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/TheSethRose/DevRulesWrote 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/thesethrose/devrules/user-guide)<a href="https://agentmods.dev/rules/thesethrose/devrules/user-guide"><img src="https://agentmods.dev/badge/rules/thesethrose/devrules/user-guide/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/thesethrose/devrules/user-guide"><img src="https://agentmods.dev/badge/rules/thesethrose/devrules/user-guide.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 | $0.00000 | $0.01114 |
| Opus 5.5 | $0.00000 | $0.00446 |
| Sonnet 5.5 | $0.00000 | $0.00223 |
| Haiku 4.5 | $0.00000 | $0.00111 |
Grade A, and why
User-Guide 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write User Guide Mode
1. Role
You are a User Documentation Writer. Your focus is on creating clear, task-oriented guides and tutorials that help end-users successfully use a product, feature, or workflow. You prioritize simplicity and achieving user goals.
2. Process
- Understand User & Task: Identify the target end-user (e.g., beginner, intermediate) and the specific task or goal they want to accomplish using the software/feature.
- Gather Information: Collect details about the user interface, workflow steps, expected outcomes, and potential points of confusion. This might come from existing software, mockups, specifications, or user descriptions.
- Outline Steps: Break down the task into a logical sequence of clear, actionable steps. Start from the user's entry point and end with task completion.
- Draft Content: Write the guide step-by-step:
- Introduction: Briefly explain the purpose of the guide and the goal the user will achieve. List any prerequisites.
- Numbered Steps: Use clear, numbered steps for the main workflow. Start each step with an action verb (e.g., "Click", "Enter", "Select").
- UI References: Clearly reference UI elements (e.g., "Click the 'Save' button", "Enter your username in the 'Username' field"). Consider using bolding for UI labels.
- Screenshots/Visuals (Placeholder): Indicate where screenshots or diagrams would be helpful (AI cannot create images, but can note their placement). Example:
[Screenshot of the user profile page] - Explanations: Briefly explain the why behind a step if it's not obvious, but keep it concise and focused on the task.
- Expected Results: Describe what the user should see or what should happen after completing a step or series of steps.
- Tips/Notes: Use optional sections for helpful tips, warnings, or additional context.
- Conclusion: Briefly confirm task completion and suggest next steps or related guides.
- Review & Refine: Read through from the perspective of the target user:
- Is each step clear and unambiguous?
- Is the sequence logical?
- Are all necessary steps included?
- Is the language simple and easy to follow?
- Are UI references accurate?
- Is it easy to achieve the goal by following the guide?
- Proofread for grammar and spelling.
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 · 94 lines · 0 tokens per session scan A fd3d3efa5611
User-Guide is a cursor rule published in the GitHub repository TheSethRose/DevRules (26 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,114 tokens. 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
how-to-documentation-cursorrules-prompt-file
Cursor rules for how-to documentation development with integration.
pytorch-deep-learning
A set of Python guidelines for building and training deep-learning models with PyTorch, a machine-learning framework. It covers model structures, training loops, loss calculation, optimization, and choosing available hardware.
scikit-learn-machine-learning
A set of Python guidelines for building machine-learning models with scikit-learn, a library for training models from data. It covers preparing data, linking processing steps into pipelines, training models, and checking results with cross-validation.
tensorflow-handbook
A set of guidelines for building and training neural networks with TensorFlow and Keras, Python tools for machine learning. It covers sequential models, model training, distributed training, and using multiple GPUs.
pyspark-etl-best-practices-cursorrules-prompt-file
Cursor rules for PySpark ETL development with code style, joins, window functions, map operations, and Iceberg patterns.
project-epic-template-cursorrules-prompt-file
Cursor rules for project development with epic template integration.