// Add predefined field definitions to the Prompture field registry. Handles field structure, categories, template variables, enum support, and thread-safe registration. Use when adding reusable extraction fields.
| name | add-field |
| description | Add predefined field definitions to the Prompture field registry. Handles field structure, categories, template variables, enum support, and thread-safe registration. Use when adding reusable extraction fields. |
| metadata | {"author":"prompture","version":"1.0"} |
Adds new fields to the global BASE_FIELD_DEFINITIONS registry in prompture/field_definitions.py.
Ask the user for each field:
linkedin_url, blood_type)str, int, float, bool, list, dict"" for str, 0 for int, 0.0 for float, [] for list, False for boolTrue if the field can legitimately be absent"field_name": {
"type": str,
"description": "What this field represents.",
"instructions": "How the LLM should extract or compute this value.",
"default": "",
"nullable": False,
},
"enum": ["low", "medium", "high"]instructions: {{current_year}}, {{current_date}}, {{current_datetime}}, {{current_month}}, {{current_day}}, {{current_weekday}}, {{current_iso_week}}prompture/field_definitions.pyAdd fields to BASE_FIELD_DEFINITIONS under the right category comment. If the category is new, add a comment header:
# ── Medical Fields ──────────────────────────────────
Alphabetical order within each category.
python -c "from prompture.field_definitions import get_field_definition; print(get_field_definition('field_name'))"
pytest tests/test_field_definitions.py -x -q
Update LLM model pricing in Prompture. Pricing resolves through a pluggable source registry — by default local KB JSON files (primary, curated) then models.dev (fallback). Use when model prices change, new models launch, models.dev data is stale, or you need to add a custom pricing source.
Scaffold a new LLM provider driver for Prompture. Creates sync + async driver classes, registers them in the driver registry, adds settings, env template, setup.py extras, package exports, discovery integration, and models.dev pricing. Use when adding support for a new LLM provider.
Create a new Prompture usage example script. Follows project conventions for file naming, section structure, docstrings, and output formatting. Use when demonstrating extraction use cases or provider integrations.
Create reusable persona system prompts for Prompture. Covers Persona dataclass, template variables, composition, trait registry, global registry, serialization, and Conversation integration. Use when defining system prompts for extraction or agent behavior.
Add function-calling tools to Prompture agents. Covers ToolDefinition, ToolRegistry, tool_from_function, decorator patterns, serialization formats, and Conversation integration. Use when adding callable tools for LLM function calling.
Add unit and integration tests for Prompture functionality. Uses pytest conventions, shared fixtures from conftest.py, and the integration marker pattern. Use when writing tests for new or existing features.