Claw Compactor v6.0 — 50%+ savings through rule-based compression, dictionary encoding, session observation compression, and progressive context loading.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。
SkillHub CLI
skillhub install aeromomo/cut-your-tokens-97percent-savings-on-session-transcripts-via-observation-extraction打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。
"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 │
│...
这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。
"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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