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/thatrebeccarae/claude-marketingWrote 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/thatrebeccarae/claude-marketing/llms-txt)<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/llms-txt"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/llms-txt/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/thatrebeccarae/claude-marketing/llms-txt"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/llms-txt.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 | $0.00041 | $0.02493 |
| Opus 5 | $0.00020 | $0.01247 |
| Sonnet 5 | $0.00008 | $0.00499 |
| Haiku 4.5 | $0.00004 | $0.00249 |
Grade A, and why
llms-txt 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 9d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
llms.txt Generator
Generate and maintain llms.txt files that help AI answer engines surface your project accurately.
When to Use
- Launching a new open-source project or documentation site
- Major documentation restructure or content overhaul
- Improving your project's visibility in AI search (ChatGPT, Perplexity, Google AI Overviews)
- Onboarding a project to AI-friendly discoverability standards
- Periodic refresh after significant repo changes
What Is llms.txt
llms.txt is a plain-text markdown file placed in a project's root that gives LLMs a curated map of the project's most important content. Think of it as robots.txt for AI comprehension — instead of telling crawlers where they can go, it tells them what matters and how the project is organized.
The specification was proposed by Answer.AI and is documented at llmstxt.org. Adoption is growing across developer tools, documentation sites, and open-source projects. Projects with an llms.txt are easier for AI to understand, cite, and recommend accurately.
Usage
/llms-txt generate [repo-path]
Scan a repository and generate a new llms.txt file. If no path is provided, uses the current working directory.
/llms-txt audit [repo-path]
Check an existing llms.txt for completeness, broken links, stale descriptions, and missing high-priority content. Produces a report with specific recommendations.
/llms-txt update [repo-path]
Refresh an existing llms.txt based on current repo state. Preserves manually curated descriptions while adding new content and removing references to deleted files.
Procedure
Step 1: Scan Repo Structure
Identify all documentation-relevant files in the repository:
- README.md (root and significant subdirectories)
docs/directory and its contents- API documentation (OpenAPI specs, API reference pages)
- Tutorials, guides, and getting-started content
- CHANGELOG.md, CONTRIBUTING.md, FAQ.md
- Architecture and design decision docs
- Configuration and deployment guides
- Example directories with their own READMEs
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.
- 9d ago First seen · 241 lines · 41 tokens per session scan A 9941ccc6634f
llms-txt is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (133 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 2,493 once invoked, about $0.0002 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 2026-08-30.
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.
cli-error-handling
CLI command error handling patterns.
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.