Migration Architect

The agent generates phased migration plans with risk assessment, compatibility analysis, and rollback runbooks for database, service, infrastructure, and API migrations. It validates schema compatibility, detects breaking changes, and produces rollback procedures with trigger conditions and communication templates.

Quick Start

# Generate a phased migration plan
python scripts/migration_planner.py --input migration_spec.json --output plan.json --format both

# Check schema compatibility between versions
python scripts/compatibility_checker.py --before v1_schema.json --after v2_schema.json --type database

# Generate rollback runbook from a migration plan
python scripts/rollback_generator.py --input plan.json --output runbook.json --format both

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Core Workflows

Workflow 1: Plan a Database Migration

1. Create migration spec JSON with type: "database", source, target, constraints (data volume, max downtime, dependencies)
2. Run migration_planner.py to generate phased plan with risk assessment
3. Run compatibility_checker.py to detect breaking schema changes
4. Review generated phases -- each should have validation criteria and rollback triggers
5. Validation checkpoint: Zero unaddressed breaking changes; every phase has rollback steps; downtime within max_downtime_minutes constraint

python scripts/compatibility_checker.py --before v1.json --after v2.json --type database --format json
python scripts/migration_planner.py --input spec.json --output plan.json
python scripts/rollback_generator.py --input plan.json --output runbook.json

Workflow 2: Plan a Service Migration (Strangler Fig)

1. Define spec with type: "service", pattern: "strangler_fig", source/target services, and dependencies
2. Generate migration plan with traffic routing phases (10% -> 50% -> 100%)
3. Review rollback triggers at each traffic percentage gate
4. Validation checkpoint: Circuit breaker thresholds defined; monitoring configured; feature flags lock rollout percentage between phases

Workflow 3: Validate Migration Compatibility

1. Export before/after schemas as JSON (database or OpenAPI format)
2. Run compatibility_checker.py to identify breaking changes, type mismatches, and constraint violations
3. Review generated migration scripts and their rollback counterparts
4. Validation checkpoint: All breaking changes have migration scripts; rollback scripts verified; validation queries pass

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Migration Patterns

Pattern	Type	Best For
Expand-Contract	Database	Zero-downtime schema evolution with backfill
Dual-Write	Database	Maintaining consistency during transition
Change Data Capture	Database	Large dataset migration with eventual consistency
Strangler Fig	Service	Incremental service replacement via gateway routing
Parallel Run	Service	Shadow traffic for correctness validation
Canary Deployment	Service	Gradual traffic shift with metric monitoring
Blue-Green	Infrastructure	Instant cutover with full rollback capability

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Anti-Patterns

• Big bang migration -- migrating everything at once maximizes blast radius; always use phased execution with validation gates
• Stale backups -- taking backups at plan creation instead of immediately before execution; always create fresh backup as first execution task
• Skipping staging -- production-only migration attempts have no safety net; run the full process in a staging environment first
• No data reconciliation -- row counts match but data differs; use checksum validation and business logic queries on critical tables
• Ignoring dependent systems -- breaking downstream consumers during cutover; map all dependencies and coordinate migration windows
• Feature flag drift -- changing rollout percentage mid-phase causes inconsistent user experience; lock flags during each phase

Troubleshooting

Problem	Cause	Solution
Compatibility checker reports false positives on type changes	Source and target schemas use vendor-specific type aliases (e.g., serial vs int) that are not in the built-in type compatibility matrix	Normalize type aliases to canonical SQL/JSON types in both schema files before running the checker
Migration planner generates unrealistic duration estimates	Complexity multiplier does not account for organizational factors like change-board approvals or cross-team coordination	Adjust the constraints block in your input spec to include special_requirements entries for each non-technical blocker
Rollback generator produces empty rollback steps for a phase	The phase name in the migration plan does not match any of the recognized keywords (migration, cutover, preparation)	Use standard phase names from the planner output, or ensure custom phase names contain one of the recognized keywords
Data validation fails after migration but row counts match	Soft deletes, filtered records, or computed columns cause hash/checksum mismatches even when primary data is intact	Use business logic validation (aggregate queries on key columns) instead of full-row checksums for tables with soft deletes or generated columns
Dual-write pattern causes write conflicts during cutover	Race conditions between source and target systems when replication lag exceeds the write interval	Implement idempotent writes with conflict-resolution timestamps, and increase the delta-sync frequency before the cutover window
Rollback triggered but legacy database backup is stale	Backup was taken at plan creation time rather than immediately before the migration execution phase	Always create a fresh backup as the first task of the migration execution phase; reference migration_planner.py preparation-phase tasks
Feature flag rollout causes inconsistent user experience	Hash-based routing sends the same user to different paths across sessions when the flag name or rollout percentage changes mid-migration	Lock the flag name and rollout percentage during each migration phase; only adjust between validated phase gates

