**Requirements:** Python 3.9+. Optional: `pip install tiktoken` for exact token counts (falls back to heuristic).

## Architecture

## Commands

All commands: `python3 scripts/mem_compress.py <workspace> <command> [options]`

| Command | Description | Typical Savings |
|---------|-------------|-----------------|
| `full` | Complete pipeline (all steps in order) | 50%+ combined |
| `benchmark` | Dry-run performance report | — |
| `compress` | Rule-based compression | 4-8% |
| `dict` | Dictionary encoding with auto-codebook | 4-5% |
| `observe` | Session transcript → observations | ~97% |
| `tiers` | Generate L0/L1/L2 summaries | 88-95% on sub-agent loads |
| `dedup` | Cross-file duplicate detection | varies |
| `estimate` | Token count report | — |
| `audit` | Workspace health check | — |
| `optimize` | Tokenizer-level format fixes | 1-3% |

### Global Options
- `--json` — Machine-readable JSON output
- `--dry-run` — Preview changes without writing
- `--since YYYY-MM-DD` — Filter sessions by date
- `--auto-merge` — Auto-merge duplicates (dedup)

## Real-World Savings

| Workspace State | Typical Savings | Notes |
|---|---|---|
| Session transcripts (observe) | **~97%** | Megabytes of JSONL → concise observation MD |
| Verbose/new workspace | **50-70%** | First run on unoptimized workspace |
| Regular maintenance | **10-20%** | Weekly runs on active workspace |
| Already-optimized | **3-12%** | Diminishing returns — workspace is clean |

## cacheRetention — Complementary Optimization

Before compression runs, enable **prompt caching** for a 90% discount on cached tokens:

Compression reduces token count, caching reduces cost-per-token. Together: 50% compression + 90% cache discount = **95% effective cost reduction**.

## Heartbeat Automation

Run weekly or on heartbeat:

## Configuration

Optional `claw-compactor-config.json` in workspace root:

git clone https://github.com/aeromomo/claw-compactor.git
cd claw-compactor

# See how much you'd save (non-destructive)
python3 scripts/mem_compress.py /path/to/workspace benchmark

# Compress everything
python3 scripts/mem_compress.py /path/to/workspace full

┌─────────────────────────────────────────────────────────────┐
│                      mem_compress.py                        │
│                   (unified entry point)                     │
└──────┬──────┬──────┬──────┬──────┬──────┬──────┬──────┬────┘
       │      │      │      │      │      │      │      │
       ▼      ▼      ▼      ▼      ▼      ▼      ▼      ▼
  estimate compress  dict  dedup observe tiers  audit optimize
       └──────┴──────┴──┬───┴──────┴──────┴──────┴──────┘
                        ▼
                  ┌────────────────┐
                  │     lib/       │
                  │ tokens.py      │ ← tiktoken or heuristic
                  │ markdown.py    │ ← section parsing
                  │ dedup.py       │ ← shingle hashing
                  │ dictionary.py  │ ← codebook compression
                  │ rle.py         │ ← path/IP/enum encoding
                  │ tokenizer_     │
                  │   optimizer.py │ ← format optimization
                  │ config.py      │ ← JSON config
                  │ exceptions.py  │ ← error types
                  └────────────────┘

{
  "models": {
    "model-name": {
      "cacheRetention": "long"
    }
  }
}

## Memory Maintenance (weekly)
- python3 skills/claw-compactor/scripts/mem_compress.py <workspace> benchmark
- If savings > 5%: run full pipeline
- If pending transcripts: run observe

{
  "chars_per_token": 4,
  "level0_max_tokens": 200,
  "level1_max_tokens": 500,
  "dedup_similarity_threshold": 0.6,
  "dedup_shingle_size": 3
}