Vet an open-source library before adding it
Evaluates a dependency for maintenance health, security, license fit, and lock-in so you can decide whether to adopt it.
The prompt
Help me decide whether to add an open-source library to our codebase.
Library and version: [NAME, VERSION, LINK]
What I need it for: [THE PROBLEM IT SOLVES]
Alternatives: [OTHER LIBRARIES, OR "WRITE IT OURSELVES"]
Our product and licensing context: [e.g. "closed-source SaaS", "distributed mobile app", "open-source under MIT"]
What I've found so far: [STARS, LAST RELEASE, OPEN ISSUES, CONTRIBUTORS, DOWNLOADS, KNOWN CVEs - paste what you checked]
Evaluate against:
1. Fit: does it solve my problem without pulling in far more than I need? Bundle or binary size impact.
2. Maintenance health: release cadence, number of active maintainers (bus factor), issue and PR responsiveness, funding or company backing.
3. Security: known vulnerabilities, how they've been handled historically, transitive dependency count, install scripts, and supply-chain red flags.
4. License: compatibility with how we distribute our software. Flag copyleft obligations (GPL, AGPL, LGPL) and anything unusual. This is general information; confirm with legal for anything unclear.
5. Lock-in: how hard it would be to replace, and whether we should wrap it behind our own interface.
6. Quality: docs, types, test coverage, API stability.
Use only the facts I provided; for anything you can't verify, list it under "Check before deciding" with how to check it (e.g. registry pages, vulnerability databases, the repo's commit history).
Return a scorecard table (criterion, finding, rating: good/concern/blocker), a recommendation (adopt / adopt with a wrapper / avoid / build it ourselves), and the reasoning in 5 sentences.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Software Development prompts
What to fill in
- NAME, VERSION, LINK
- THE PROBLEM IT SOLVES
- OTHER LIBRARIES, OR "WRITE IT OURSELVES"
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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