Config Schema Migrator Skill

Config Schema Migrator Skill

When to Use This Skill

Activate this skill when you need to:

• Evolve Pydantic configuration schemas (add fields, change types, restructure sections)
• Maintain backward compatibility with existing config files
• Write migration scripts to automate config updates
• Implement environment variable substitution in config fields
• Add deprecation warnings for old config patterns
• Validate config schemas with field validators
• Create discriminated unions for config type discrimination

Key Principles

1. Backward Compatibility First: Never break existing configs without a migration path
2. Deprecation Before Removal: Warn users before removing old config sections
3. Automatic Migration: Provide scripts to automate config updates (don't force manual editing)
4. Type Safety: Use Pydantic validators to catch config errors at load time
5. Environment Variables: Support ${VAR_NAME} substitution for secrets
6. Clear Messaging: Provide helpful error messages with migration instructions

Pattern 1: Adding New Config Sections with Backward Compatibility

Example: Adding adapters Section While Keeping cli_tools

Problem: You need to add a new config section (adapters) to replace an old one (cli_tools) without breaking existing configs.

Solution Pattern (from models/​config.py):

from typing import Optional
from pydantic import BaseModel
import warnings

class Config(BaseModel):
    """Root configuration model."""

    # New section (preferred)
    adapters: Optional[dict[str, AdapterConfig]] = None

    # Legacy section (deprecated)
    cli_tools: Optional[dict[str, CLIToolConfig]] = None

    def model_post_init(self, __context):
        """Post-initialization validation."""
        # Ensure at least one section exists
        if self.adapters is None and self.cli_tools is None:
            raise ValueError(
                "Configuration must include either 'adapters' or 'cli_tools' section"
            )

        # Emit deprecation warning for old section
        if self.cli_tools is not None and self.adapters is None:
            warnings.warn(
                "The 'cli_tools' configuration section is deprecated. "
                "Please migrate to 'adapters' section with explicit 'type' field. "
                "See migration guide: docs/​migration/​cli_tools_to_adapters.md",
                DeprecationWarning,
                stacklevel=2,
            )

Key Techniques:

• Use Optional for both old and new sections
• Validate in model_post_init() that at least one exists
• Emit DeprecationWarning when old section is used
• Reference migration documentation in warning message
• Allow both sections temporarily for gradual migration

Pattern 2: Type Discrimination with Discriminated Unions

Example: CLI vs HTTP Adapters

Problem: You have config objects that can be one of several types (CLI adapter, HTTP adapter, etc).

Solution Pattern (from models/​config.py):

from typing import Annotated, Literal, Union
from pydantic import BaseModel, Field

class CLIAdapterConfig(BaseModel):
    """Configuration for CLI-based adapter."""
    type: Literal["cli"] = "cli"
    command: str
    args: list[str]
    timeout: int = 60

class HTTPAdapterConfig(BaseModel):
    """Configuration for HTTP-based adapter."""
    type: Literal["http"] = "http"
    base_url: str
    api_key: Optional[str] = None
    timeout: int = 60
    max_retries: int = 3

# Discriminated union - Pydantic uses 'type' field to determine which model
AdapterConfig = Annotated[
    Union[CLIAdapterConfig, HTTPAdapterConfig],
    Field(discriminator="type")
]

YAML Usage:

adapters:
  claude:
    type: cli  # Discriminator field
    command: "claude"
    args: ["-p", "{prompt}"]
    timeout: 60

  ollama:
    type: http  # Different type triggers HTTPAdapterConfig
    base_url: "http://localhost:11434"
    timeout: 120

Key Techniques:

• Use Literal["value"] for discriminator field with default value
• Create Annotated[Union[...], Field(discriminator="type")]
• Pydantic automatically routes to correct model based on type field
• Each type has different required fields (validated automatically)

Pattern 3: Environment Variable Substitution

Example: API Keys and Secrets

Problem: You need to inject secrets from environment variables without hardcoding in YAML.

