schema.linter

Purpose

Validate configuration files, JSON schemas, and structured data files against defined rules to catch errors before runtime. This is a shared skill applicable across all Carbon ACX file types.

When to Use

Trigger Patterns:

"Validate all config files"
"Check schemas for errors"
"Lint JSON/YAML files"
"Verify wrangler.toml is valid"
Pre-commit hook integration

Do NOT Use When:

Validating Python/TypeScript code (use language-specific linters)
Checking CSV data integrity (use data-specific validation)

Allowed Tools

read_file - Read config files and schemas
python - Parse and validate YAML/JSON/TOML
bash - Run external validators (yamllint, jsonschema)

Access Level: 1 (Local Execution - read-only)

Expected I/O

Input:

File paths or glob patterns
Example: "schemas/**/*.json", "wrangler.toml", "config/*.yaml"

Output:

Validation report with errors/warnings
File:line references for each issue
Suggested fixes when possible

Dependencies

Required:

Python 3.11+ with PyYAML, jsonschema, toml libraries
Optional: yamllint, prettier (JSON)

Configuration:

config.json - Linting rules and schema paths

Examples

Example: Validate JSON Schema

User: "Use schema.linter to check all config files"

Output:

✅ config/layers.json - Valid
✅ config/sectors.json - Valid
❌ config/profiles.json - 2 errors:
   Line 15: Missing required field 'profile_id'
   Line 23: Invalid type for 'vintage' (expected integer, got string)

⚠️  schemas/figure-manifest.schema.json - 1 warning:
   Line 45: Description field is empty

Summary: 2 files valid, 1 file with errors, 1 file with warnings

Limitations

Cannot fix files automatically (reports only)
Limited to YAML/JSON/TOML formats
Custom schema definitions required for validation

Validation Criteria

✅ All files parse without syntax errors
✅ Required fields present
✅ Types match schema definitions
✅ No duplicate keys

Maintenance

Owner: Platform Team Review Cycle: Quarterly Last Updated: 2025-10-18 Version: 1.0.0