Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Memory Pruner
Automatically prune and compact agent memory files to prevent unbounded growth. Circular buffer for logs, importance-based retention for state, and configura...
skill openclawclawhub Free
Automatically prune and compact agent memory files to prevent unbounded growth. Circular buffer for logs, importance-based retention for state, and configura...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/memory_pruner.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.1.0
Provenance
- Publisher
- Trypto1019
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Memory Pruner
Keep your agent's memory lean. Automatically prune logs, compact state files, and enforce size limits so your agent never runs out of disk or context window.
Why This Exists
Agents accumulate memory files over time. Logs grow unbounded. State files collect stale entries. Eventually your boot-up reads 50K tokens of memory and half of it is outdated. Memory Pruner enforces limits and keeps only what matters.
Prune a memory file (keep last N lines)
python3 {baseDir}/scripts/memory_pruner.py prune --file ~/wake-state.md --max-lines 200
Prune a log directory (circular buffer, keep last N files)
python3 {baseDir}/scripts/memory_pruner.py prune-logs --dir ~/agents/logs/ --keep 7
Compact a state file (remove sections matching a pattern)
python3 {baseDir}/scripts/memory_pruner.py compact --file ~/wake-state.md --remove-before "2026-02-14"
Check memory sizes
python3 {baseDir}/scripts/memory_pruner.py stats --dir ~/
Dry run (show what would be pruned)
python3 {baseDir}/scripts/memory_pruner.py prune --file ~/wake-state.md --max-lines 200 --dry-run
Features
Line-based pruning: Keep last N lines of any file Log rotation: Circular buffer for log directories (keep last N files, delete oldest) Date-based compaction: Remove entries older than a cutoff date Size limits: Enforce max file sizes in bytes Dry run mode: Preview changes before applying Stats: Overview of memory file sizes and growth rates
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts
- SKILL.md
- scripts/memory_pruner.py