swarm-forge-skill

Scaffolding AI workflow context files like CLAUDE.md and AGENTS.md involves inspecting the repository state and generating targeted knowledge files for CLIs like Claude Code, Codex, OpenCode, and Gemini CLI. This guided process replaces ad-hoc setup with safe, deterministic context merging.

Configuring a multi-CLI AI workflow in a new repository is best done through guided scaffolding that creates entries like CLAUDE.md, AGENTS.md, and GEMINI.md while applying development patterns like TDD, BDD, ATDD, or SDD to structure agent interactions.

Yes, you can use this scaffolding approach with Gemini CLI and Codex without losing existing configuration. It performs deterministic context merging to ensure minimal prompts and safe merges, preserving your current repository context while adding new workflow artifacts.

Deterministic context merging works by safely combining new AI workflow context with existing repository files, preventing overwrites during scaffolding. It ensures that refreshing workflow summaries as agents or folders change results in minimal prompts and consistent knowledge file updates.

Yes, guided context scaffolding supports four development patterns: TDD, BDD, ATDD, and SDD. These patterns are integrated into the workflow context setup to align AI agent behavior with your preferred software engineering methodology.

You should avoid ad-hoc scaffolding for repository AI context setup when managing multiple CLIs, as it risks overwriting existing knowledge files. Using guided, safe scaffolding instead ensures deterministic context merging and protects your current workflow configuration.