linkedin-post

Category: Content & Multimedia | Uploader: langchain-ai | Downloads: 0 | Version: v1.0（Latest）

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.

Changelog: Source: GitHub https://github.com/langchain-ai/langgraph-101

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Current level: agents/deep_agent/skills/linkedin-post/

📄 SKILL.md 1.9 KB

SKILL.md

---
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
```

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linkedin-post

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