Borrowing it
Nothing to install: this file belongs to altaidevorg/rules-for-ai. 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/altaidevorg/rules-for-ai/main/.cursorrulesgit clone --depth 1 https://github.com/altaidevorg/rules-for-aiWrote 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/altaidevorg/rules-for-ai/cursorrules)<a href="https://agentmods.dev/rules/altaidevorg/rules-for-ai/cursorrules"><img src="https://agentmods.dev/badge/rules/altaidevorg/rules-for-ai/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/altaidevorg/rules-for-ai/cursorrules"><img src="https://agentmods.dev/badge/rules/altaidevorg/rules-for-ai/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 | $0.00000 | $0.13624 |
| Opus 5.5 | $0.00000 | $0.05450 |
| Sonnet 5.5 | $0.00000 | $0.02725 |
| Haiku 4.5 | $0.00000 | $0.01362 |
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 2mo 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.
This is a copy
100% identical to cursorrules — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,664 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Coding: Humans Design, Agents code!
If you are an AI agents involved in building LLM Systems, read this guide VERY, VERY carefully! This is the most important chapter in the entire document. Throughout development, you should always (1) start with a small and simple solution, (2) design at a high level (
docs/design.md) before implementation, and (3) frequently ask humans for feedback and clarification. {: .warning }
Agentic Coding Steps
Agentic Coding should be a collaboration between Human System Design and Agent Implementation:
| Steps | Human | AI | Comment |
|---|---|---|---|
| 1. Requirements | ★★★ High | ★☆☆ Low | Humans understand the requirements and context. |
| 2. Flow | ★★☆ Medium | ★★☆ Medium | Humans specify the high-level design, and the AI fills in the details. |
| 3. Utilities | ★★☆ Medium | ★★☆ Medium | Humans provide available external APIs and integrations, and the AI helps with implementation. |
| 4. Node | ★☆☆ Low | ★★★ High | The AI helps design the node types and data handling based on the flow. |
| 5. Implementation | ★☆☆ Low | ★★★ High | The AI implements the flow based on the design. |
| 6. Optimization | ★★☆ Medium | ★★☆ Medium | Humans evaluate the results, and the AI helps optimize. |
| 7. Reliability | ★☆☆ Low | ★★★ High | The AI writes test cases and addresses corner cases. |
- Requirements: Clarify the requirements for your project, and evaluate whether an AI system is a good fit.
- Understand AI systems' strengths and limitations:
- Good for: Routine tasks requiring common sense (filling forms, replying to emails)
- Good for: Creative tasks with well-defined inputs (building slides, writing SQL)
- Not good for: Ambiguous problems requiring complex decision-making (business strategy, startup planning)
- Keep It User-Centric: Explain the "problem" from the user's perspective rather than just listing features.
- Balance complexity vs. impact: Aim to deliver the highest value features with minimal complexity early.
- Understand AI systems' strengths and limitations:
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.
- 2mo ago First seen · 1,664 lines · 0 tokens per session scan A f2b18dceffa3
cursorrules is a cursor rule published in the GitHub repository altaidevorg/rules-for-ai (2 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 13,624 tokens. A static security scan graded it A with 0 findings. It is 100% identical to cursorrules, differing in 0 lines, and is treated as a copy.
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-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).
control-plane-descriptors
Control plane descriptor and instance implementation patterns.