Solution Pattern (from models/​config.py):

import os
import re
from pydantic import BaseModel, field_validator

class HTTPAdapterConfig(BaseModel):
    """Configuration for HTTP-based adapter."""
    base_url: str
    api_key: Optional[str] = None

    @field_validator("api_key", "base_url")
    @classmethod
    def resolve_env_vars(cls, v: Optional[str], info) -> Optional[str]:
        """Resolve ${ENV_VAR} references in string fields."""
        if v is None:
            return v

        # Pattern: ${VAR_NAME}
        pattern = r"\$\{([^}]+)\}"
        is_api_key = info.field_name == "api_key"

        def replacer(match):
            env_var = match.group(1)
            value = os.getenv(env_var)
            if value is None:
                # For optional fields like api_key, use sentinel
                if is_api_key:
                    return "__MISSING_API_KEY__"
                # For required fields, raise error
                raise ValueError(
                    f"Environment variable '{env_var}' is not set. "
                    f"Required for configuration."
                )
            return value

        result = re.sub(pattern, replacer, v)

        # If api_key has sentinel marker, return None (graceful degradation)
        if is_api_key and "__MISSING_API_KEY__" in result:
            return None

        return result

YAML Usage:

adapters:
  openrouter:
    type: http
    base_url: "https://openrouter.ai/​api/​v1"
    api_key: "${OPENROUTER_API_KEY}"  # Resolved from environment

Key Techniques:

• Use @field_validator on fields that may contain env vars
• Use info.field_name to customize behavior per field
• Regex pattern r"\$\{([^}]+)\}" to find ${VAR_NAME}
• For optional fields (api_key): return None if env var missing
• For required fields (base_url): raise ValueError if env var missing
• Always load .env file first with dotenv.load_dotenv() in load_config()

Pattern 4: Writing Migration Scripts

Example: CLI Tools to Adapters Migration

Problem: You need to migrate existing config files from old format to new format automatically.

Solution Pattern (from scripts/​migrate_config.py):

#!/​usr/​bin/​env python3
"""
Migration script: cli_tools → adapters

Migrates config.yaml from legacy cli_tools format to new adapters format
with explicit type fields.

Usage:
    python scripts/​migrate_config.py [path/​to/​config.yaml]
"""

import shutil
import sys
from pathlib import Path
from typing import Any, Dict
import yaml

def migrate_config_dict(config: Dict[str, Any]) -> Dict[str, Any]:
    """
    Migrate config dictionary from cli_tools to adapters format.

    Returns:
        Migrated config with adapters section
    """
    # If already migrated, return as-is
    if "adapters" in config and "cli_tools" not in config:
        print("Info: Config already migrated (has 'adapters' section)")
        return config

    # If no cli_tools, nothing to migrate
    if "cli_tools" not in config:
        print("Warning: No 'cli_tools' section found, nothing to migrate")
        return config

    # Create new config with adapters
    migrated = config.copy()

    # Transform cli_tools → adapters
    adapters = {}
    for name, cli_config in config["cli_tools"].items():
        adapters[name] = {
            "type": "cli",  # Add explicit type discriminator
            "command": cli_config["command"],
            "args": cli_config["args"],
            "timeout": cli_config["timeout"],
        }

    migrated["adapters"] = adapters
    del migrated["cli_tools"]

    print(f"Success: Migrated {len(adapters)} CLI tools to adapters format")
    return migrated

def migrate_config_file(path: str) -> None:
    """
    Migrate config file from cli_tools to adapters format.

    Creates a backup at {path}.bak before modifying.
    """
    config_path = Path(path)

    if not config_path.exists():
        raise FileNotFoundError(f"Config file not found: {path}")

    # Create backup BEFORE modifying
    backup_path = Path(f"{path}.bak")
    shutil.copy2(config_path, backup_path)
    print(f"Created backup: {backup_path}")

    # Load config
    with open(config_path, "r") as f:
        config = yaml.safe_load(f)

    # Migrate
    migrated = migrate_config_dict(config)

    # Write migrated config
    with open(config_path, "w") as f:
        yaml.dump(migrated, f, default_flow_style=False, sort_keys=False)

    print(f"Migrated config written to: {config_path}")
    print(f"\nInfo: Review the changes and delete {backup_path} when satisfied.")

def main():
    """Main entry point."""
    config_path = sys.argv[1] if len(sys.argv) > 1 else "config.yaml"

    print(f"Migrating config: {config_path}")
    print("-" * 50)

    try:
        migrate_config_file(config_path)
        print("\nMigration complete!")
        print("\nNext steps:")
        print("1. Review the migrated config.yaml")
        print("2. Test loading: python -c 'from models.config import load_config; load_config()'")
        print("3. Delete backup if satisfied: rm config.yaml.bak")
    except Exception as e:
        print(f"\nError: Migration failed: {e}")
        sys.exit(1)

if __name__ == "__main__":
    main()

Key Techniques:

• Always create backup before modifying config file (shutil.copy2)
• Idempotent migrations: Check if already migrated, return early if so
• Separate dict and file logic: migrate_config_dict() for logic, migrate_config_file() for I/​O
• Clear console output: Print status messages for user feedback
• Testing instructions: Print validation commands after migration
• Error handling: Catch exceptions, print helpful message, exit with code 1
• YAML preservation: Use sort_keys=False to preserve key order

Pattern 5: Field Validation for Path Resolution

Example: Database Path with Environment Variables

Problem: You need to resolve relative paths and environment variables for config fields.

