{"categories":[{"label":"Markup","url":"https://skillfed.io/packages/category/text-processing-markup/2"}],"enrichment":{"capability":"POML is a markup language and Python SDK for structuring, templating, and styling prompts for large language models, with support for embedding external data and managing presentation variations.","skillfed_tags":["llm-prompting","template-engine","markup-language"],"use_cases":["Build reusable prompt templates with variables and conditionals to generate different prompts from a single POML file.","Embed images, CSV data, or text files directly into prompts and control their formatting via styling rules.","Separate prompt logic from presentation so you can test different verbosity or syntax styles without rewriting core prompts.","Organize multi-step reasoning prompts (role, context, task, output format) in a structured, maintainable markup file.","Integrate POML rendering into a Python application to dynamically generate and send prompts to an LLM API."],"what_it_does":"POML is a markup language designed to bring structure and reusability to prompt engineering for LLMs. It uses an HTML-like syntax with semantic components (role, task, output-format, etc.) to organize prompt logic, embed external data (text, spreadsheets, images), and apply CSS-like styling to control presentation without changing core content. The Python SDK integrates POML into your application workflow, rendering templates with variables, loops, and conditionals to generate data-driven prompts dynamically.\n\nThe package targets developers who write complex, multi-part prompts and want to separate content from formatting, reduce duplication, and manage prompt variations systematically. It requires Python 3.9 or later and depends on pydantic for validation and nodejs-wheel for the underlying rendering engine. The project is maintained by Microsoft but is still in early releases (0.0.8), so the API and feature set may evolve.","worth_installing":"Yes, if you regularly write complex, multi-part prompts and want a structured, reusable approach to prompt engineering. The MIT license is permissive, and the package has no known vulnerabilities. However, be aware that it is in early releases (0.0.8) with aging maintenance (354 days since last release), so expect the API to evolve and support to be slower. The nodejs-wheel dependency adds medium install friction. Start with a small pilot to verify it fits your workflow before committing to it in production."},"id":"poml","links":{"html":"https://skillfed.io/packages/poml","md":"https://skillfed.io/packages/poml.md","pypi":"https://pypi.org/project/poml/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-08-25","license_spdx":null,"license_treatment":"unclear","name":"poml","python_support":"supports_current","summary":"Prompt Orchestration Markup Language"},"popularity":{"monthly_downloads":74492,"position":14824,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.8"}