Success Criteria

• Data Integrity: 100% of records pass post-migration checksum and referential-integrity validation with zero data loss or corruption.
• Downtime Within Budget: Actual service unavailability stays within the max_downtime_minutes constraint defined in the migration spec (target: zero-downtime for critical systems).
• Performance Parity: P95 latency and throughput on the target system are within 10% of pre-migration baseline measurements during the first 72 hours after cutover.
• Rollback Readiness: Every migration phase has a tested rollback procedure that can restore the previous state within 25% of the original phase duration.
• Stakeholder Sign-off: All stakeholders listed in the migration plan confirm acceptance criteria are met before legacy decommission begins.
• Zero Critical Defects: No severity-critical or severity-high issues remain unresolved 48 hours after cutover; all issues are tracked with owners and ETAs.
• Compliance Continuity: All regulatory and compliance controls (audit logging, access controls, encryption) remain fully operational throughout the migration and are validated in the target environment.

Scope & Limitations

This skill covers:

• End-to-end migration planning for databases, services, infrastructure, and APIs with phased execution and validation gates.
• Automated compatibility analysis between schema versions (SQL and REST/JSON) including breaking-change detection and migration script generation.
• Rollback runbook generation with trigger conditions, data recovery plans, escalation matrices, and communication templates.
• Risk assessment frameworks covering technical, business, operational, and compliance risk categories.

This skill does NOT cover:

• Actual execution of migrations against live systems -- the tools generate plans, reports, and scripts but do not connect to databases or cloud APIs.
• Application-level code refactoring required to support new schemas or APIs; see engineering/api-design-reviewer for API contract changes and engineering/database-designer for schema design.
• Real-time monitoring, alerting, or dashboard provisioning during migration execution; see engineering/observability-designer for observability setup.
• Cloud cost optimization or capacity planning for target infrastructure; see engineering/ci-cd-pipeline-builder for deployment pipeline configuration.

Integration Points

Skill	Integration	Data Flow
engineering/database-designer	Design target schema before migration planning	Target schema JSON feeds into compatibility_checker.py --after
engineering/api-design-reviewer	Validate API contract changes for service migrations	OpenAPI spec diffs feed into compatibility_checker.py --type api
engineering/observability-designer	Set up monitoring dashboards referenced in migration runbooks	Migration plan success metrics inform alerting rule definitions
engineering/ci-cd-pipeline-builder	Embed migration validation in CI/CD stages	compatibility_checker.py runs as a pipeline stage; migration_planner.py --validate gates deployments
engineering/runbook-generator	Extend rollback runbooks with operational procedures	rollback_generator.py output serves as input for detailed operational runbooks
engineering/release-manager	Coordinate migration cutover with release schedules	Migration plan phases and timelines align with release windows and feature-flag rollout stages

Tool Reference

migration_planner.py

Purpose: Generates comprehensive, phased migration plans with risk assessment, rollback strategies, timeline estimates, and stakeholder communication structures from a JSON migration specification.

Usage:

python scripts/migration_planner.py --input <spec.json> [--output <plan.json>] [--format <format>] [--validate]

Flags/Parameters:

Flag	Required	Default	Description
--input, -i	Yes	--	Input migration specification file (JSON). Must contain type, source, and target fields.
--output, -o	No	stdout	Output file path for the migration plan (JSON). Text output saved to same path with .txt extension when format includes text.
--format, -f	No	both	Output format. Choices: json, text, both.
--validate	No	false	Validate the migration specification without generating a plan. Exits with 0 if valid.

