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
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.
git clone --depth 1 https://github.com/sanjeed5/awesome-cursor-rules-mdcWrote 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/langgraph)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/langgraph"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/langgraph.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 | $0.00290 | $0.00290 |
| Opus 5 | $0.00145 | $0.00145 |
| Sonnet 5 | $0.00058 | $0.00058 |
| Haiku 4.5 | $0.00029 | $0.00029 |
Grade A, and why
langgraph 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 3d 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.
What it actually says
langgraph Best Practices
Code Organization and Structure
1. Explicit Graph Topology & Modularity
Define clear, single responsibilities for each node. Encapsulate common patterns into reusable subgraphs. Prefer Directed Acyclic Graphs (DAGs); use cycles only when essential for feedback loops.
✅ GOOD: Focused nodes, composable subgraphs
from langgraph.graph import StateGraph, END
from typing import TypedDict, List
class AgentState(TypedDict):
messages: List[str]
tool_output: str | None
def fetch_data_node(state: AgentState) -> AgentState:
return {"messages": state["messages"] + ["Data fetched."]}
def analyze_data_node(state: AgentState) -> AgentState:
return {"messages": state["messages"] + ["Data analyzed."]}
def build_subgraph():
builder = StateGraph(AgentState)
builder.add_node("fetch", fetch_data_node)
builder.add_node("analyze", analyze_data_node)
builder.add_edge("fetch", "analyze")
builder.set_entry_point("fetch")
builder.set_finish_point("analyze")
return builder.compile()
2. Functional API Preference
Prior
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.
- 3d ago First seen · 38 lines · 290 tokens per session scan A b75526288812
langgraph is a cursor rule published in the GitHub repository sanjeed5/awesome-cursor-rules-mdc (3,571 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 290 tokens to every session, about $0.0014 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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