infrahub-managing-checks

name	infrahub-managing-checks
description	Creates Infrahub check definitions — Python validation logic and GraphQL queries for proposed change pipelines. TRIGGER when: writing validation checks, creating Python checks, building data quality guards for proposed changes. DO NOT TRIGGER when: designing schemas, querying live data, building transforms or generators.
paths	["checks/**/*.py","queries/**/*.gql"]
allowed-tools	["Read","Write","Edit","Bash","Grep"]
argument-hint	[check-name] [description...]
metadata	{"version":"1.1.0","author":"OpsMill"}

Infrahub Check Creator

Overview

Expert guidance for creating Infrahub checks. Checks are user-defined validation logic (Python + GraphQL) that run as part of a proposed change pipeline. If a check logs any errors, the proposed change cannot be merged.

Project Context

Infrahub config: !cat .infrahub.yml 2>/dev/null || echo "No .infrahub.yml found"

Existing checks: !find . -name "*.py" -path "*/checks/*" 2>/dev/null | head -20

Existing queries: !find . -name "*.gql" -path "*/queries/*" 2>/dev/null | head -20

When to Use

• Writing validation logic for proposed changes
• Creating data quality guards (e.g., rack collision detection)
• Building global checks that validate all objects of a type
• Building targeted checks that validate specific grouped objects
• Debugging check failures or understanding the check lifecycle

Rule Categories

Priority	Category	Prefix	Description
CRITICAL	Architecture	architecture-	Three components, global vs targeted, execution flow
CRITICAL	Python Class	python-	InfrahubCheck base class, validate(), log_error/log_info
HIGH	API Reference	api-	Class attributes, instance properties, methods, lifecycle
HIGH	Registration	registration-	.infrahub.yml config, query name matching, parameters
MEDIUM	Patterns	patterns-	Error collection, shared utilities, scoped validation
LOW	Testing	testing-	infrahubctl check commands, branch testing

Check Basics

Every check has three components:

1. GraphQL query (.gql file) -- fetches the data to validate
2. Python class -- inherits from InfrahubCheck, implements validate()
3. Configuration -- declared in .infrahub.yml under check_definitions

from infrahub_sdk.checks import InfrahubCheck


class MyCheck(InfrahubCheck):
    query = "my_query"  # Must match query name

    def validate(self, data: dict) -> None:
        # Validation logic here
        if something_is_wrong:
            self.log_error(
                message="Problem description"
            )

Workflow

Follow these steps when creating a check:

1. Understand the validation goal — What data condition should block a proposed change? Determine whether this is a global check (all objects of a type) or targeted (specific group). Read rules/architecture-types.md.
2. Write the GraphQL query — Create a .gql file that fetches the data to validate. Read ../infrahub-common/graphql-queries.md for query patterns.
3. Implement the Python class — Inherit from InfrahubCheck, implement validate(). Read rules/python-validate.md for the class pattern and rules/api-reference.md for available methods.
4. Register in .infrahub.yml — Add the check under check_definitions. The query name must match the Python class query attribute. See rules/registration-config.md.
5. Test — Run infrahubctl check to validate. See rules/testing-commands.md.

Supporting References

• examples.md -- Complete check patterns (global, targeted, minimal)
• ../infrahub-common/graphql-queries.md -- GraphQL query writing reference
• ../infrahub-common/infrahub-yml-reference.md -- .infrahub.yml project configuration
• ../infrahub-common/rules/ -- Shared rules (git integration, caching gotchas) that apply across all skills
• ../infrahub-managing-schemas/SKILL.md -- Schema definitions checks validate against
• rules/ -- Individual rules organized by category prefix