Solution Pattern (from models/​config.py):

import os
import re
from pathlib import Path
from pydantic import BaseModel, field_validator

class DecisionGraphConfig(BaseModel):
    """Configuration for decision graph memory."""
    db_path: str = "decision_graph.db"

    @field_validator("db_path")
    @classmethod
    def resolve_db_path(cls, v: str) -> str:
        """
        Resolve db_path to absolute path relative to project root.

        Processing steps:
        1. Resolve ${ENV_VAR} environment variable references
        2. Convert relative paths to absolute paths relative to project root
        3. Keep absolute paths unchanged
        4. Return normalized absolute path as string

        Examples:
            "decision_graph.db" → "/​path/​to/​project/​decision_graph.db"
            "/​tmp/​foo.db" → "/​tmp/​foo.db" (unchanged)
            "${DATA_DIR}/​graph.db" → "/​var/​data/​graph.db" (if DATA_DIR=/​var/​data)
        """
        # Step 1: Resolve environment variables
        pattern = r"\$\{([^}]+)\}"

        def replacer(match):
            env_var = match.group(1)
            value = os.getenv(env_var)
            if value is None:
                raise ValueError(
                    f"Environment variable '{env_var}' is not set. "
                    f"Required for db_path configuration."
                )
            return value

        resolved = re.sub(pattern, replacer, v)

        # Step 2: Convert to Path object
        path = Path(resolved)

        # Step 3: If relative, make it relative to project root
        if not path.is_absolute():
            # This file is at: project_root/​models/​config.py
            # Project root is two levels up from this file
            project_root = Path(__file__).parent.parent
            path = (project_root / path).resolve()

        # Step 4: Return as string (normalized, absolute)
        return str(path)

Key Techniques:

• Resolve env vars BEFORE path resolution
• Use Path(__file__).parent.parent to find project root
• Convert relative paths to absolute (prevents CWD issues)
• Keep absolute paths unchanged
• Return as string for serialization compatibility

Pattern 6: Deprecating Fields with Validation

Example: Deprecating similarity_threshold in Favor of tier_boundaries

Problem: You need to replace a single config field with a more complex structure.

Solution Pattern (from models/​config.py):

from pydantic import BaseModel, Field, field_validator

class DecisionGraphConfig(BaseModel):
    """Configuration for decision graph memory."""

    # DEPRECATED field (kept for backward compatibility)
    similarity_threshold: float = Field(
        0.7,
        ge=0.0,
        le=1.0,
        description="DEPRECATED: Use tier_boundaries instead.",
    )

    # NEW field (preferred)
    tier_boundaries: dict[str, float] = Field(
        default_factory=lambda: {"strong": 0.75, "moderate": 0.60},
        description="Similarity score boundaries for tiered injection"
    )

    @field_validator("tier_boundaries")
    @classmethod
    def validate_tier_boundaries(cls, v: dict[str, float]) -> dict[str, float]:
        """Validate tier boundaries: strong > moderate > 0."""
        if not isinstance(v, dict) or "strong" not in v or "moderate" not in v:
            raise ValueError("tier_boundaries must have 'strong' and 'moderate' keys")

        if not (0.0 < v["moderate"] < v["strong"] <= 1.0):
            raise ValueError(
                f"tier_boundaries must satisfy: 0 < moderate ({v['moderate']}) "
                f"< strong ({v['strong']}) <= 1"
            )

        return v

YAML Usage:

decision_graph:
  # OLD (still works, but deprecated in field description)
  similarity_threshold: 0.7

  # NEW (preferred)
  tier_boundaries:
    strong: 0.75
    moderate: 0.60

Key Techniques:

• Keep deprecated field with default value
• Add "DEPRECATED" to field description
• Validate new field structure with @field_validator
• Document migration in code comments and CLAUDE.md
• Eventually remove deprecated field in future major version

