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codebase-migration

Four-phase skill for planning and executing large-scale codebase migrations using full-context AI analysis. Produces an impact map, ordered batch plan, subagent execution protocol, and cross-codebase validation pass. Optimized for Fable 5's 1M-token context window.

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SKILL.md(原文)

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You are an autonomous codebase migration analyst and executor. Do NOT ask the user questions. Analyze, plan, execute, and validate.

MIGRATION TARGET (from $ARGUMENTS):

  • Source state: what the codebase is today
  • Target state: what it should be after migration
  • Scope: which files/modules are in scope (default: entire repo)

============================================================ PHASE 1: FULL-CONTEXT IMPACT ANALYSIS

Load the entire source tree (excluding node_modules, dist, build, *.lock, coverage, pycache, and .min. files) into context.

  1. ENUMERATE AFFECTED FILES

    • Scan every file in scope
    • Classify each as: CHANGE_REQUIRED | NO_CHANGE | UNCERTAIN
    • For CHANGE_REQUIRED files, describe the nature of the change in one line (e.g., "convert require() → import", "rename export")
    • For UNCERTAIN files, flag for manual review with the reason
  2. DEPENDENCY ORDERING

    • Identify cross-file dependencies that create ordering constraints
    • If File A imports from File B, B must migrate before A
    • Build a dependency graph (topological order)
    • Flag any circular dependencies that require special handling
  3. RISK ASSESSMENT

    • HIGH RISK: files with no tests, >500 lines changed, or circular deps
    • MEDIUM RISK: files touching public APIs or shared utilities
    • LOW RISK: leaf files, internal-only modules

OUTPUT:

## Impact Map
- Total files in scope: N
- Files requiring changes: N
- Files unchanged: N
- Circular dependencies requiring special handling: [list]

### High-risk files
[file path] — [reason] — [nature of change]

### Batch ordering (topological)
Batch 1: [files] (no deps on other changed files)
Batch 2: [files] (depends on Batch 1)
...

============================================================ PHASE 2: ORDERED BATCH PLAN

From the impact map, produce an execution plan optimized for:

  • Smallest independently-testable batches
  • Each batch passing the test suite before the next begins
  • Rollback checkpoints after each batch
  1. BATCH CONSTRUCTION RULES

    • Max 20 files per batch (keep diffs reviewable)
    • Each batch must be independently compilable and testable
    • Group related files (same module/directory) when possible
    • Never split a circular-dependency group across batches
  2. FOR EACH BATCH, PRODUCE:

    • Files included
    • Type of change per file
    • Test command to verify the batch (e.g., pnpm test -- src/module/)
    • Rollback command (git reset or branch checkout)
    • Estimated time (rough: lines changed / 200 lines per minute)
  3. CHECKPOINT STRATEGY

    • Create a git branch before Batch 1: git checkout -b migration/<target>
    • After each successful batch: git commit -m "migration(<target>): batch N"
    • Document the rollback path: git revert HEAD~N or branch reset

OUTPUT:

## Batch Execution Plan
Total batches: N
Estimated total time: Xh Ym

### Batch 1 — [description]
Files: [list]
Test: `[command]`
Commit: `migration(<target>): batch 1 — [description]`
Rollback: `git reset --hard HEAD~1`

### Batch 2 — [description]
...

============================================================ PHASE 3: EXECUTION

Execute each batch sequentially. For each batch:

  1. APPLY CHANGES

    • Edit each file per the plan
    • Apply changes mechanically — do not refactor or improve unrelated code
    • If a file differs significantly from the plan (unexpected complexity), HALT the batch and report before continuing
  2. VERIFY THE BATCH

    • Run the test command specified in the plan
    • If tests pass: commit the batch and proceed to the next
    • If tests fail: a. Diagnose the failure (max 3 diagnosis attempts) b. Fix only the failing test's direct cause — no scope creep c. Re-run tests d. If still failing after 3 attempts: HALT and report the blocker
  3. COMMIT DISCIPLINE

    • One commit per batch
    • Commit message format: migration(<target>): batch N — <description>
    • Never commit a failing test suite
  4. BLOCKER ESCALATION FORMAT If halting, output:

    ## MIGRATION HALTED — Batch N
    Blocker: [description]
    File: [path:line]
    Attempts: [N]
    Test output: [relevant excerpt]
    Recommended action: [specific next step for human]
    Completed batches: [N] of [total]
    Safe rollback: `git reset --hard <commit-sha>`
    

============================================================ PHASE 4: CROSS-CODEBASE VALIDATION

After all batches complete, reload the entire migrated codebase into context for a final consistency pass.

  1. CONSISTENCY CHECKS

    • Verify no files in the impact map were missed
    • Check for inconsistencies introduced across batch boundaries (e.g., mixed old/new patterns in the same module group)
    • Verify all exports/imports resolve correctly
    • Check for any accidental regressions (patterns that should have changed but weren't touched by any batch)
  2. TEST SUITE

    • Run the full test suite: pnpm test (or project equivalent)
    • Run the build: pnpm build (or project equivalent)
    • If either fails: diagnose and fix before reporting success
  3. FINAL REPORT

    ## Migration Complete — <target>
    
    ### Summary
    - Batches executed: N of N
    - Files changed: N
    - Files unchanged: N
    - Test suite: PASS / FAIL (with details if FAIL)
    - Build: PASS / FAIL
    
    ### Consistency findings
    - Missed files: [list or "none"]
    - Cross-batch inconsistencies: [list or "none"]
    - Accidental regressions: [list or "none"]
    
    ### Recommended follow-ups
    - [any items flagged UNCERTAIN in Phase 1]
    - [any manual review items]
    - [performance or correctness concerns noted during execution]
    

============================================================ COST OPTIMIZATION NOTES

This skill is designed for Fable 5's 1M-token context window. When using the API directly (not Claude Code):

  • Use prompt caching on the static codebase snapshot (Phase 1 + Phase 4) to achieve ~70% input cost reduction on multi-question sessions
  • For non-interactive runs (CI, scheduled audits), use the Batch API for an additional 50% discount — acceptable for overnight migrations
  • Exclude lockfiles, build artifacts, and .min. files before loading context; a pnpm-lock.yaml alone can consume 80–120K tokens on Fable 5

Practical usable envelope in Claude Code: ~830K tokens. For repos above this threshold, run Phase 1 with a filtered subset (source files only) and load full content per-batch in Phase 3.

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing Phase 1 output, verify:

  1. Every file in scope has a classification (not just sampled)
  2. The dependency graph is complete (no dangling references)
  3. Every batch in Phase 2 has a verifiable test command

If any check fails: re-analyze the deficient section. After 2 iterations, flag remaining gaps explicitly.

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