Build production LangChain agents with create_agent(), tools, and middleware patterns.
Works with
Use create_agent() with model, tools list, and system prompt; configure state persistence with checkpointer and thread_id for conversation memory across invocations
Define tools via @tool decorator (Python) or tool() function (TypeScript) with clear descriptions so agents know when to call them
Add middleware like HumanInTheLoopMiddleware for approval workflows, custom error handling, and human-in-
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlangchain-fundamentalsExecute the skills CLI command in your project's root directory to begin installation:
Fetches langchain-fundamentals from langchain-ai/langchain-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate langchain-fundamentals. Access via /langchain-fundamentals in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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total installs
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this week
525
GitHub stars
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upvotes
Run in your terminal
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installs
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this week
525
stars
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Registry listing for langchain-fundamentals matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend langchain-fundamentals for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: langchain-fundamentals is focused, and the summary matches what you get after install.
langchain-fundamentals reduced setup friction for our internal harness; good balance of opinion and flexibility.
langchain-fundamentals fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
langchain-fundamentals has been reliable in day-to-day use. Documentation quality is above average for community skills.
langchain-fundamentals is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
langchain-fundamentals has been reliable in day-to-day use. Documentation quality is above average for community skills.
langchain-fundamentals fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
langchain-fundamentals has been reliable in day-to-day use. Documentation quality is above average for community skills.
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