内容创作
#research
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) |