usage metrics | Track your engagement health |

## Use Cases

### 1. "What Have You Learned About Me?"
```bash
slm patterns list
# Shows all preferences, workflows, and project context
```

### 2. Pre-Session Context Loading
```bash
slm patterns context
# Returns structured context for AI tools to consume
```

### 3. Onboarding a New AI Tool
```bash
slm learning status
# Verify learning is active, then use your existing memories
# New tool benefits from all previously learned patterns
```

### 4. Weekly Review
```bash
slm engagement
# Check if you are using your memory system effectively
```

## Requirements

- SuperLocalMemory V3.7+
- Optional: `lightgbm` and `scipy` for ML-powered ranking
- Works without optional deps (uses rule-based ranking as fallback)

## Notes

- **Privacy:** All learning is local. Zero telemetry, zero cloud calls.
- **Separate storage:** Learning data lives in `learning.db`, separate from `memory.db`.
- **Non-destructive:** `slm learning reset` only deletes learning data, never memories.
- **Graceful degradation:** If learning deps are missing, core features work normally.

## Related Commands

- `slm recall` - Search memories (results ranked by learned patterns)
- `slm useful <id>` - Mark memory as useful (feedback for learning)
- `slm status` - Overall system status
- `slm patterns update` - Re-learn patterns from existing memories

---

**Created by:** [Varun Pratap Bhardwaj](https://github.com/varun369) (Solution Architect)
**Project:** SuperLocalMemory V3
**License:** AGPL-3.0 (see [LICENSE](../../LICENSE))
**Repository:** https://github.com/qualixar/superlocalmemory

*Open source doesn't mean removing credit. Attribution must be preserved per AGPL-3.0 terms.*