linkedin-post

linkedin-post is a skill for Claude Code, Codex from langchain-ai/langgraph-101. It costs 36 tokens per session (434 once invoked), scanned A, original, MIT.

A writing guide for creating LinkedIn posts from research findings or a supplied topic. It defines a professional, conversational structure with an opening hook, short paragraphs, and a closing question or call to action.

In plain words
What is it for?
Use it to draft professional posts of about 150–300 words with relevant emojis, a closing prompt, and three to five hashtags.
Why use it?
It helps turn information into a readable LinkedIn post that presents the main insight early and is formatted for the platform.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

About the project

LangGraph 101 is a collection of hands-on notebooks that teach the fundamentals and advanced patterns of building agents with LangChain, LangGraph, and Deep Agents. It is for developers learning to create agents with tools, memory, streaming, human oversight, multi-agent designs, and production workflows. The catalogue add-ons support workflows for the repository's agent-building topics.

langchain-ai/langgraph-101 · 610 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/langchain-ai/langgraph-101/linkedin-post
Any agent
npx skills add langchain-ai/langgraph-101 --skill linkedin-post
Clone the repo
git clone --depth 1 https://github.com/langchain-ai/langgraph-101

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for linkedin-post

README.md
[![agentmods](https://agentmods.dev/badge/skills/langchain-ai/langgraph-101/linkedin-post.svg)](https://agentmods.dev/skills/langchain-ai/langgraph-101/linkedin-post)
Your own site
<a href="https://agentmods.dev/skills/langchain-ai/langgraph-101/linkedin-post"><img src="https://agentmods.dev/badge/skills/langchain-ai/langgraph-101/linkedin-post.svg" alt="Measured on agentmods" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 434 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00036 $0.00434
Opus 5 $0.00018 $0.00217
Sonnet 5 $0.00007 $0.00087
Haiku 4.5 $0.00004 $0.00043

Measured 6d ago against content hash 1be26ac4a52a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

linkedin-post scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/deep_agent/skills/linkedin-post/SKILL.md · 68 lines

What it actually says

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
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 68 lines · 36 tokens per session scan A 1be26ac4a52a

Subscribe to this mod's changes

linkedin-post is a skill published in the GitHub repository langchain-ai/langgraph-101 (610 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 434 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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