Review before applying
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
AI coding workspace
Dependency debugging
A version change should leave evidence. TechRetriever keeps the failing import, installed dependency and reproduction steps close enough to compare.
● ● ●TechRetriever●
A concrete starting point · Dependency debugging ×
node --version
npm ls example-library
# Match the installed version to its documented API.
# Reproduce the failing import in isolation.✳Investigate an import that broke after a dependency update.
✓ ChangesReview before applying ↗
Illustrative workspace
Connection
Base URL, credentials and a reachable server.
Project
Record the installed version, reproduce the error in a small example and inspect the documented API.
A concrete starting point
node --version
npm ls example-library
# Match the installed version to its documented API.
# Reproduce the failing import in isolation.Resolve dependency changes with evidence from your project
TechRetriever gives dependency maintenance a place in your Windows coding workflow. Open the project folder, describe the failure and ask the connected model to examine the files involved. Keep the actual error alongside the dependency declaration so the conversation starts from what your application is doing, rather than a remembered API.
A failing import after an upgrade is one useful example. Supply package.json, the installed version and a small reproduction, then ask whether the call matches that version. The aim is a reviewable application change with a reason behind it. TechRetriever does not supply a dependency repair engine or establish compatibility merely by suggesting an edit.
You choose the model connection through a supported OpenAI-compatible or Anthropic endpoint, providing credentials when required. An abliterated AI model may be an option at that endpoint; its restrictions, tool access and context capacity depend on the model and server. Check those capabilities before assigning a repository investigation.
Compare a proposed fix with the package documentation available to your project and run the failing import again. Then run the relevant application tests. Keep an intentional version change separate from unrelated upgrades, so a successful result answers the original dependency question and leaves a manageable diff.
Before accepting a dependency fix
Which version should the conversation use?
Use the version actually installed in the failing environment, together with the declared range and relevant lockfile entry. A package declaration alone may not identify the code being executed. Include the runtime version when it affects the failure.
Should I update every package while fixing an import?
Prefer a request with a defined compatibility target. A broad upgrade introduces other changes that can obscure the cause. Review any proposed version adjustment separately and check callers that depend on the affected package before accepting it.
What makes a reproduction useful here?
Show the smallest import and call that still produces the error, with expected and actual output. Keep the package and runtime versions attached to that example. The reproduction should exercise your failure without requiring unrelated application setup.
Does the workspace verify package compatibility for me?
Treat the model response as a proposal. Inspect the changed usage, rerun the reproduction and use your existing test commands. Available inspection tools depend on the configured endpoint; an explanation alone is not evidence that the dependency works.
TechRetriever · Updated 2026-10-25
