Standardize and validate LlamaFarm configurations using Pydantic v2 and JSONSchema.
npx skills add https://github.com/llama-farm/llamafarm --skill config-skills
Please help me install this Agent Skill. Skill: config-skills Source: https://github.com/llama-farm/llamafarm/tree/main/.claude/skills/config-skills Command: npx skills add https://github.com/llama-farm/llamafarm --skill config-skills
This Skill standardizes configuration patterns across LlamaFarm projects, reducing misconfigurations and drift.
Edit schema.yaml to adjust models, then run nx run generate-types to regenerate datamodel.py, and use uv run python config/validate_config.py path/to/llamafarm.yaml to validate.
High-intent search queries and answers about installing and using this skill.
You can validate YAML configuration using Pydantic v2 by defining datamodels in datamodel.py and running a validation script against schema.yaml. This ensures your config files conform to expected types and structures before deployment.
The best way to standardize configuration patterns is using a pattern-driven approach with shared templates for schema generation and YAML processing. This reduces misconfigurations and drift across projects by enforcing consistent validation rules.
You can generate JSONSchema from Pydantic models by editing schema.yaml to define your models and then running a code generation command. This regenerates datamodel.py to ensure your schema definitions stay synchronized.
No external installations are required beyond standard repository tooling. The validation process relies on existing files such as schema.yaml, datamodel.py, and validators.py already present in the repository to ensure consistency.
Yes, pattern-driven config validation using JSONSchema and Pydantic v2 is applicable to module configurations, deployment templates, and runtime settings. It helps teams standardize all config types for safer deployments and rapid onboarding.
Your datamodel.py may be out of sync because the code generation step was not run after editing schema.yaml. You must run the generate-types command to regenerate datamodel.py and ensure consistency between schema and models.
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