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context-window-optimizer上下文窗口优化器

Agent Skill

context-window-optimizer 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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

5,132

周安装

216

GitHub Stars

公开资料未说明

下载量

1,797
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:context-window-optimizer(上下文窗口优化器)
来源仓库:https://github.com/klemenska/context-window-optimizer
安装命令:
openclaw skills install context-window-optimizer
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 context-window-optimizer

简介

context-window-optimizer 通过总结旧对话与提取关键事实优化上下文使用效率。

  • 保留当前上下文的同时归档历史决策,减少冗余信息占用。
  • 适合长期项目中上下文容量紧张与历史信息干扰的场景。
  • 安装前需设置摘要频率与保留策略,避免重要细节被误删。
  • 适用于高复杂度任务中上下文资源紧张与知识沉淀的双重需求。

SKILL.md

name
context-window-optimizer
description
Optimize context window usage by summarizing old conversation segments, extracting key facts and decisions to permanent memory, and keeping current context lean. Triggers when: (1) conversation history grows beyond ~50 messages or context feels heavy; (2) before long or complex tasks; (3) after significant decisions or work completions; (4) when explicitly asked to optimize context, compact context, or clean up context.

Context Window Optimizer

Manage context strategically to prevent token waste and keep conversations effective.

Core Principle

Context is a shared resource. Keep it lean so there's room for actual work.

When to Optimize

  • Conversation exceeds ~50 messages
  • Context feels heavy before a new task
  • Starting a complex multi-step task
  • After significant decisions or completions
  • Explicit request to optimize/compact

Optimization Workflow

Step 1: Assess Context State

Run the analyzer to get context metrics:

python3 scripts/analyze_context.py --session current

This reports:

  • Message count and approximate token count
  • Age of oldest message
  • Density score (signal vs noise)

Step 2: Identify Optimization Targets

Look for:

  • Old已完成 tasks with verbose logs
  • Repeated explanations of same concept
  • Off-topic tangents
  • Raw tool outputs that could be summarized
  • Decisions that should move to permanent memory

Step 3: Extract to Memory

Decisions → MEMORY.md or relevant project file:

## Decisions (from 2026-03-25 session)
- Chose PostgreSQL over MongoDB for project X
- Agreed on 3-day sprint cadence
- User prefers detailed explanations, not summaries

Key facts → appropriate domain/project file:

## Project X Facts
- Tech stack: React + Node + Postgres
- Main user pain point: slow onboarding
- Current velocity: 5 story points/sprint

Patterns → ~/self-improving/memory.md:

## User Preferences
- Always explain the "why" before the "what"
- Prefers bullet points over paragraphs

Step 4: Summarize Dense Segments

For long work sessions, create a summary instead of keeping all details:

## Session Summary: 2026-03-25

### Work Completed
- Set up authentication flow
- Fixed memory leak in worker process
- Designed new API schema

### Decisions Made
- Use JWT over sessions (simpler, scales better)
- Defer caching to v2 (not blocking)

### Open Questions
- Final tech stack for notifications (push vs polling)
- Need user feedback on onboarding flow

### Next Steps
- Implement auth endpoints
- Write tests for worker
- Schedule design review

Step 5: Archive, Don't Delete

Never delete context — archive it:

  • Move summaries to memory/YYYY-MM-DD.md
  • Keep pointers in session for recovery
  • Use [[archived:filename.md]] notation

Context Density Rules

Content TypeAction
Completed tasksSummarize outcome, archive details
DecisionsExtract to MEMORY.md or project file
Key factsExtract to relevant domain/project
Tool logsSummarize if successful, keep if debugging
Repeated conceptsRemove duplicates, keep one canonical
Off-topicSkip or summarize in notes
System promptsNever touch
Skills metadataOnly load relevant ones

Quick Commands

TaskCommand
Analyze current contextpython3 scripts/analyze_context.py --session current
Summarize sessionpython3 scripts/summarize_session.py --session current --output summary.md
Extract decisionspython3 scripts/extract_decisions.py --session current

Files

  • scripts/analyze_context.py — Context metrics and optimization suggestions
  • scripts/summarize_session.py — Create session summary
  • scripts/extract_decisions.py — Pull out decisions and key facts
  • references/patterns.md — Common summarization patterns

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

88.58%
按下载量换算1,592

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

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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