PatrickJS/awesome-cursorrules is a collection of Markdown rule files that give Cursor AI editor project-specific instructions about code, frameworks, workflows, and standards. Developers use it to find reusable guidance for shaping Cursor’s behavior in different kinds of software projects.
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/PatrickJS/awesome-cursorrulesWrote 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/patrickjs/awesome-cursorrules/hol-hedera-typescript-cursorrules-prompt-file)<a href="https://agentmods.dev/rules/patrickjs/awesome-cursorrules/hol-hedera-typescript-cursorrules-prompt-file"><img src="https://agentmods.dev/badge/rules/patrickjs/awesome-cursorrules/hol-hedera-typescript-cursorrules-prompt-file.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 | $0.01824 | $0.01824 |
| Opus 5 | $0.00912 | $0.00912 |
| Sonnet 5 | $0.00365 | $0.00365 |
| Haiku 4.5 | $0.00182 | $0.00182 |
Grade A, and why
hol-hedera-typescript-cursorrules-prompt-file 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cursorrules — 92% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hashgraph Online (HOL) Development Rules
You are an expert TypeScript developer building applications with Hashgraph Online (HOL) - the open-source SDK for AI agents and decentralized applications on Hedera.
Technology Stack
Core:
- Language: TypeScript (strict mode)
- Runtime: Node.js 20+
- Package Manager: pnpm
- Testing: Jest with @swc/jest
HOL SDK:
- @hashgraphonline/standards-sdk - Core SDK for HCS standards and Registry Broker
- @hol-org/hashnet-mcp - MCP server for AI agent integration
Frontend (when applicable):
- Framework: Next.js 14+ (App Router)
- UI: shadcn/ui + Tailwind CSS
- Icons: react-icons (Lucide preferred)
Coding Standards
TypeScript Requirements
- NEVER use
any- define proper interfaces - NEVER use
as anycasting - use type guards - ALWAYS define explicit return types
- ALWAYS use generics for flexible code
- Validate external data with type guards or zod
File Naming
- Use kebab-case:
registry-client.ts,topic-manager.ts - Test files:
__tests__/registry-client.test.ts - Components:
registry-browser.tsx
Code Style
- Max 500 lines per file - split larger files
- No nested ternaries
- No inline comments - use JSDoc only
- No console.log - use Logger from standards-sdk
- Prettier formatting required
React Patterns (when applicable)
- NO render functions like
renderContent() - NO inline callbacks in JSX
- NO hooks in loops/conditionals
- ALWAYS use separate child components
- ALWAYS define Props interfaces
HOL SDK Usage
RegistryBrokerClient - Initialization
import { RegistryBrokerClient } from '@hashgraphonline/standards-sdk';
const client = new RegistryBrokerClient({
baseUrl: 'https://api.hol.org',
apiKey: process.env.HOL_API_KEY,
});
RegistryBrokerClient - Search Agents
import { RegistryBrokerClient, SearchParams, Logger } from '@hashgraphonline/standards-sdk';
const logger = new Logger({ module: 'AgentSearch', level: 'info' });
const client = new RegistryBrokerClient();
const searchParams: SearchParams = {
q: 'weather',
registry: 'hcs-10',
limit: 10,
page: 1,
};
const results = await client.search(searchParams);
logger.info('Search completed', { total: results.total });
results.hits.forEach(agent => {
logger.debug('Agent found', { name: agent.name, description: agent.description });
});
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 · 263 lines · 1,824 tokens per session scan A 72767bc9c2f6
hol-hedera-typescript-cursorrules-prompt-file is a cursor rule published in the GitHub repository PatrickJS/awesome-cursorrules (40,734 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 1,824 tokens to every session, about $0.0091 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.
Other cursor rules, from other repositories
03-mcp-configuration
Defines the available Model Context Protocol (MCP) servers and their capabilities based on the user's mcp.json. Use when interacting with any MCP Server command.
vercel-ai-sdk
Vercel AI SDK: streaming AI responses, tool calling, structured output.
langchain
LangChain: chains, agents, memory, tools.
pydantic
Pydantic: BaseModel, validators, Field, Settings.
pytorch
PyTorch: neural networks, model training, GPU optimization.
tensorflow
TensorFlow: Keras, model training, production deployment.