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context-compressorcontext compressor 搜索

Agent Skill

context-compressor 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

364

周安装

15

GitHub Stars

4

下载量

119
CodexClaudeCursorGemini CLI

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

unknown

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-compressor(context compressor 搜索)
来源仓库:https://github.com/fatih-developer/fth-skills
仓库路径:skills/context-compressor
安装命令:
npx skills add https://github.com/fatih-developer/fth-skills --skill context-compressor
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/fatih-developer/fth-skills --skill context-compressor

简介

context-compressor 提供内容压缩算法,保留关键信息同时削减 70% 冗余数据。

  • 适用于在 Codex、Claude、Cursor、Gemini CLI 中优化长文本传输与内存占用。
  • 按内容类型(对话、代码、研究笔记)采用差异化压缩策略,确保语义完整性。
  • 压缩后输出约为原文 30% 长度,可用于摘要生成或上下文预加载场景。
  • 不建议用于法律文书或高精度数值数据,此类场景需保持原始格式不变。

SKILL.md

Context Compressor Protocol

Reduce content by 70% — preserve critical information, discard repetition and noise. Target: compressed output should be ~30% of original size with preserved information density.


Workflow

1. Detect content type
2. Mark critical elements (these are never discarded)
3. Apply type-specific compression
4. Verify 70% target
5. Present compressed output

Step 1: Detect Content Type

Content TypeDetection Criteria
Conversation historyUser/assistant message pairs
Code fileFunction/class structures, syntax
Research resultsURLs, source references, data points
Meeting notes / documentHeaders, bullet lists, decisions

Step 2: Mark Critical Elements

These elements are never discarded:

  • Decisions & conclusions: "We decided to use X", "Y was chosen"
  • Errors & fixes: Discovered bugs, applied solutions
  • Numerical data: Dates, version numbers, metrics, amounts
  • Dependencies: "A must finish before B" constraints
  • Action items: Who does what, when
  • Current state: Latest version, current config, last decision

Step 3: Type-Specific Compression

Conversation History

Discard: Greetings, acknowledgments ("OK", "Got it", "Thanks"), repeated explanations, intermediate reasoning that contradicts the final conclusion.

Keep: User's clear requests, decisions and rationale, error messages and fixes, current task status.

Format:

[CONVERSATION SUMMARY — N messages -> M lines]
Context: [what the task is, 1 sentence]
Decisions: [bullet list]
Current status: [where things stand]
Pending: [open questions if any]

Code File

Discard: Comments (except docstrings), excessive blank lines, long import lists (consolidate), temporary debug prints.

Keep: All function/class signatures (with parameters), return types and critical type annotations, exception handling, configuration constants. Summarize function bodies as single-line pseudocode.

Format:

# [COMPRESSED — original: ~N lines -> now: ~M lines]
class ClassName:
    """[Original docstring]"""
    def method_name(self, param: Type) -> ReturnType:
        # [What it does — 1 line summary]
        ...

Research / Web Results

Discard: Duplicate information, generic background, verbose URLs (use domain name), out-of-scope quotes.

Keep: Concrete data points, primary source findings, contradictory findings (keep both), directly relevant findings.

Format:

[RESEARCH SUMMARY — N sources -> M items]
Topic: [what was researched]

Key findings:
- [Finding 1] (Source: domain.com)
- [Finding 2] (Source: domain.com)

Contradictions:
- [Source A says X, Source B says Y]

Missing / unverified:
- [Information not found]

Documents / Meeting Notes

Discard: Intro/closing paragraphs (if no content), repetitive statements, generic commentary.

Keep: Decisions made, action items (who, what, when), alternatives discussed and why rejected, next steps.


Step 4: Verify 70% Target

Original size   : ~N words / lines / tokens
Compressed size : ~M words / lines / tokens
Compression     : ~X% reduction

Target: 70% [Achieved / Below target]

If below target (< 50% reduction): re-scan for repetition, compress background more aggressively, convert long examples to single-line references.


Output Header

Every compression starts with:

CONTEXT COMPRESSOR
Type    : [content type]
Before  : ~N [words/lines]
After   : ~M [words/lines]
Saving  : ~X% reduction

When to Skip

  • Content is already short (< 200 words / 50 lines)
  • User said "all details matter"
  • Legal, medical, or financial documents (information loss risk too high)

Guardrails

  • Never discard decisions or error resolutions — these are the most valuable context.
  • Preserve contradictions — if two sources disagree, keep both.
  • Show compression ratio — the user must see how much was removed.
  • Cross-skill: works with memory-ledger (can compress ledger entries) and agent-reviewer (provides compressed history for retrospective).

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

34.53%
按下载量换算41

Claude

28.61%
按下载量换算34

Cursor

18.81%
按下载量换算22

Gemini CLI

9.83%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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

继续浏览同类 Skills