social-media

social-media is a skill for Claude Code, Codex from langchain-ai/deepagents. It costs 58 tokens per session (1,156 once invoked), scanned A, original, MIT.

A writing assistant for social media content, including posts and multi-part threads. It can create opening hooks, hashtag suggestions, content structures, and accompanying images.

In plain words
What is it for?
Use it for LinkedIn posts, Twitter/X threads, captions, social posts, and repurposing content for social media, with the content and image saved as files.
Why use it?
It helps turn a topic or existing material into organized content suited to social platforms. Required research provides information to use before drafting.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one.

Good fit Use it for LinkedIn posts, Twitter/X threads, captions, social posts, and repurposing content for social media, with the content and image saved as files.

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Install with agentmods
npx agentmods add skills/langchain-ai/deepagents/social-media
About the project

Deep Agents is an extensible agent harness that provides an out-of-the-box agent for long, multi-step tasks, with features such as planning, sub-agents, filesystem access, context management, memory, and human approval of tool calls. It is used by developers building agents with different language models, and its catalogue entries extend the harness with reusable skills, MCP servers, and instructions.

langchain-ai/deepagents · 29,185 stars · on GitHub · docs.langchain.com

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.

Any agent
npx skills add langchain-ai/deepagents --skill social-media
Clone the repo
git clone --depth 1 https://github.com/langchain-ai/deepagents

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 social-media

README.md
[![agentmods](https://agentmods.dev/badge/skills/langchain-ai/deepagents/social-media/github.svg)](https://agentmods.dev/skills/langchain-ai/deepagents/social-media)
Your own site
<a href="https://agentmods.dev/skills/langchain-ai/deepagents/social-media"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/social-media/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for social-media

Your own site · 80×15
<a href="https://agentmods.dev/skills/langchain-ai/deepagents/social-media"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/social-media.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,156 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 18 Mar 2026
  • Snyk pass 18 Mar 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00058 $0.01156
Opus 5 $0.00029 $0.00578
Sonnet 5 $0.00012 $0.00231
Haiku 4.5 $0.00006 $0.00116

Measured 10d ago against content hash bf03ac229f36, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

social-media 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 10d 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.

examples/content-builder-agent/skills/social-media/SKILL.md · 186 lines

How it starts

The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Social Media Content Skill

Research First (Required)

Before writing any social media content, you MUST delegate research:

  1. Use the task tool with subagent_type: "researcher"
  2. In the description, specify BOTH the topic AND where to save:
task(
    subagent_type="researcher",
    description="Research [TOPIC]. Save findings to research/[slug].md"
)

Example:

task(
    subagent_type="researcher",
    description="Research renewable energy trends in 2025. Save findings to research/renewable-energy.md"
)
  1. After research completes, read the findings file before writing

Output Structure (Required)

Every social media post MUST have both content AND an image:

LinkedIn posts:

linkedin/
└── <slug>/
    ├── post.md        # The post content
    └── image.png      # REQUIRED: Generated visual

Twitter/X threads:

tweets/
└── <slug>/
    ├── thread.md      # The thread content
    └── image.png      # REQUIRED: Generated visual

Example: A LinkedIn post about "prompt engineering" → linkedin/prompt-engineering/

You MUST complete both steps:

  1. Write the content to the appropriate path
  2. Generate an image using generate_image and save alongside the post

A social media post is NOT complete without its image.

Platform Guidelines

LinkedIn

Format:

  • 1,300 character limit (show more after ~210 chars)
  • First line is crucial - make it hook
  • Use line breaks for readability
  • 3-5 hashtags at the end

Tone:

  • Professional but personal
  • Share insights and learnings
  • Ask questions to drive engagement
  • Use "I" and share experiences

Structure:

[Hook - 1 compelling line]

[Empty line]

[Context - why this matters]

[Empty line]

[Main insight - 2-3 short paragraphs]

[Empty line]

[Call to action or question]

#hashtag1 #hashtag2 #hashtag3

Twitter/X

Format:

  • 280 character limit per tweet
  • Threads for longer content (use 1/🧵 format)
  • No more than 2 hashtags per tweet

Read the full file on GitHub · 186 lines

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. 10d ago First seen · 186 lines · 58 tokens per session scan A bf03ac229f36

Subscribe to this mod's changes

social-media is a skill published in the GitHub repository langchain-ai/deepagents (29,185 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 1,156 once invoked, about $0.0003 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.