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Roles, Tone, and Constraints into the `system` parameter. Additionally, converts `schema` definitions into forced **Tool Use** calls, extracting structured results to match OpenAI's output format.\n    - **OpenAI**: Maps generic roles to the appropriate `system` or `developer` (for O-series) messages.\n    - **Gemini**: Structurally adapts components into System Instructions and content parts. For structured outputs, it automatically transforms JSON schemas into Gemini's `responseSchema` format, ensuring strict adherence to the defined structure.\n- **CLI Tool**: Scaffold, manage, process, and **execute** prompts directly from the terminal.\n- **Model Agnostic**: Format prompts for different models (GPT-4, Claude, Gemini, etc.) automatically.\n- **Execution Engine**: Run prompts against OpenAI, Anthropic, or Gemini APIs directly.\n- **Portable**: Serialize prompts to JSON or XML for easy exchange between systems.\n- **Type-Safe**: Full TypeScript support with excellent IntelliSense.\n\n## Installation\n\n```bash\nnpm install @riotprompt/riotprompt\n```\n\n## MCP Server\n\nRiotPrompt includes a Model Context Protocol (MCP) server that allows AI assistants to create, process, and execute prompts. See [MCP.md](MCP.md) for configuration and usage details.\n\n```json\n{\n  \"mcpServers\": {\n    \"riotprompt\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@riotprompt/riotprompt\", \"riotprompt-mcp\"]\n    }\n  }\n}\n```\n\n## CLI Usage\n\nRiotPrompt comes with a command-line interface to help you organize, process, and execute prompts.\n\n### 1. Create a Prompt\n\nScaffold a new prompt directory structure:\n\n```bash\n# Create a new prompt in 'my-prompt' directory\nnpx riotprompt create my-prompt --persona \"You are a data expert.\"\n\n# Import an existing prompt from JSON or XML\nnpx riotprompt create my-prompt --import existing-prompt.json\n```\n\nThis creates a structured directory:\n```\nmy-prompt/\n├── persona.md          # System prompt / Persona definition\n├── instructions.md     # Main task instructions\n└── context/            # Directory for reference files (data.json, docs.md)\n```\n\n### 2. Process a Prompt\n\nCompile a prompt directory (or file) into a formatted payload for an LLM, or export it to other formats.\n\n```bash\n# Format for GPT-4 (output to console)\nnpx riotprompt process my-prompt -m gpt-4\n\n# Export to JSON (useful for API integrations)\nnpx riotprompt process my-prompt --format json --output prompt.json\n\n# Export to XML\nnpx riotprompt process my-prompt --format xml --output prompt.xml\n```\n\n### 3. Execute a Prompt\n\nRun the prompt directly against an LLM provider.\n\n#### Provider Setup & API Keys\n\nRiotPrompt supports multiple LLM providers. You'll need to obtain an API key for the provider you wish to use and set it as an environment variable (recommended) or pass it via the `-k` flag.\n\n##### Google Gemini\n1.  **Get API Key**: Visit [Google AI Studio](https://aistudio.google.com/), sign in, and click \"Get API key\".\n2.  **Set Environment Variable**: `GEMINI_API_KEY`\n3.  **Available Models**: Check the [Gemini models documentation](https://ai.google.dev/models/gemini). Common models include `gemini-1.5-pro`, `gemini-1.5-flash`.\n\n##### OpenAI\n1.  **Get API Key**: Go to the [OpenAI Platform](https://platform.openai.com/api-keys), sign up/login, and create a new secret key.\n2.  **Set Environment Variable**: `OPENAI_API_KEY`\n3.  **Available Models**: See [OpenAI Models](https://platform.openai.com/docs/models). Common models: `gpt-4o`, `gpt-4-turbo`, `gpt-3.5-turbo`.\n\n##### Anthropic (Claude)\n1.  **Get API Key**: Access the [Anthropic Console](https://console.anthropic.com/settings/keys) and generate an API key.\n2.  **Set Environment Variable**: `ANTHROPIC_API_KEY`\n3.  **Available Models**: View [Claude Models](https://docs.anthropic.com/en/docs/models-overview). Common models: `claude-3-opus-20240229`, `claude-3-sonnet-20240229`.\n\n**Example .env file:**\n```bash\nOPENAI_API_KEY=sk-...\nANTHROPIC_API_KEY=sk-ant-...\nGEMINI_API_KEY=AIza...\n```\n\n**Commands**:\n\n```bash\n# Run with default model (usually gpt-4)\nnpx riotprompt execute my-prompt\n\n# Run with specific model\nnpx riotprompt execute my-prompt -m claude-3-opus\n\n# Run with explicit API key (overrides env)\nnpx riotprompt execute my-prompt -m gpt-4 -k sk-proj-...\n\n# Control parameters\nnpx riotprompt execute my-prompt -t 0.7 --max-tokens 1000\n```\n\n### Configuration\n\nYou can configure defaults using a `riotprompt.yaml` file in your project root:\n\n```yaml\ndefaultModel: \"gpt-4\"\npromptsDir: \"./prompts\"\noutputDir: \"./output\"\n```\n\n## Library Usage\n\nYou can also use RiotPrompt programmatically in your application.\n\n```typescript\nimport { cook, registerTemplates } from 'riotprompt';\n\n// Advanced prompt creation\nimport { z } from \"zod\";\n\nconst prompt = await cook({\n  basePath: __dirname,\n  persona: { content: 'You are a helpful AI assistant' },\n  // ...\n  // Structured Output with Zod\n  schema: z.object({\n      summary: z.string(),\n      tags: z.array(z.string()),\n      confidence: z.number().min(0).max(1)\n  })\n});\n\n// Register and use templates\nregisterTemplates({\n  'analysis': {\n    persona: { content: 'You are an expert analyst' },\n    instructions: [{ content: 'Provide detailed analysis' }],\n  },\n});\n\nconst analysisPrompt = await cook({\n  basePath: __dirname,\n  template: 'analysis',\n  content: [{ content: dataToAnalyze, title: 'Data' }],\n});\n```\n\n## Documentation\n\nFull documentation is available at [https://kjerneverk.github.io/riotprompt/](https://kjerneverk.github.io/riotprompt/).\n\nYou can also explore the guides in the source:\n- [Core Concepts](docs/public/core-concepts.md)\n- [Recipes System](docs/public/recipes.md)\n- [API Reference](docs/public/api-reference.md)\n- [Template Configuration](docs/public/template-configuration.md)\n\n## Philosophy\n\nRiotPrompt is designed to be completely generic and unopinionated. Unlike other prompt libraries that assume specific use cases, RiotPrompt provides the building blocks for any prompt-based application while maintaining type safety and developer experience.\n\n## Architecture\n\n- **Cook Function**: Core prompt creation engine\n- **Template System**: Reusable configuration patterns\n- **Content Processing**: Flexible content handling (files, directories, inline)\n- **Override System**: Hierarchical customization\n- **Type Safety**: Full TypeScript support throughout\n\n## Contributing\n\nContributions are welcome! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n## License\n\nApache-2.0 License - see [LICENSE](LICENSE) for details.\n\n---\n\n*Build better prompts, faster.*\n\n<!-- v1.0.0 -->\n","readmeFilename":"README.md"}