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clawpressorclawpressor 搜索

Agent Skill

clawpressor 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

29,540

周安装

1,195

GitHub Stars

公开资料未说明

下载量

9,273
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:clawpressor(clawpressor 搜索)
来源仓库:https://github.com/beboxos/clawpressor
安装命令:
openclaw skills install clawpressor
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install clawpressor

简介

压缩 OpenClaw 会话上下文以减少令牌使用并延长会话生命周期。使用 NLP 摘要 (Sumy) 智能压缩对话历史记录,同时保留基本上下文。在提及会话压缩、令牌减少、上下文清理或会话大小超过安全阈值 (~300KB) 时触发。在以下情况下使用:(1) OpenClaw 接近 50% 上下文限制,(2) 由于上下文较大,会话速度变慢,(3) 减少过多令牌消耗导致的 API 成本,(4) 延长会话生命周期而不强制重新启动。

SKILL.md

name
clawpressor
description
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 exceeds safe thresholds (~300KB). Use when (1) OpenClaw approaches 50% context limit, (2) Sessions are slowing down due to large context, (3) Reducing API costs from excessive token consumption, (4) Extending session lifetime without forced reboots.

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

SituationAction
Context at 30-40%Plan compression soon
Context at 50%URGENT — OpenClaw will force compact
Session > 300KBCompress to restore performance
Slow responsesLarge context likely the cause
High API costsCompress 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

MetricBeforeAfterGain
Messages1686-96%
Size347 KB12 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

IssueSolution
"Sumy not installed"Run pip install sumy and NLTK downloads
No session foundCheck ~/.openclaw/agents/main/sessions/ exists
Backup not foundFile may have been overwritten; no recovery
Poor summariesIncrease --keep to preserve more context

Credits

  • Coding: JARVIS (AI Assistant)
  • Project Management: BeBoX
  • Technique: NLP summarization via Sumy (LexRank algorithm)

Related

  • See memory/openclaw-context-optimization.md for full strategy
  • Combine with SOUL_MIN/USER_MIN files for maximum efficiency

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

89.36%
按下载量换算8,286

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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