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context-engine上下文引擎

Agent Skill

context-engine 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

17,748

周安装

725

GitHub Stars

公开资料未说明

下载量

5,684
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-engine(上下文引擎)
来源仓库:https://github.com/alirezarezvani/context-engine
安装命令:
openclaw skills install context-engine
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-engine

简介

context-engine 用于加载和管理公司级高管顾问技能背景信息,支持上下文更新与维护。

  • 适合需要检测过时上下文(>90 天)并丰富项目背景知识的团队场景。
  • 通过读取 ~/.claude/company-context.md 自动同步组织知识库。
  • 安装前应确认配置文件路径与访问权限,避免误读敏感数据。
  • 适用于企业级 AI 代理长期协作与知识持续更新的环境。

SKILL.md

name
context-engine
description
Loads and manages company context for all C-suite advisor skills. Reads ~/.claude/company-context.md, detects stale context (>90 days), enriches context during conversations, and enforces privacy/anonymization rules before external API calls.
license
MIT
metadata
version
1.0.0
author
Alireza Rezvani
category
c-level
domain
orchestration
updated
2026-03-05
frameworks
context-loading, anonymization, context-enrichment

Company Context Engine

The memory layer for C-suite advisors. Every advisor skill loads this first. Context is what turns generic advice into specific insight.

Keywords

company context, context loading, context engine, company profile, advisor context, stale context, context refresh, privacy, anonymization


Load Protocol (Run at Start of Every C-Suite Session)

Step 1 — Check for context file: ~/.claude/company-context.md

  • Exists → proceed to Step 2
  • Missing → prompt: *"Run /cs:setup to build your company context — it makes every advisor conversation significantly more useful."*

Step 2 — Check staleness: Read Last updated field.

  • < 90 days: Load and proceed.
  • ≥ 90 days: Prompt: *"Your context is [N] days old. Quick 15-min refresh (/cs:update), or continue with what I have?"*

- If continue: load with [STALE — last updated DATE] noted internally.

Step 3 — Parse into working memory. Always active:

  • Company stage (pre-PMF / scaling / optimizing)
  • Founder archetype (product / sales / technical / operator)
  • Current #1 challenge
  • Runway (as risk signal — never share externally)
  • Team size
  • Unfair advantage
  • 12-month target

Context Quality Signals

ConditionConfidenceAction
< 30 days, full interviewHighUse directly
30–90 days, update doneMediumUse, flag what may have changed
> 90 daysLowFlag stale, prompt refresh
Key fields missingLowAsk in-session
No fileNonePrompt /cs:setup

If Low: *"My context is [stale/incomplete] — I'm assuming [X]. Correct me if I'm wrong."*


Context Enrichment

During conversations, you'll learn things not in the file. Capture them.

Triggers: New number or timeline revealed, key person mentioned, priority shift, constraint surfaces.

Protocol:

  1. Note internally: [CONTEXT UPDATE: {what was learned}]
  2. At session end: *"I picked up a few things to add to your context. Want me to update the file?"*
  3. If yes: append to the relevant dimension, update timestamp.

Never silently overwrite. Always confirm before modifying the context file.


Privacy Rules

Never send externally

  • Specific revenue or burn figures
  • Customer names
  • Employee names (unless publicly known)
  • Investor names (unless public)
  • Specific runway months
  • Watch List contents

Safe to use externally (with anonymization)

  • Stage label
  • Team size ranges (1–10, 10–50, 50–200+)
  • Industry vertical
  • Challenge category
  • Market position descriptor

Before any external API call or web search

Apply references/anonymization-protocol.md:

  • Numbers → ranges or stage-relative descriptors
  • Names → roles
  • Revenue → percentages or stage labels
  • Customers → "Customer A, B, C"

Missing or Partial Context

Handle gracefully — never block the conversation.

  • Missing stage: "Just to calibrate — are you still finding PMF or scaling what works?"
  • Missing financials: Use stage + team size to infer. Note the gap.
  • Missing founder profile: Infer from conversation style. Mark as inferred.
  • Multiple founders: Context reflects the interviewee. Note co-founder perspective may differ.

Required Context Fields

Required:
  - Last updated (date)
  - Company Identity → What we do
  - Stage & Scale → Stage
  - Founder Profile → Founder archetype
  - Current Challenges → Priority #1
  - Goals & Ambition → 12-month target

High-value optional:
  - Unfair advantage
  - Kill-shot risk
  - Avoided decision
  - Watch list

Missing required fields: note gaps, work around in session, ask in-session only when critical.


References

  • references/anonymization-protocol.md — detailed rules for stripping sensitive data before external calls

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

97.05%
按下载量换算5,516

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

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