SkillHub

cut-your-tokens-97percent-savings-on-session-transcripts-via-observation-extraction

v6.0.0

Claw Compactor v6.0 — 50%+ savings through rule-based compression, dictionary encoding, session observation compression, and progressive context loading.

Sourced from ClawHub, Authored by aeromomo

Installation

Please help me install the skill `cut-your-tokens-97percent-savings-on-session-transcripts-via-observation-extraction` from SkillHub official store. npx skills add aeromomo/cut-your-tokens-97percent-savings-on-session-transcripts-via-observation-extraction

🦞 Claw Compactor

!Claw Compactor Banner

"Cut your tokens. Keep your facts."

Cut your AI agent's token spend in half. One command compresses your entire workspace — memory files, session transcripts, sub-agent context — using 5 layered compression techniques. Deterministic. Mostly lossless. No LLM required.

Features

  • 5 compression layers working in sequence for maximum savings
  • Zero LLM cost — all compression is rule-based and deterministic
  • Lossless roundtrip for dictionary, RLE, and rule-based compression
  • ~97% savings on session transcripts via observation extraction
  • Tiered summaries (L0/L1/L2) for progressive context loading
  • CJK-aware — full Chinese/Japanese/Korean support
  • One command (full) runs everything in optimal order

5 Compression Layers

# Layer Method Savings Lossless?
1 Rule engine Dedup lines, strip markdown filler, merge sections 4-8% ✅
2 Dictionary encoding Auto-learned codebook, $XX substitution 4-5% ✅
3 Observation compression Session JSONL → structured summaries ~97% ❌*
4 RLE patterns Path shorthand ($WS), IP prefix, enum compaction 1-2% ✅
5 Compressed Context Protocol ultra/medium/light abbreviation 20-60% ❌*

*Lossy techniques preserve all facts and decisions; only verbose formatting is removed.

Quick Start

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

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

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      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
                  └────────────────┘

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:

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

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:

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

Cron example:

0 3 * * 0 cd /path/to/skills/claw-compactor && python3 scripts/mem_compress.py /path/to/workspace full

Configuration

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

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

All fields optional — sensible defaults are used when absent.

Artifacts

File Purpose
memory/.codebook.json Dictionary codebook (must travel with memory files)
memory/.observed-sessions.json Tracks processed transcripts
memory/observations/ Compressed session summaries
memory/MEMORY-L0.md Level 0 summary (~200 tokens)

FAQ

Q: Will compression lose my data? A: Rule engine, dictionary, RLE, and tokenizer optimization are fully lossless. Observation compression and CCP are lossy but preserve all facts and decisions.

Q: How does dictionary decompression work? A: decompress_text(text, codebook) expands all $XX codes back. The codebook JSON must be present.

Q: Can I run individual steps? A: Yes. Every command is independent: compress, dict, observe, tiers, dedup, optimize.

Q: What if tiktoken isn't installed? A: Falls back to a CJK-aware heuristic (chars÷4). Results are ~90% accurate.

Q: Does it handle Chinese/Japanese/Unicode? A: Yes. Full CJK support including character-aware token estimation and Chinese punctuation normalization.

Troubleshooting

  • FileNotFoundError on workspace: Ensure path points to workspace root (contains memory/ or MEMORY.md)
  • Dictionary decompression fails: Check memory/.codebook.json exists and is valid JSON
  • Zero savings on benchmark: Workspace is already optimized — nothing to do
  • observe finds no transcripts: Check sessions directory for .jsonl files
  • Token count seems wrong: Install tiktoken: pip3 install tiktoken

Credits

  • Inspired by claude-mem by thedotmack
  • Built by Bot777 🤖 for OpenClaw

License

MIT