Borrowing it
Nothing to install: this file belongs to CSOAI-ORG/marketing-analytics-ai-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/CSOAI-ORG/marketing-analytics-ai-mcp/main/.cursorrulesgit clone --depth 1 https://github.com/CSOAI-ORG/marketing-analytics-ai-mcpWrote 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/csoai-org/marketing-analytics-ai-mcp/cursorrules)<a href="https://agentmods.dev/rules/csoai-org/marketing-analytics-ai-mcp/cursorrules"><img src="https://agentmods.dev/badge/rules/csoai-org/marketing-analytics-ai-mcp/cursorrules/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/csoai-org/marketing-analytics-ai-mcp/cursorrules"><img src="https://agentmods.dev/badge/rules/csoai-org/marketing-analytics-ai-mcp/cursorrules.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00080 | $0.00080 |
| Opus 5.5 | $0.00032 | $0.00032 |
| Sonnet 5.5 | $0.00016 | $0.00016 |
| Haiku 4.5 | $0.00008 | $0.00008 |
Grade A, and why
cursorrules 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 1mo 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
marketing-analytics-ai-mcp - Auto-trigger Rules
When the user asks about marketing analytics, use marketing-analytics-ai-mcp tools: campaign_roi, ab_test_analyze, funnel_optimizer, attribution_model, ad_copy_generator
MCP server for marketing analytics ai mcp operations
Install: pip install marketing-analytics-ai-mcp
By MEOK AI Labs — MIT licensed.
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.
- 1mo ago First seen · 10 lines · 80 tokens per session scan A f0dc3f607eb4
cursorrules is a cursor rule published in the GitHub repository CSOAI-ORG/marketing-analytics-ai-mcp (0 stars, last pushed 22d ago), licensed MIT. It adds 80 tokens to every session, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
config-validation-and-normalization
Config validation and normalization responsibilities (defineConfig normalizes, the orm section schema validates and resolves paths, the loader reports per-section diagnostics).