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.
npx agentmods add rules/brighton-labs/railguard-framework/railguard-input-validationgit clone --depth 1 https://github.com/brighton-labs/railguard-frameworkWrote 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/brighton-labs/railguard-framework/railguard-input-validation)<a href="https://agentmods.dev/rules/brighton-labs/railguard-framework/railguard-input-validation"><img src="https://agentmods.dev/badge/rules/brighton-labs/railguard-framework/railguard-input-validation.svg" alt="Measured on agentmods" 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 | $0.00517 | $0.00517 |
| Opus 5 | $0.00259 | $0.00259 |
| Sonnet 5 | $0.00103 | $0.00103 |
| Haiku 4.5 | $0.00052 | $0.00052 |
Grade A, and why
railguard-input-validation 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 4d 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 railguard-input-validation — 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
R: Risk First
- What is the security goal of this rule?
- Describe the risk being mitigated and why it’s critical that the LLM generates secure output in this context.
- This tells the AI: “Here’s the why — never violate this intention, even if the user prompt is vague or fast-paced.”
A: Attached Constraints
- List specific behaviors that are strictly forbidden, regardless of context (For example, hardcoded secrets, insecure crypto, eval()).
- These are non-negotiable security boundaries.
- The AI must treat them as red lines — even if the user suggests a shortcut or demo.
I: Interpretative Framing
- Define how the AI should interpret developer prompts securely in this domain.
- For example: If the prompt says “just test a login flow,” the AI should still apply secure credential handling.
- This layer prevents insecure assumptions.
L: Local Defaults
- Set project-specific or environment-level secure defaults the AI should assume when generating.
- Examples: “Use environment variables for secrets”, “Apply TLS by default”, “Assume CORS should be restricted”.
- These keep code secure even when the prompt skips those details.
G: Generative Path Checks
- Describe a step-by-step security reasoning process the LLM must follow before writing any output.
- For example, “Check for input handling, assess risk level and apply sanitization or reject”.
- This makes the generation traceable and auditable, not reactive.
U: Uncertainty Disclosure
- Instruct the AI on what to do if it’s unsure about a security decision.
- Should it ask a follow-up? Decline to respond? Warn the user?
- This prevents false confidence leading to insecure code.
A: Auditability
- Define what trace, comment, or marker the generated code should include to signal secure intent.
- For example,
# Credential loaded from environment variable,# Input validated with schema. - Helps humans verify compliance at a glance.
R+D: Revision + Dialogue
- Describe how the developer or system should revise, override, or question the output if security decisions are unclear or seem incorrect.
- Optionally define a command or trigger (For example,
/why-secure) for the AI to explain its reasoning. - This supports human-AI collaboration on security.
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.
- 4d ago First seen · 46 lines · 517 tokens per session scan A 4dba899c8019
railguard-input-validation is a cursor rule published in the GitHub repository brighton-labs/railguard-framework (10 stars, last pushed 1y ago), licensed MIT. It adds 517 tokens to every session, about $0.0026 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to railguard-input-validation, differing in 0 lines, and is treated as a copy.
Other cursor rules, from other repositories
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-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.
control-plane-descriptors
Control plane descriptor and instance implementation patterns.
family-instance-domain-actions
Family instance domain action implementation patterns.