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/maccman/growth-agentsWrote 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/maccman/growth-agents/project-guidelines)<a href="https://agentmods.dev/rules/maccman/growth-agents/project-guidelines"><img src="https://agentmods.dev/badge/rules/maccman/growth-agents/project-guidelines/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/maccman/growth-agents/project-guidelines"><img src="https://agentmods.dev/badge/rules/maccman/growth-agents/project-guidelines.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 | $0.00760 | $0.00760 |
| Opus 5.5 | $0.00304 | $0.00304 |
| Sonnet 5.5 | $0.00152 | $0.00152 |
| Haiku 4.5 | $0.00076 | $0.00076 |
Grade A, and why
project-guidelines 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.
How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Agents - Project Guidelines
Project Overview
This is a marketing ops and growth automation playground powered by Cursor AI. Agents are designed to perform complex, multi-step marketing tasks including lead enrichment, SEO audits, campaign analysis, content generation, competitive intelligence, and reporting.
Users interact with agents through Cursor's Chat to direct marketing tasks.
Assume users are non-technical marketing ops and growth leaders. Be friendly, skip jargon, and never ask them to run terminal commands or choose a programming language — just handle it.
Agent Development Process
- Planning First: Always come up with a good plan before executing any task. You MUST get sign off on the plan from the user before continuing.
- Write and run typescript or python code: Write and run typescript/python code to test your ideas. For anything that is best done programmatically, like math or other types of logic, then just write a quick TypeScript/python program and run it.
- ALWAYS prefer running executables vs writing scripts: Running executables is faster than writing scripts.
- Data from the web: Use the
web_searchandmcp_browsermcptools to get data from the web. Never use themcp_browsermcptool to Google search - you should use the dedicatedweb_searchtool for that as it's more reliable. - Use unix principles: Use unix principles to build your scripts. Use
stdinandstdout. Use|to chain commands together. - Stream output: Stream output to the console. e.g. when processing CSVs, output line by line.
- Never alter a file in the
data/directory in-place: Always either produce a copy or, if you're editing textual data, usestdinandstdoutto edit the data.
AI SDK
To communicate with AI providers, use the Vercel AI SDK.
- Use Vercel AI SDK: Use the AI SDK directly with providers like
@ai-sdk/openai,@ai-sdk/anthropic,@ai-sdk/google - No Custom Abstractions: Don't create custom AI client wrappers - use the SDK directly
- Environment variables for AI providers are automatically handled by Vercel AI SDK
- No need to manually check or validate API keys in code
- Standard variables:
OPENAI_API_KEY,ANTHROPIC_API_KEY,GOOGLE_GENERATIVE_AI_API_KEY
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 · 60 lines · 760 tokens per session scan A 14b1a70fd153
project-guidelines is a cursor rule published in the GitHub repository maccman/growth-agents (5 stars, last pushed 7mo ago), licensed MIT. It adds 760 tokens to every session, about $0.0030 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).