Codebase migration batches
Skill yeaight7/agent-powerups/plugins/software-engineering/skills/codebase-migration-batches
Use when a wide refactor or migration is too large to ship as one change — API renames across many files, framework or config migrations, or mechanical codemod-backed refactors that need reviewable batches.From its SKILL.md
npx -y skills add yeaight7/agent-powerups --skill codebase-migration-batchesAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
2.3 KB, 474 tokens by cl100k_base, as published. Nobody here has run it
Codebase Migration Batches
Use this skill for broad, repetitive refactors that should move in slices.
When to Use
- API renames across many files
- Framework or config migrations
- Mechanical refactors backed by codemods or search/replace rules
- Any change where "all at once" would be too large to review safely
Core Rules
- Define the transform precisely before editing files
- Keep one migration theme per batch
- Prefer deterministic codemods over manual repetition
- Maintain a batch ledger so already-migrated files are not touched twice
- Validate each batch before moving to the next
- No auto-commit, auto-push, or auto-PR
Workflow
-
Define the migration
- What changes
- What does not change
- How success is measured
-
Measure the blast radius
- Count affected files
- Sample representative files
- Identify edge cases before the first codemod pass
-
Choose batch size
- Small enough to review and debug
- Large enough to make visible progress
-
Run the transform
- Prefer AST-aware tools when syntax matters
- Use plain search/replace only for truly mechanical edits
- Keep any helper scripts under versioned project control if they are reused
-
Verify the batch
- Run targeted tests first
- Run broader validation when the batch changes shared primitives
- Track residual manual fixes separately from the codemod logic
-
Advance or stop
- Continue if the batch is stable
- Shrink the batch if review or validation gets noisy
- Stop and redesign the transform if repeated manual cleanup dominates
Batch Ledger
Track at least:
Batch ID
Files included
Transform rule
Validation run
Known exceptions
Verification
- The transform was defined precisely — what changes, what does not — before the first file edit
- Each batch held one migration theme and passed validation before the next began
- The batch ledger records files, transform rule, validation run, and exceptions for every batch
- No already-migrated file was touched twice; no auto-commit, auto-push, or auto-PR occurred
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most architecture codebase skills give in 474 tokens
Counted across 811 of the 1,134 authors here whose files we hold, read 2026-08-07
- Ask the user which candidate to explorein 45 of 811, across 15 files
- Apply the deletion test to suspected shallow modulesin 43 of 811, across 15 files
- Read any relevant architecture decision records firstin 31 of 811, across 8 files
- Use exact glossary terms in every suggestionin 30 of 811, across 10 files
- Accept dependencies instead of creating themin 24 of 811, across 5 files
- Include before and after visualisations for each candidatein 24 of 811, across 5 files
- Read the domain glossary before exploringin 24 of 811, across 6 files
- Return results instead of producing side effectsin 23 of 811, across 4 files
- Explore the codebase for shallow modules and frictionin 23 of 811, across 3 files
- Introduce seams only where things varyin 22 of 811, across 3 files
- Reduce the number of methodsin 21 of 811, across 2 files
- Design deep modules with small interfacesin 21 of 811, across 3 files
Said here and by no other author read
- Define the transform precisely before editing files
- Keep one migration theme per batch
- Prefer deterministic codemods over manual repetition
- Maintain a batch ledger of migrated files
- Validate each batch before moving to the next
- Count affected files and sample representative files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.