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linkedin-post

Write a LinkedIn post based on research findings or a given topic. Use this skill when asked to create LinkedIn content, professional posts, or thought leadership pieces.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

获取

https://deepseekmodel.com/api/download.php?id=langchain-ai-langgraph-101-agents-deep-agent-skills-linkedin-post-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name linkedin-post description Write a LinkedIn post based on research findings or a given topic. Use this skill when asked to create LinkedIn content, professional posts, or thought leadership pieces. LinkedIn Post Skill Format Hook : Start with a bold opening line that grabs attention (this appears before the "see more" cut) Body : 3-5 short paragraphs, each 1-2 sentences Use line breaks between paragraphs for readability Include 1-2 relevant emojis per paragraph (don't overdo it) End with a call-to-action or question to drive engagement Add 3-5 relevant hashtags at the bottom Tone Professional but conversational Share insights, not just information Use "I" statements and personal perspective where appropriate Avoid jargon unless the audience expects it Length Ideal: 150-300 words LinkedIn truncates after ~210 characters, so the first line must hook the reader Template [Bold hook / surprising stat / question] [Context -- why this matters] [Key insight 1] [Key insight 2] [Key insight 3 or personal takeaway] [Call to action / question for engagement] #hashtag1 #hashtag2 #hashtag3 Example Most AI agents fail not because of the model -- but because of context management. After researching the latest agent frameworks, one pattern keeps emerging: the best agents treat their context window like a scarce resource. Here's what separates good agents from great ones: 1. They offload intermediate results to a filesystem instead of keeping everything in context 2. They delegate to subagents for isolation -- the main agent only sees summaries 3. They use progressive disclosure -- loading instructions only when relevant The shift from "bigger context window" to "smarter context management" is where the real breakthroughs are happening. What patterns have you seen work best in your agent architectures? #AIAgents #LangChain #LangGraph #ContextEngineering
Agent 识别该技能的关键词,点击任意一个即可复制。

该技能未提供触发词。

下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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