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/grafana)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/grafana"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/grafana.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 | $0.02431 | $0.02431 |
| Opus 5 | $0.01215 | $0.01215 |
| Sonnet 5 | $0.00486 | $0.00486 |
| Haiku 4.5 | $0.00243 | $0.00243 |
Grade A, and why
grafana 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 4d 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grafana Best Practices
Our team treats Grafana dashboards, alerts, and data sources as first-class code artifacts. This means applying software engineering principles: version control, peer review, automated testing, and CI/CD. Adhere strictly to these guidelines to ensure our observability platform remains robust, maintainable, and actionable.
1. Code Organization and Structure
Define all Grafana resources declaratively using Observability as Code (OaC). This enables Git-based workflows, automated provisioning, and consistent deployments.
1.1. Declarative Configuration (OaC)
Always define dashboards, alerts, and data sources in JSON/YAML files. Store these in Git and provision them via Grafana's API. Avoid manual UI configuration for anything beyond initial prototyping.
❌ BAD: Manual UI changes, no version history. ✅ GOOD: Git-versioned JSON/YAML files, automated provisioning.
// my-service-dashboard.json
{
"apiVersion": 1,
"title": "My Service Overview",
"uid": "my-service-overview",
"panels": [
// ... panel definitions ...
],
"templating": {
"list": [
// ... variables ...
]
}
}
1.2. Utilize Foundation SDKs
For complex or dynamically generated dashboards, prefer using Grafana Foundation SDKs (Go, Python, TypeScript) to programmatically define resources. This provides strong typing, validation, and better code reusability than raw JSON manipulation.
// dashboard.ts (using TypeScript SDK)
import { Dashboard, Row, Panel, GraphPanel } from '@grafana/sdk';
const dashboard = new Dashboard({
title: 'Service Health',
uid: 'service-health',
panels: [
new Row({
panels: [
new GraphPanel({
title: 'CPU Utilization',
targets: [{ expr: 'sum(rate(node_cpu_seconds_total{mode="idle"}[5m])) by (instance)' }],
yAxis: { min: 0 }
})
]
})
]
});
console.log(JSON.stringify(dashboard.toGrafanaJson(), null, 2));
1.3. Templated Variables
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.
- 4d ago First seen · 249 lines · 2,431 tokens per session scan A 4c037b02434b
grafana 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 2,431 tokens to every session, about $0.0122 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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Apache Superset development standards and guidelines for Cursor IDE.
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CLI command error handling patterns.
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Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
control-plane-descriptors
Control plane descriptor and instance implementation patterns.