15. Custom Slash Commands

Slash commands provide a conversational interface to AI capabilities, executing predefined workflows with minimal typing. Unlike free-form chat, slash commands encode institutional knowledgeΓÇöreview checklists, documentation standards, testing requirementsΓÇöinto repeatable processes that don't require explaining context each time.

The command framework interprets text input, extracts command identifiers and arguments, and routes execution to appropriate handlers. A command like /review pr:123 triggers the PR review workflow, while /explain-error opens an interactive debugging session. The framework handles common concernsΓÇöauthentication, context injection, output formattingΓÇöwhile each command implements domain-specific logic.

Command design requires understanding what operations benefit from AI assistance versus what benefits from standardization. Code review, documentation generation, and complex refactoring tasks suit AI well. Formatting enforcement, copyright header insertion, and import organization suit standardization. Some operations combine bothΓÇöAI generates content while standardization enforces format requirements.

Argument parsing enables flexible command usage. Commands should accept multiple argument styles: positional arguments for quick invocation, named flags for explicit options, and interactive prompts when arguments are missing. /test-generate UserService might infer the target module, while /test-generate --file services/user.py --framework pytest provides explicit control.

Context injection provides the AI with relevant information. Commands often need access to current file, open PR details, selected code, or repository state. The framework collects this context and makes it available to command handlers. A well-designed context system means command implementations don't need to handle retrievalΓÇöcontext arrives pre-packaged.

Output formatting varies by command type. Some commands produce text suitable for direct insertionΓÇödocumentation, test templates, commit messages. Others produce structured data consumed by other toolsΓÇöJSON for CI integration, markdown for pull request comments. The framework should support multiple output formats with appropriate rendering.

Command discovery matters for adoption. Developers won't use commands they don't know exist. Inline help via /help or /help command-name provides documentation. Command palettes in IDE integrations surface available commands contextually. Documentation should include realistic examples showing command usage.

Version compatibility requires attention as AI models evolve. Commands that prompt the model explicitly might produce different quality across model versions. Locking to specific prompt versions or implementing output validation prevents regressions when underlying models change.