kiss-data-migration-plan

name	kiss-data-migration-plan
description	Drafts the data migration plan for a project that replaces or upgrades a system with existing data. Covers migration strategy (Big Bang / Phased / Parallel Run / Trickle), source-to-target field mapping, data quality rules, extraction/transformation/load (ETL) outline, cutover window, rollback triggers, reconciliation approach, and post-migration validation checklist. Does not execute migrations or modify databases. Use when a Waterfall project involves migrating data from a legacy system, when a customer requires a formal migration runbook, or when data continuity is a contractual obligation.
compatibility	Requires kiss project structure with .kiss/ directory
metadata	{"author":"github-kiss","source":"agent-skills/kiss-data-migration-plan/kiss-data-migration-plan.md"}
user-invocable	true
disable-model-invocation	false

User Input

$ARGUMENTS

Consider the user input before proceeding (if not empty).

Audience and tone (interactive mode)

When KISS_AGENT_MODE=interactive (the default), assume the user has limited technical background and limited domain knowledge. Run this skill as a guided questionnaire:

• One question at a time. No walls of questions.
• Yes / no first. Phrase so yes, no, not sure, or skip is a valid answer.
• No jargon. Say "moving data from the old system to the new one" not "ETL pipeline"; "checking the data arrived correctly" not "reconciliation"; "when to stop and go back" not "rollback trigger".
• Choices, not blank fields. Offer lettered options (A/B/C/D). Always include "Not sure — sensible default".
• Always recommend. State which option you would pick and why.
• not sure / skip triggers a sensible default, marked "(default applied)" and a DMDEBT entry.

When KISS_AGENT_MODE=auto, skip the questionnaire and log decisions.

Migration strategies

Strategy	Description	When to use
Big Bang	All data moved in a single cutover window (downtime required)	Small dataset; short acceptable downtime
Phased	Data migrated in batches by domain or date range	Large dataset; partial go-live acceptable
Parallel Run	Old and new systems run simultaneously; data kept in sync	Zero downtime required; high risk tolerance needed
Trickle (CDC)	Continuous replication using Change Data Capture until cutover	Very large dataset; near-zero downtime

Inputs

• .kiss/context.yml
• {context.paths.docs}/analysis/srs.md — data entities, volumes, retention rules (FR-NNN / NFR-NNN related to data)
• {context.paths.docs}/architecture/intake.md — legacy system description, technology stack
• {context.paths.docs}/design/<feature>/data-model.md — target data model (if produced by kiss-dev-design)
• {context.paths.docs}/project/risk-register.md — data-related risks

Outputs

• {context.paths.docs}/analysis/data-migration-plan.md — the migration strategy and scope (primary artefact; owned by business-analyst)
• {context.paths.docs}/analysis/field-mapping.md — source-to- target field mapping table (business logic; owned by business-analyst)
• {context.paths.docs}/operations/migration-runbook.md — step-by- step cutover runbook (technical execution; owned by DevOps)
• {context.paths.docs}/analysis/data-migration-plan.extract — companion KEY=VALUE ledger (DM_STRATEGY, DM_CUTOVER_WINDOW, DM_ROLLBACK_TRIGGER, DM_RECORD_VOLUME, DM_VALIDATION_APPROACH)
• {context.paths.docs}/analysis/dm-debts.md — open items (DMDEBT-NN: …)

Context Update

Does not mutate .kiss/context.yml.

Handoffs

• kiss-phase-gate (orr / go-live gate) checks whether the migration plan is complete and the dry-run result is documented.
• kiss-baseline can snapshot field-mapping.md as part of the design baseline.
• kiss-test-strategy should include a data-validation test tier for the migration; this plan feeds that tier.
• kiss-risk-register should capture migration-specific risks (data loss, downtime over-run, rollback complexity).
• kiss-deployment-strategy is read to align the migration cutover window with the deployment window.

AI authoring scope

Does:

• Draft the migration strategy selection with rationale.
• Produce a source-to-target field mapping table from the data model and SRS entities.
• Define data quality rules (null handling, type coercion, dedup, encoding, date formats).
• Outline ETL steps at the logical level (extract, transform, validate, load, verify).
• Define cutover window, rollback triggers, and rollback procedure.
• Produce a post-migration reconciliation checklist.

Does not:

• Write ETL code or SQL scripts.
• Access any database or data file.
• Execute or schedule any migration step.

Outline

1. Determine scope — from user input, or ask: a. What is the source system? (name, type, rough record count) b. What data must be migrated? (all / subset by date / subset by entity) c. Is downtime acceptable? (yes / no / limited window)

2. Interactive questionnaire (interactive mode only): a. Source system type: A) relational DB, B) file system / CSV, C) another SaaS, D) mix. b. Record volume: A) < 100K, B) 100K–1M, C) 1M–100M, D) > 100M. c. Migration strategy preference (see table above). d. Cutover window: A) weekend maintenance, B) overnight weekday, C) business hours with parallel run, D) not decided. e. Rollback trigger: A) any reconciliation failure, B) > X% failure rate, C) manual decision only. f. Privacy / compliance: does the migrated data contain PII, financial data, or health records? (affects encryption and audit requirements).

3. Draft field mapping — for each target entity in the data model, map source fields to target fields, noting:

   • Data type transformation needed (e.g. VARCHAR → UUID)
   • Nullable / required differences
   • Default value for missing source data
   • Transformation rule (concatenate, split, lookup, generate) Mark unmapped fields as DMDEBT entries.

4. Define data quality rules:

   • Null handling policy per critical field
   • Duplicate detection strategy
   • Character encoding (UTF-8 requirement)
   • Date / time zone normalisation
   • Referential integrity checks

5. Outline ETL process (logical, not code):

   • Extract: source query / export mechanism, incremental vs full
   • Transform: rule application order, error handling
   • Load: batch size, idempotency strategy, index rebuild
   • Verify: record count match, sample spot-checks, checksum

6. Define cutover plan:

   • Freeze point (when writes to old system stop)
   • Migration window duration estimate
   • Rollback decision point and trigger
   • Communication plan for downtime

7. Draft migration runbook — step-by-step numbered procedures for the cutover team, including go / no-go decision checkpoints.

8. Post-migration validation checklist — items the team must verify within 24 h of cutover before decommissioning the source.

9. Write outputs — write plan, field mapping, runbook, and extract.

10. Summary — print strategy, estimated cutover window, unmapped field count (DMDEBT), and recommended next action.

Usage

<SKILL_DIR> = the integration's skills root.

DM_STRATEGY="phased" \
DM_SOURCE="legacy-mysql-5.7" \
DM_RECORD_VOLUME="500000" \
DM_CUTOVER_WINDOW="2026-07-05T22:00/2026-07-06T06:00" \
  bash <SKILL_DIR>/kiss-data-migration-plan/scripts/bash/draft-dm-plan.sh --auto

Answer keys

Key	Meaning	Default
DM_STRATEGY	big-bang / phased / parallel / trickle	big-bang
DM_SOURCE	Source system description	(ask in interactive)
DM_RECORD_VOLUME	Approximate total record count	(ask in interactive)
DM_CUTOVER_WINDOW	ISO interval start/end	(ask in interactive)
DM_ROLLBACK_TRIGGER	any-failure / threshold / manual	any-failure
DM_HAS_PII	true / false — data contains PII	false