Claw Compactor v6.0 — 50%+ savings through rule-based compression, dictionary encoding, session observation compression, and progressive context loading.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install aeromomo/cut-your-tokens-97percent-savings-on-session-transcripts-via-observation-extractionInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
"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.
full) runs everything in optimal order| # | 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.
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).
┌─────────────────────────────────────────────────────────────┐
│ mem_compress.py │
│...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
"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.
full) runs everything in optimal order| # | 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.
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).
┌─────────────────────────────────────────────────────────────┐
│ mem_compress.py │
│...
# 🦞 Claw Compactor  *"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 ```bash 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 │ │...
# 🦞 Claw Compactor  *"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 ```bash 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 │ │...
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