ESLint-like Tool for Composing AI Agent Rules

Here is ESLint-like tool for composing AI agent rules — here’s why

Łukasz Holc has developed ai-rulesmith to solve the fragmentation of AI agent context files. The tool manages disparate formats like CLAUDE.md and .cursorrules from a single JSON configuration.

Why This Matters

Developers currently face manual overhead synchronizing rules across multiple AI agents, leading to inconsistent behaviors and outdated instructions. By applying the ESLint model to AI prompts, engineers can enforce project standards programmatically rather than relying on redundant, error-prone markdown files.

Key Insights

• Priority Zones: LLMs pay most attention to the beginning and end of context windows, which ai-rulesmith leverages via ‘before_start’ and ‘before_finish’ sections.
• Multi-step Workflows: The tool enables sequential rule files for distinct phases such as Create, Review, and Ship to guide agent progression.
• Built-in Library: The system includes 29 rules across 9 categories distilled from sources like awesome-cursorrules and Addy Osmani’s spec writing guide (2026).
• Automated Testing: The ‘rulesmith test’ feature uses an LLM to simulate prompts and a judge model to evaluate if rules successfully influence agent behavior.

Working Examples

Initialization and building process for ai-rulesmith.

npm install -g ai-rulesmith
rulesmith init
rulesmith build

Practical Applications

• Use Case: Standardizing ‘code-style/strict-typescript’ across both Cursor and Claude Code to ensure consistent type safety.
• Pitfall: Maintaining separate .cursorrules and CLAUDE.md files manually, which leads to synchronization drift and forgotten updates.

References:

• https://github.com/Luzgan/ai-rulesmith
• https://dev.to/luzgan/i-built-an-eslint-like-tool-for-composing-ai-agent-rules-heres-why-gfd