Claw Compactor v6.0 — 50%+ savings through rule-based compression, dictionary encoding, session observation compression, and progressive context loading.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install aeromomo/cut-your-tokens-97percent-savings-on-session-transcripts-via-observation-extractionExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
"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 │
│...
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
"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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