awesome-cursor-rules-mdc is a generator that creates Cursor MDC rule files from structured library information, using semantic search and language models to gather and organize guidance. Developers use it to produce reusable rules for libraries in Cursor, and the catalogue includes 200 of those rules.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sanjeed5/awesome-cursor-rules-mdcnpx agentmods add rules/sanjeed5/awesome-cursor-rules-mdc/langchain-jsWrote 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/rules/sanjeed5/awesome-cursor-rules-mdc/langchain-js)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/langchain-js"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/langchain-js.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 | $0.03839 | $0.03839 |
| Opus 5 | $0.01920 | $0.01920 |
| Sonnet 5 | $0.00768 | $0.00768 |
| Haiku 4.5 | $0.00384 | $0.00384 |
Grade A, and why
langchain-js 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 5d 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 — 520 lines — stays where its author put it; the contents beside it link to each section on GitHub.
langchain-js Best Practices
Building LLM-powered applications with langchain-js requires a disciplined approach to ensure maintainability, performance, and reliability. This guide outlines our team's definitive best practices.
1. Code Organization and Structure
Always prioritize modularity and explicit typing. Break down complex logic into small, reusable components.
1.1. Modular Components & Single Responsibility
Each LangChain component (Agent, Tool, Chain, Model) should reside in its own file or a dedicated module, adhering to the Single Responsibility Principle.
❌ BAD: Monolithic file
// src/agent.js
import { ChatOpenAI } from "@langchain/openai";
import { createAgent, tool } from "langchain";
import { z } from "zod";
const getStockPrice = tool(async ({ ticker }) => { /* ... */ }, { /* ... */ });
const getNews = tool(async ({ query }) => { /* ... */ }, { /* ... */ });
const model = new ChatOpenAI({ temperature: 0.7 });
const agent = createAgent({
model,
tools: [getStockPrice, getNews],
});
export async function runFinancialAgent(input) {
return agent.invoke(input);
}
✅ GOOD: Modular, reusable components
// src/tools/getStockPrice.ts
import { tool } from "langchain";
import { z } from "zod";
export const getStockPriceTool = tool(
async ({ ticker }: { ticker: string }) => {
// Simulate API call
if (ticker === "AAPL") return "$170.00";
return "Price not found.";
},
{
name: "get_stock_price",
description: "Get the current stock price for a given ticker symbol.",
schema: z.object({
ticker: z.string().describe("The stock ticker symbol (e.g., AAPL)"),
}),
}
);
// src/tools/getNews.ts
import { tool } from "langchain";
import { z } from "zod";
export const getNewsTool = tool(
async ({ query }: { query: string }) => {
// Simulate API call
return `Latest news for ${query}: Market is up!`;
},
{
name: "get_news",
description: "Get the latest news for a given query.",
schema: z.object({
query: z.string().describe("The news query"),
}),
}
);
// src/agents/financialAgent.ts
import { ChatOpenAI } from "@langchain/openai";
import { createAgent } from "langchain";
import { getStockPriceTool } from "../tools/getStockPrice";
import { getNewsTool } from "../tools/getNews";
const model = new ChatOpenAI({ temperature: 0.7 });
export const financialAgent = createAgent({
model,
tools: [getStockPriceTool, getNewsTool],
});
// src/index.ts (or wherever the agent is invoked)
import { financialAgent } from "./agents/financialAgent";
async function main() {
const result = await financialAgent.invoke({
messages: [{ role: "user", content: "What's the price of AAPL and the latest market news?" }],
});
console.log(result);
}
main();
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.
- 5d ago First seen · 520 lines · 3,839 tokens per session scan A a3222686804f
langchain-js is a cursor rule published in the GitHub repository sanjeed5/awesome-cursor-rules-mdc (3,570 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 3,839 tokens to every session, about $0.0192 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 2026-09-03.
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prompt-routing
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prompting-for-qe
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llm-zod-jsonschema
Best Practice for LLM Output Parsing with Zod and JSON Schema.
prompt-evals
Prompt eval fixtures — case design, assertions, versioning, CI gates, no PII in golden data.