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Agent Skill · 研究检索

shadows-context-optimizer(阴影上下文优化器)

shadows-context-optimizer 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。

shadows-context-optimizer 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 14,199。

总安装 8,751周安装 361GitHub Stars 公开资料未说明下载 2,859核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.1.0

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:shadows-context-optimizer(阴影上下文优化器)
来源仓库:https://github.com/nakedoshadow/shadows-context-optimizer
安装命令:
openclaw skills install shadows-context-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 shadows-context-optimizer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于优化令牌使用和上下文窗口效率的工具,减少冗余提示内容。适合在达到上下文限制时提升代理性能与响应质量。通过 clawhub 安装并使用 openclaw skills install shadows-context-optimizer 命令启用。使用前应确认是否会触发联网或文件操作,避免越权访问。

建议结合项目实际需求核验其压缩策略与适用性。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install shadows-context-optimizer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

shadows-context-optimizer 怎么安装?

在终端执行 openclaw skills install shadows-context-optimizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

shadows-context-optimizer 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

shadows-context-optimizer 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install shadows-context-optimizer 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

shadows-context-optimizer 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

shadows-context-optimizer 用的人多吗?

公开数据是累计安装 8,751 次,近一周安装 361 次,下载 2,859 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

shadows-context-optimizer 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
shadows-context-optimizer

阴影上下文优化器 · 本页

8,751—执行命令MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
context-optimizer
description
Token and context window optimization — compact prompts, reduce redundancy, prioritize critical context. Use when hitting context limits or to improve agent efficiency.
metadata
{ "openclaw": { "emoji": "⚡", "homepage": "https://clawhub.ai/NakedoShadow", "os": ["darwin", "linux", "win32"] } }

Context Optimizer — Token Economy Protocol

Version: 1.1.0 | Author: Shadows Company | License: MIT


WHEN TO TRIGGER

  • Context window approaching capacity (>70% usage)
  • Agent responses becoming slower or less coherent
  • User says "optimize context", "compact", "reduce tokens"
  • Working with very large codebases
  • Multi-file operations causing context bloat

WHEN NOT TO TRIGGER

  • Short conversations with plenty of context remaining
  • Simple single-file operations

PREREQUISITES

No binaries required. This is a pure reasoning skill about optimizing context window usage. It provides strategies and patterns — it does not execute commands or access external systems.


PRINCIPLES

1. Reference Over Inline

Instead of reading entire files into context, reference them:

  • "The auth module at src/auth/index.ts handles..." instead of pasting 500 lines
  • Read only the specific functions/sections needed
  • Use line ranges: Read file.py lines 45-80 instead of the whole file

2. DRY Prompts — Zero Duplication

  • Never repeat information already in system context
  • Don't re-describe tools you already know about
  • Don't re-state project conventions that are in CLAUDE.md/SOUL.md
  • If a fact was established earlier, reference it, don't restate it

3. Lazy-Load Strategy

  • Load detailed context only when needed for the current task
  • Use subagents/sub-tasks for exploration (protects main context)
  • Delegate research to agents, keep main context for execution

4. Smart File Reading

WRONG: Read the entire 2000-line file
RIGHT: Read lines 150-200 where the function is defined

WRONG: Read all 15 config files
RIGHT: Read only the config relevant to current task

WRONG: Grep the entire codebase for "import"
RIGHT: Grep specific directories for specific patterns

5. Output Compression

When reporting results:

  • Lead with the answer, not the reasoning
  • Skip filler words and unnecessary transitions
  • Use tables for comparative data
  • Use bullet points for lists, not paragraphs

TECHNIQUES

Technique 1 — Context Audit

Assess current context usage:

  1. Count how many files have been read in this session
  2. Identify which file contents are still relevant to the current task
  3. Determine what information can be summarized instead of kept verbatim
  4. Flag redundant tool results that repeat already-known information

Technique 2 — Compaction

When context is high:

  1. Summarize completed work (keep outcomes, drop process details)
  2. Drop file contents that are no longer needed for the active task
  3. Keep only active task context in working memory
  4. Preserve critical state: decisions made, errors encountered, current objectives

Technique 3 — Subagent Delegation

For research-heavy tasks:

  1. Spawn a subagent for codebase exploration
  2. Subagent returns only findings (not raw file contents)
  3. Main context stays clean for implementation
  4. Multiple subagents can run in parallel for independent queries

Technique 4 — Structured Responses

WRONG (100 tokens):
"I've looked at the file and after careful analysis I believe that
the issue is related to the authentication middleware where the
token validation function doesn't properly handle expired tokens."

RIGHT (30 tokens):
"Bug: `validateToken()` in auth middleware doesn't handle expired
tokens. Fix: add expiry check at line 45."

ANTI-PATTERNS TO AVOID

Anti-PatternFix
Reading whole files when you need 10 linesUse offset + limit
Listing all MCP serversAgent already knows them
Repeating deny rulesAlready in settings
Describing the OS/environmentAlready in system context
Re-reading files read earlierSummarize and reference
Multiple searches for one queryOne well-crafted search
Verbose status updatesConcise milestone updates

RULES

  1. Minimum viable context — load only what's needed NOW
  2. Summarize, don't accumulate — compress completed work
  3. Delegate exploration — use subagents for research
  4. Direct answers — skip preamble, lead with the point
  5. 3-search maximum — never use more than 3 search tools for one query

SECURITY CONSIDERATIONS

This skill is purely advisory — it provides strategies for token optimization. It does not execute commands, read files, make network calls, modify configuration, or store data. Zero risk profile.

  • Commands executed: None
  • Data read: None (advisory reasoning only)
  • Network access: None
  • Persistence: None
  • Credentials: None required
  • File modification: None

OUTPUT FORMAT

Apply the techniques above inline during agent operation. No separate report is generated — the skill manifests as improved efficiency in the agent's behavior: shorter responses, fewer tool calls, targeted file reads, and minimal context consumption.


Published by Shadows Company — "We work in the shadows to serve the Light."

平台分布

OpenClaw

72.07%
按下载量换算2,060

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install shadows-context-optimizer 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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