Dependency Upgrade Assistant
Both tools can search for package usage across a codebase. Copilot has direct access to package changelogs via GitHub. Cursor's multi-file editing handles the actual code changes. The confidence rating prevents silent regressions.
Help me upgrade {{package_name}} from v{{current_version}} to v{{target_version}}. Steps: 1. **Changelog analysis**: Summarize breaking changes between {{current_version}} and {{target_version}}. Focus on changes that affect the APIs we actually use 2. **Impact scan**: Search the codebase for all imports from {{package_name}} and list how each is affected 3. **Migration plan**: For each breaking change, show: - Before (our current code) - After (updated code) - Confidence: High (documented migration) / Medium (inferred) / Low (needs manual verification) 4. **Apply changes**: Update the code, starting with the lowest-risk changes 5. **Verify**: Run {{test_command}} and {{build_command}} after changes Additional checks: - Are there peer dependency conflicts with our other packages? - Do any of our other dependencies also need updating for compatibility? - Are there new features in {{target_version}} we should adopt? (list but don't implement without asking) If the upgrade path requires an intermediate version (e.g., v2 → v3 → v4), note that and provide the stepped plan.
Variables to customize
Why this prompt works
Both tools can search for package usage across a codebase. Copilot has direct access to package changelogs via GitHub. Cursor's multi-file editing handles the actual code changes. The confidence rating prevents silent regressions.
Save this prompt to your library
Organize, version, and access your best prompts across ChatGPT, Claude, and Cursor.
Related prompts
Get thorough code reviews with actionable feedback tailored to your language, framework, and standards.
Context-Aware Code CompletionProviding the surrounding code and project context lets the model match existing patterns exactly. The constraint against modifying existing code prevents unwanted side effects.
Inline Code SuggestionConstraining suggestions to match existing style and scope produces insertions that feel native to the codebase. The 'no explanation' rule mimics real inline completion behavior.
Code ExplanationThe audience level parameter adjusts complexity automatically. Requiring a usage example ensures the explanation is practical, not just theoretical.