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.
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.
npx skills add langchain-ai/deepagents --skill social-mediagit clone --depth 1 https://github.com/langchain-ai/deepagentsWrote 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.
[](https://agentmods.dev/skills/langchain-ai/deepagents/social-media)<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.
<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>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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:
- Use the
tasktool withsubagent_type: "researcher" - 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"
)
- 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:
- Write the content to the appropriate path
- Generate an image using
generate_imageand save alongside the post
A social media post is NOT complete without its image.
Platform Guidelines
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
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.
- 10d ago First seen · 186 lines · 58 tokens per session scan A bf03ac229f36
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.
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dawn
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cite-sources
How to attribute every factual claim to a corpus document.
synthesize-findings
How to merge researcher sub-answers into one cited report.
code-reviewer
A code-review guide written in Chinese for examining source code for bugs, security risks, performance problems, and quality issues.