Input Specification Fields:

• type (required): Migration type -- database, service, infrastructure, data, or api.
• source (required): Source system identifier.
• target (required): Target system identifier.
• pattern: Migration pattern (e.g., schema_change, data_migration, strangler_fig, parallel_run, cloud_migration, on_prem_to_cloud).
• constraints.data_volume_gb: Data volume in GB (affects complexity scoring).
• constraints.dependencies: List of dependent systems.
• constraints.max_downtime_minutes: Maximum allowed downtime in minutes.
• constraints.special_requirements: List of additional requirements that increase complexity.
• constraints.compliance_requirements: List of compliance frameworks that apply.

Example:

python scripts/migration_planner.py --input migration_spec.json --output plan.json --format both

Output Formats:

• JSON: Complete MigrationPlan object with migration_id, phases (each with tasks, validation criteria, rollback triggers), risks (categorized by severity), rollback_plan, success_criteria, and stakeholders.
• Text: Human-readable report with sections for phases, risk assessment, rollback strategy, success criteria, and stakeholders.

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compatibility_checker.py

Purpose: Analyzes schema and API compatibility between two versions, identifies breaking changes, data type mismatches, constraint violations, and generates migration script suggestions with rollback counterparts.

Usage:

python scripts/compatibility_checker.py --before <old.json> --after <new.json> [--type <schema_type>] [--output <report.json>] [--format <format>]

Flags/Parameters:

Flag	Required	Default	Description
--before	Yes	--	Path to the before/old schema file (JSON).
--after	Yes	--	Path to the after/new schema file (JSON).
--type	No	database	Schema type to analyze. Choices: database, api.
--output, -o	No	stdout	Output file path for the compatibility report (JSON).
--format, -f	No	both	Output format. Choices: json, text, both.

Input Schema Format (database): JSON with a tables object where each key is a table name containing columns (with type, nullable, length, default per column) and constraints (with primary_key, foreign_key, unique, check, index arrays).

Input Schema Format (api): OpenAPI-style JSON with paths (keyed by route, containing HTTP methods) and components.schemas (keyed by model name with properties, required arrays, and field type definitions).

Example:

python scripts/compatibility_checker.py --before v1_schema.json --after v2_schema.json --type database --format json

Output Formats:

• JSON: CompatibilityReport with overall_compatibility level, counts by change type (breaking_changes_count, potentially_breaking_count, non_breaking_changes_count, additive_changes_count), detailed issues list (each with severity, impact, suggested migration), migration_scripts (with rollback scripts and validation queries), risk_assessment, and recommendations.
• Text: Human-readable report with color-coded severity sections, issue details, and migration script listings.

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rollback_generator.py

Purpose: Takes a migration plan (typically the JSON output of migration_planner.py) and generates a comprehensive rollback runbook with phase-by-phase reversal steps, automated trigger conditions, data recovery plans, escalation matrices, and communication templates.

Usage:

python scripts/rollback_generator.py --input <plan.json> [--output <runbook.json>] [--format <format>]

Flags/Parameters:

Flag	Required	Default	Description
--input, -i	Yes	--	Input migration plan file (JSON). Expects output from migration_planner.py or any JSON with migration_id, migration_type, and phases fields.
--output, -o	No	stdout	Output file path for the rollback runbook (JSON).
--format, -f	No	both	Output format. Choices: json, text, both.

Example:

python scripts/migration_planner.py --input spec.json --output plan.json --format json
python scripts/rollback_generator.py --input plan.json --output runbook.json --format both

Output Formats:

• JSON: RollbackRunbook object with runbook_id, rollback_phases (each containing ordered steps with script content, validation commands, success criteria, and failure escalation), trigger_conditions (with metric thresholds and auto-execute flags), data_recovery_plan (backup location, recovery scripts, estimated recovery time), communication_templates (for technical, business, and executive audiences), escalation_matrix, validation_checklist, post_rollback_procedures, and emergency_contacts.
• Text: Human-readable runbook with phase-by-phase rollback instructions, trigger condition summaries, communication templates, and a post-rollback checklist.