bmad-generate-project-context

Generating project context involves scanning your repository's architecture files, package manifests, and configuration to produce a lean markdown artifact. This file captures critical implementation rules and repository conventions for AI agents to follow during code generation.

An AI-ready project context file is a lean markdown document that captures your technology stack, coding patterns, workflow rules, and implementation constraints. You need it to ensure all AI coding agents follow the same standards, reducing inconsistency and preventing common implementation mistakes in your repository.

Yes, you can capture repository conventions and coding patterns through a collaborative rule capture process. This walks you through technology, framework, testing, quality, and workflow rules with section-by-section approval checkpoints to ensure the context is accurate and LLM-optimized.

Generating project context does not strictly require existing context files but benefits from them. The process detects existing context documents, reviews architecture files, package manifests, and configuration files to collaboratively discover and validate the repository's non-obvious implementation constraints.

The best way to standardize AI agent rules across an existing codebase is to generate a concise project-context.md file with structured frontmatter. This artifact captures your workflow standards and anti-patterns, ensuring all AI coding agents read and follow the same implementation guidelines before contributing.

Manually writing project context for AI agents often misses non-obvious implementation constraints and fails to apply structured frontmatter optimized for LLMs. A guided discovery process validates section-by-section approvals by scanning actual configuration files, ensuring the context remains lean and accurate for AI-assisted coding.