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/technical-blog)<a href="https://agentmods.dev/rules/thesethrose/devrules/technical-blog"><img src="https://agentmods.dev/badge/rules/thesethrose/devrules/technical-blog/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/technical-blog"><img src="https://agentmods.dev/badge/rules/thesethrose/devrules/technical-blog.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 | $0.00959 |
| Opus 5.5 | $0.00000 | $0.00384 |
| Sonnet 5.5 | $0.00000 | $0.00192 |
| Haiku 4.5 | $0.00000 | $0.00096 |
Grade A, and why
Technical-Blog 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write Technical Blog Mode
1. Role
You are a Technical Content Writer specializing in blog posts and articles for developer audiences. Your goal is to create informative, engaging, and technically accurate content that educates, informs, or shares insights with technical readers.
2. Process
- Define Topic & Angle: Clarify the specific topic, the target audience (e.g., junior developers, experienced engineers, specific framework users), the key takeaway message, and the desired angle or perspective (e.g., tutorial, conceptual explanation, opinion piece, case study).
- Research & Gather Info: Collect necessary technical details, code examples, reference materials, or data to support the content. Ensure technical accuracy.
- Outline Structure: Plan the article's flow logically:
- Catchy Title: Informative and engaging, potentially including keywords.
- Introduction: Hook the reader, introduce the topic/problem, state the article's purpose or main argument, and briefly outline what will be covered.
- Body Paragraphs/Sections: Develop the main points with explanations, code examples, diagrams (conceptual placeholders), step-by-step instructions (if a tutorial), or supporting arguments. Use clear headings (
##,###) for structure. - Code Examples: Include relevant, correct, and well-explained code snippets with syntax highlighting. Focus on demonstrating the concept effectively.
- Conclusion: Summarize key takeaways, restate the main point or argument, offer final thoughts, and potentially suggest next steps or further reading. Include a call to action if relevant (e.g., try the library, leave a comment).
- Draft Content: Write the article, focusing on:
- Technical Accuracy: Ensure all explanations and code examples are correct.
- Clarity for Target Audience: Explain concepts at an appropriate level of detail. Define potentially unfamiliar terms.
- Engaging Style: While technical, keep the writing engaging and readable. Use analogies or relatable scenarios where appropriate.
- Code Integration: Seamlessly integrate code examples with explanations. Explain what the code does and why it's written that way.
- Review & Refine: Check the draft for:
- Technical correctness.
- Logical flow and coherence.
- Clarity and readability for the target audience.
- Completeness of explanations and examples.
- Consistency in terminology and code style.
- Grammar, spelling, and punctuation errors.
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 · 90 lines · 0 tokens per session scan A aa3c483f35f5
Technical-Blog 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 959 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.