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 agentmods add skills/langchain-ai/deepagents/blog-postnpx skills add langchain-ai/deepagents --skill blog-postgit 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/blog-post)<a href="https://agentmods.dev/skills/langchain-ai/deepagents/blog-post"><img src="https://agentmods.dev/badge/skills/langchain-ai/deepagents/blog-post.svg" alt="Measured on agentmods" height="20"></a>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 | $0.01090 |
| Opus 5 | $0.00029 | $0.00545 |
| Sonnet 5 | $0.00012 | $0.00218 |
| Haiku 4.5 | $0.00006 | $0.00109 |
Grade A, and why
blog-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.
Copies of this mod
1 near-identical copy found in the catalogue:
- blog-post — 88% identical, 17 lines differ
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blog Post Writing Skill
Research First (Required)
Before writing any blog post, 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 the current state of AI agents in 2025. Save findings to research/ai-agents-2025.md"
)
- After research completes, read the findings file before writing
Output Structure (Required)
Every blog post MUST have both a post AND a cover image:
blogs/
└── <slug>/
├── post.md # The blog post content
└── hero.png # REQUIRED: Generated cover image
Example: A post about "AI Agents in 2025" → blogs/ai-agents-2025/
You MUST complete both steps:
- Write the post to
blogs/<slug>/post.md - Generate a cover image using
generate_imageand save toblogs/<slug>/hero.png
A blog post is NOT complete without its cover image.
Blog Post Structure
Every blog post should follow this structure:
1. Hook (Opening)
- Start with a compelling question, statistic, or statement
- Make the reader want to continue
- Keep it to 2-3 sentences
2. Context (The Problem)
- Explain why this topic matters
- Describe the problem or opportunity
- Connect to the reader's experience
3. Main Content (The Solution)
- Break into 3-5 main sections with H2 headers
- Each section covers one key point
- Include code examples, diagrams, or screenshots where helpful
- Use bullet points for lists
4. Practical Application
- Show how to apply the concepts
- Include step-by-step instructions if applicable
- Provide code snippets or templates
5. Conclusion & CTA
- Summarize key takeaways (3 bullets max)
- End with a clear call-to-action
- Link to related resources
Cover Image Generation
After writing the post, generate a cover image using the generate_cover tool:
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.
- 6d ago First seen · 135 lines · 58 tokens per session scan A 36f7f23aa3a9
blog-post is a skill published in the GitHub repository langchain-ai/deepagents (28,997 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 1,090 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.
Other skills, from other repositories
python-patterns
Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
refine
End-of-session reflection. Reviews friction encountered during the session and proposes updates to docs/ to capture lessons learned.
recover-from-failure
How to recover when a tool call fails — diagnose, not blindly retry.
workspace-conventions
Reminders about how Dawn's workspace tools behave and what the path-jail allows.
dawn
Build AI agents and workflows with the Dawn framework — the TypeScript meta-framework for LangGraph. Use when creating, editing, or debugging a Dawn app (routes, tools, state, agents, workflows, testing, deployment).
synthesize-findings
How to merge researcher sub-answers into one cited report.