SkillHub

clawpressor

v1.0.0

Compress OpenClaw session context to reduce token usage and extend session lifetime. Uses NLP summarization (Sumy) to intelligently compact conversation history while preserving essential context. Triggers on mentions of session compression, token reduction, context cleanup, or when session size exc...

Sourced from ClawHub, Authored by beboxos

Installation

Please help me install the skill `clawpressor` from SkillHub official store. npx skills add beboxos/clawpressor

ClawPressor - Session Context Compressor

Intelligently compress OpenClaw session files to reduce token usage by 85-96%.

Author: JARVIS (AI Coder) | Managed by: BeBoX License: MIT | Version: 1.0.0

Quick Start

# Preview compression without changes
python3 scripts/compress.py --dry-run

# Apply compression
python3 scripts/compress.py --apply

# Restore from backup
python3 scripts/compress.py --restore

When to Use

Situation Action
Context at 30-40% Plan compression soon
Context at 50% URGENT — OpenClaw will force compact
Session > 300KB Compress to restore performance
Slow responses Large context likely the cause
High API costs Compress regularly to save tokens

How It Works

  1. Preserves recent context — Keeps last 5 messages intact for immediate context
  2. Summarizes old messages — Uses LexRank algorithm to extract key information
  3. Replaces with compact block — Single system message containing summary
  4. Creates backup — Original preserved as .backup file

Prerequisites

pip install sumy
python -c "import nltk; nltk.download('punkt_tab'); nltk.download('stopwords')"

Command Reference

# Find and compress latest session (dry-run)
python3 scripts/compress.py

# Compress specific session
python3 scripts/compress.py --session /path/to/session.jsonl --apply

# Keep more recent messages (default: 5)
python3 scripts/compress.py --keep 10 --apply

# Restore if something went wrong
python3 scripts/compress.py --restore

# View compression statistics
python3 scripts/compress.py --stats

Typical Results

Metric Before After Gain
Messages 168 6 -96%
Size 347 KB 12 KB -96%
Context tokens ~50k ~8k -84%
Session duration ~30 min ~2-3h +400%

Integration with Workflows

In HEARTBEAT.md:

## Context Maintenance (1x/jour)
- Check session size: `ls -lh ~/.openclaw/agents/main/sessions/*.jsonl`
- If > 200KB: `python3 skills/clawpressor/scripts/compress.py --apply`

Manual check:

# See current session stats
ls -lh ~/.openclaw/agents/main/sessions/*.jsonl | head -1

Safety

  • Always creates .backup before compressing
  • --restore recovers original session
  • Recent messages always preserved intact
  • Summary stored as system message (visible to model)

Troubleshooting

Issue Solution
"Sumy not installed" Run pip install sumy and NLTK downloads
No session found Check ~/.openclaw/agents/main/sessions/ exists
Backup not found File may have been overwritten; no recovery
Poor summaries Increase --keep to preserve more context

Credits

  • Coding: JARVIS (AI Assistant)
  • Project Management: BeBoX
  • Technique: NLP summarization via Sumy (LexRank algorithm)
  • See memory/openclaw-context-optimization.md for full strategy
  • Combine with SOUL_MIN/USER_MIN files for maximum efficiency