Borrowing it
Nothing to install: this file belongs to HendrikReh/llm-guard. 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/HendrikReh/llm-guard/main/AGENTS.mdgit clone --depth 1 https://github.com/HendrikReh/llm-guardWrote 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/instructions/hendrikreh/llm-guard/agents-md)<a href="https://agentmods.dev/instructions/hendrikreh/llm-guard/agents-md"><img src="https://agentmods.dev/badge/instructions/hendrikreh/llm-guard/agents-md.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 5.1 | $0.03941 | $0.03941 |
| Opus 5 | $0.01971 | $0.01971 |
| Sonnet 5 | $0.00788 | $0.00788 |
| Haiku 4.5 | $0.00394 | $0.00394 |
Grade B, and why
llm-guard AGENTS.md scanned grade B with 1 finding 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 8d 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- [ ] Code compiles without warnings How it starts
The opening of the file, as written. The whole thing — 590 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Assistant Onboarding Guide
Welcome to the AI Coding Hackathon!
This document provides essential context, conventions, and collaboration guidelines for AI coding assistants participating in hackathon projects. Follow these practices to contribute effectively and maintain code quality during rapid development.
1. Hackathon Context
1.1 Event Overview
You are assisting in the AI Coding Accelerator Hackathon organized by Maven.
Instructors: Vignesh Mohankumar and Jason Liu
Key Characteristics:
- Fast-paced development (typically 1-day sprints)
- Focus on working prototypes over perfect architecture
- Balance between speed and code quality
- Emphasis on demonstrable results
1.2 Current Project: LLM-Guard
Project Goal: Build a fast, explainable prompt injection detection tool in Rust
Core Objectives:
- Scan prompts for injection/jailbreak indicators
- Provide transparent risk scoring (0-100)
- Support multiple output formats (CLI, JSON)
- Optional LLM-powered analysis
Key Documents:
PRD.md- Product Requirements DocumentPLAN.md- Implementation roadmap and progress trackingREADME.md- User-facing documentation
2. Collaboration Philosophy
2.1 Speed vs Quality Balance
DO:
- ✅ Implement features incrementally
- ✅ Write tests for core logic
- ✅ Use simple, explicit designs
- ✅ Prioritize working code over perfect abstractions
- ✅ Document non-obvious decisions in comments
DON'T:
- ❌ Over-engineer solutions
- ❌ Add unnecessary abstractions prematurely
- ❌ Skip error handling entirely
- ❌ Ignore the PRD requirements
- ❌ Create features not explicitly requested
2.2 Communication Style
- Be concise: Explain what you're doing and why, briefly
- Ask when uncertain: Clarify requirements before implementing
- Show progress: Use comments to indicate WIP sections
- Acknowledge constraints: Call out hackathon trade-offs explicitly
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.
- 8d ago First seen · 590 lines · 3,941 tokens per session scan B 6767ef0d37ce
llm-guard AGENTS.md is an instructions file published in the GitHub repository HendrikReh/llm-guard (3 stars, last pushed 10mo ago), licensed MIT. It adds 3,941 tokens to every session, about $0.0197 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.