AI Coding Assistant Context (CLAUDE.md & .cursorrules)

Static analysis catches issues *after* code is written. But what if you could guide AI models never to generate these mistakes in the first place?

AI Guard bridges this gap with ai-guard init-context. It automatically analyzes your configured rules and synthesizes clear, unambiguous instruction sets tailored to modern AI coding assistants.

Why Prompt Guardrails Matter

LLMs operate probabilistically. When prompted to generate a route handler or an async pipeline, they rely on frequent internet snippets—which often cut corners (e.g., omitting await inside array callbacks, or skipping error rethrows).

When you provide concise system instructions in standard context files, AI coding assistants adhere to those architectural guardrails with significantly higher fidelity.

Generating Context Files

Run a single command in your project root:

npx ai-guard init-context

This command detects your environment and automatically writes or updates: - CLAUDE.md — Read automatically by Claude Code CLI and Anthropic integrations. - .cursorrules — Read by Cursor IDE for editor completions and agent mode. - .github/copilot-instructions.md — Read by GitHub Copilot Chat and PR summaries.

Claude Code (CLAUDE.md)

Claude Code checks for CLAUDE.md at the start of every session. The generated section includes strict rules such as:

# AI Guard Safety Directives
- ALWAYS await or attach .catch() to promises; never leave floating promises.
- NEVER pass async callbacks to Array.prototype.map() or forEach() expecting sequential execution; use Promise.all() or for...of.
- NEVER commit raw credentials, tokens, or private keys; always reference process.env.
- NEVER write empty catch blocks (catch (e) {}); always log or handle the error.

Cursor IDE (.cursorrules)

The generated .cursorrules instructs Cursor's underlying composer and autocomplete models to reject non-deterministic patterns before proposing edits.

Shifting Left: Prevention + Verification

The most resilient developer workflow combines both: 1. At Generation Time: init-context keeps AI assistants on the right path. 2. At CI / Pre-Commit: ESLint & AI Guard CLI deterministically verify that zero violations slipped through.