Testing Migration Scripts

Before Deployment Checklist

1. Unit Test the Migration Logic:

   def test_migrate_config_dict():
       """Test migration transforms cli_tools to adapters."""
       old_config = {
           "cli_tools": {
               "claude": {
                   "command": "claude",
                   "args": ["-p", "{prompt}"],
                   "timeout": 60
               }
           }
       }
   
       migrated = migrate_config_dict(old_config)
   
       assert "adapters" in migrated
       assert "cli_tools" not in migrated
       assert migrated["adapters"]["claude"]["type"] == "cli"
       assert migrated["adapters"]["claude"]["command"] == "claude"
   

2. Test Idempotency:

   def test_migrate_idempotent():
       """Test migrating already-migrated config is safe."""
       already_migrated = {
           "adapters": {
               "claude": {"type": "cli", "command": "claude"}
           }
       }
   
       result = migrate_config_dict(already_migrated)
       assert result == already_migrated  # No changes
   

3. Manual Testing Steps:

   # 1. Create test config
   cp config.yaml config.test.yaml
   
   # 2. Run migration
   python scripts/​migrate_config.py config.test.yaml
   
   # 3. Verify backup created
   ls -la config.test.yaml.bak
   
   # 4. Test loading migrated config
   python -c "from models.config import load_config; c = load_config('config.test.yaml'); print('OK')"
   
   # 5. Compare files
   diff config.test.yaml.bak config.test.yaml
   
   # 6. Clean up
   rm config.test.yaml config.test.yaml.bak
   

4. Load-Time Validation:

   # After migration, always test that config loads without errors
   python -c "from models.config import load_config; load_config()"
   

Complete Migration Workflow

When you need to evolve a config schema:

Step 1: Update Pydantic Models

1. Add new section/​fields as Optional (don't break existing configs)
2. Keep old section/​fields for backward compatibility
3. Add @field_validator for new field validation
4. Add deprecation warnings in model_post_init()

Step 2: Write Migration Script

1. Create scripts/​migrate_*.py with clear docstring
2. Implement migrate_config_dict() for logic (testable)
3. Implement migrate_config_file() for I/​O (backup, load, migrate, save)
4. Add main() with CLI argument parsing
5. Print clear instructions after migration

Step 3: Test Migration

1. Write unit tests for migrate_config_dict()
2. Test idempotency (running twice is safe)
3. Test edge cases (missing sections, already migrated)
4. Manually test on real config file
5. Verify migrated config loads successfully

Step 4: Document Migration

1. Update CLAUDE.md with migration instructions
2. Add migration notes to config.yaml comments
3. Reference migration script in deprecation warnings
4. Update README if needed

Step 5: Deploy

1. Commit schema changes + migration script together
2. Announce deprecation to users
3. Provide migration timeline (e.g., "deprecated in v2.0, removed in v3.0")
4. Keep backward compatibility for at least one major version

Real-World Example: The cli_tools → adapters Migration

Context: AI Counsel needed to support both CLI and HTTP adapters, requiring type discrimination.

Changes Made:

1. Schema Evolution (models/​config.py):

   • Created CLIAdapterConfig and HTTPAdapterConfig with type discriminator
   • Made adapters and cli_tools both optional
   • Added validation that at least one exists
   • Added deprecation warning for cli_tools

2. Migration Script (scripts/​migrate_config.py):

   • Transforms cli_tools → adapters with type: "cli"
   • Creates backup before modifying
   • Idempotent (safe to run multiple times)
   • Clear user feedback and next steps

3. Testing:

   • Unit tests for migrate_config_dict()
   • Integration tests for file I/​O
   • Manual testing on production config

4. Documentation:

   • Updated CLAUDE.md with migration guide
   • Added comments to config.yaml explaining both formats
   • Referenced migration script in deprecation warning

Result: Users can migrate seamlessly with one command, and old configs continue working with a warning.

Common Patterns Summary

File References

• Config Models: /​Users/​harrison/​Github/​ai-counsel/​models/​config.py
• Migration Script: /​Users/​harrison/​Github/​ai-counsel/​scripts/​migrate_config.py
• Config File: /​Users/​harrison/​Github/​ai-counsel/​config.yaml
• Project Docs: /​Users/​harrison/​Github/​ai-counsel/​CLAUDE.md

Key Takeaways

1. Never break existing configs - always provide migration path
2. Automate migrations - don't force manual editing
3. Use Pydantic validators - catch errors at load time
4. Support env vars - never hardcode secrets
5. Test thoroughly - unit + integration + manual testing
6. Document clearly - in code, CLAUDE.md, and warnings
7. Version carefully - deprecate → warn → remove (over multiple versions)

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When you detect config schema evolution needs, activate this skill and follow these patterns to ensure smooth, backward-compatible migrations.