{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Markup","url":"https://skillfed.io/packages/category/text-processing-markup"}],"enrichment":{"capability":"Renders Handlebars templates for composing LLM prompts, with compile-time validation against Pydantic models to catch typos and missing fields before rendering.","skillfed_tags":["llm-prompts","pydantic-integration","type-safe-templates"],"use_cases":["Build system prompts for AI agents from structured data (guidelines, examples, context) stored in a database or config without hardcoding.","Compose few-shot examples dynamically by iterating over lists of Turn objects or similar structured records in a template.","Validate user-provided prompt templates against a schema before rendering to catch errors early.","Generate personalized LLM prompts by rendering templates with customer-specific or session-specific Pydantic model instances.","Catch template typos and missing fields at compile time rather than discovering them during prompt rendering at runtime."],"what_it_does":"pydantic-handlebars is a Handlebars template engine designed specifically for building LLM system prompts from dynamic, structured data. It bridges templating and type safety by validating templates against Pydantic models at compile time, catching field name typos and schema mismatches before any data is rendered. This prevents a common class of prompt-building bugs where a template references a field that doesn't exist in the data model.\n\nThe package supports standard Handlebars syntax\u2014variable interpolation, dot-notation paths, block helpers (each, if, unless), and conditionals\u2014but validates all field references against a Pydantic schema upfront. You compile a template with a model type, then render it with instances of that type. It also offers a lower-level schema-checking API for user-provided templates and a TypedCompiler for caching when compiling multiple templates against the same type.","worth_installing":"Yes, if you build LLM prompts from structured data and want compile-time validation. The single dependency (Pydantic), low install friction, and active maintenance make it a safe choice. Alpha status is appropriate for a focused, single-purpose tool. No known vulnerabilities. MIT license poses no restrictions. Install if type-safe, data-driven prompt composition is a workflow you need; skip if you hand-write prompts or use a different templating approach."},"id":"pydantic-handlebars","links":{"html":"https://skillfed.io/packages/pydantic-handlebars","md":"https://skillfed.io/packages/pydantic-handlebars.md","pypi":"https://pypi.org/project/pydantic-handlebars/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-25","license_spdx":"MIT","license_treatment":"permissive","name":"pydantic-handlebars","python_support":"supports_current","summary":"Handlebars template engine for composing LLM prompts, built on Pydantic"},"popularity":{"monthly_downloads":2142914,"position":3256,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.1"}