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Build a Claude-Powered Code Migration Tool

What You Will Build

A useful code migration tool should do more than paste files into a chatbot. It should identify eligible files, preserve project context, produce predictable changes, validate the result, and let a developer review every diff. In this tutorial, you will build a command-line workflow that uses Claude to migrate code, such as converting an older API, updating framework syntax, or replacing deprecated library calls.

59API is a practical low-cost gateway for this workflow. It provides pay-as-you-go access to native Claude Opus, Sonnet, Haiku, and Fable models, plus GPT models, through the API base URL https://api.59api.com. You can select a faster, less expensive model for routine files and reserve a stronger model for difficult transformations.

Step 1: Define a Narrow Migration Contract

Start with a migration contract instead of a vague instruction such as “modernize this project.” Write down the source pattern, target pattern, supported languages, excluded directories, and success criteria. For example: replace callbacks using the old client library with async calls from the new client, preserve public function signatures, and do not modify tests or generated files.

A narrow contract improves consistency and makes automated validation possible. Store it in a project file such as migration.yaml, alongside the target version and a list of allowed extensions.

Step 2: Collect Files and Context

Have the tool scan the repository while excluding directories such as .git, node_modules, vendor, build, and coverage. Filter by extensions and skip files above a configured size limit. For each candidate, collect the relative path, source text, nearby imports, and relevant configuration files.

Do not send the entire repository in every request. Group related files into batches that fit the selected model's context window. A useful first strategy is one source file per request, followed by a second pass for files that import the changed module. This keeps prompts smaller and makes failures easier to retry.

Step 3: Connect to Claude Through 59API

Use the OpenAI SDK if your tool is written in Python, JavaScript, or another supported language. Configure your 59API key from an environment variable and point the SDK at 59API's OpenAI-compatible endpoint. Depending on the SDK version, the base URL may be entered as https://api.59api.com or with its required /v1 path. Confirm the exact endpoint in your 59API account documentation.

Your first request should include the migration contract, the file path, and the source. Ask Claude to return a structured result containing status, transformed_code, explanation, warnings, and a list of assumptions. Tell it to return the original code unchanged when no migration is needed. Use a Claude Sonnet model for a balance of quality, speed, and cost; use Haiku for simple, repetitive substitutions and Opus for complex architectural changes.

Step 4: Design a Safer Prompt

Separate permanent instructions from file-specific data. The permanent instruction should say: preserve behavior, do not invent dependencies, do not change formatting unnecessarily, and return only the requested structured result. The file-specific section should include the path, language, migration rules, and source text.

Ask Claude to explain uncertain decisions rather than silently guessing. Also request a confidence value and a needs_review flag. These fields let your tool route risky files to a human instead of applying every answer automatically.

Step 5: Validate Before Writing

Parse the model response and reject malformed output. Check that transformed_code exists, that the result is valid text, and that the model did not return fenced code or commentary in the code field. Write proposed changes to a temporary directory first.

Run language-specific checks before replacing source files. Examples include a formatter, a parser, a type checker, unit tests, and a linter. For a JavaScript migration, run the project's formatter and test command. For Python, run the formatter, import or compile checks, and the relevant test suite. If validation fails, save the error output and optionally send one repair request containing the failed file and diagnostics.

Step 6: Apply Changes as Reviewable Patches

Never overwrite the repository without a dry-run option. Generate a Git diff for every proposed file and display a summary containing changed lines, warnings, validation results, estimated token usage, and the model used. Add flags such as --dry-run, --include, --exclude, --max-files, and --approve.

Only write files after explicit approval, then create a dedicated Git branch or commit. This gives developers a simple rollback path and makes the migration auditable.

Step 7: Control Cost and Improve Results

Cache results using a hash of the file content, migration contract, and model name. Do not pay to process unchanged files. Track input and output tokens, retry only transient failures, and use exponential backoff. A two-tier strategy works well: route straightforward files to low-cost Claude Haiku, then escalate low-confidence or failed files to Sonnet or Opus.

If you want inexpensive access to Claude while keeping native model quality, create a 59API account and test the workflow with a small directory first. Its pay-as-you-go pricing and referral rebate can make repeated migration runs more economical than committing to a large fixed plan.

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