Why "GitHub Stars" Isn't a Criterion
A star measures past popularity, not present fit. A library might have accumulated thousands of stars during a hype spike and gone without relevant commits for a year; another might have few stars simply because it solves a niche problem — your problem, specifically. Neither case says much about whether it's the right choice for your project.
The same goes for similar metrics: weekly downloads, number of contributors, presence on "best libraries of the year" lists. These are signals of market traction, not technical fit. A popular library can have an API poorly designed for your use case; a little-known library can fit perfectly. Evaluating by popularity is delegating the decision to the consensus of people who don't know your project.
What solves this is a checklist of criteria verifiable from the library's own repository and documentation — not from third-party rankings.
The Checklist
Active maintenance. The commit and release history answers: does the library still get attention? The frequency of trivial commits (typos, formatting) doesn't matter; what matters is whether open issues get a response and whether releases fix reported bugs. A repository with open issues unanswered for months is a signal, regardless of how many stars it has.
API surface. How much of the library will you actually use? A small, direct API is easier to understand, test, and replace later. A large API, with many configuration options and several ways to do the same thing, costs more to learn and to maintain — every unused option is still surface that someone on the team can trip over.
Weight and bundle size. For code that runs in the browser, the library's size counts against the application's performance budget. It's worth checking the real size — bundled and compressed, not the source repository's size — and whether the library supports tree-shaking, that is, whether you can import just what's used without loading the rest.
License. MIT, Apache 2.0 and BSD cover most cases without friction. Copyleft licenses (GPL and variants) or custom terms can impose obligations on how your own code is distributed — it's worth confirming compatibility with the product's model before integrating, not after.
Lock-in. How much of your code will depend directly on this library's API? A library used behind your own abstraction layer is easier to swap out later; a library whose types and conventions spread across the entire codebase becomes a structural dependency — swapping it stops being a refactor and becomes a rewrite.
Community and issues. It's not about the size of the community, it's about what you can learn by reading the open and closed issues: what problems other people ran into, whether they're the same problems your project would have, and how — or whether — they were resolved. An old, unresolved issue about a behavior that matters for your case is a concrete data point, not a minor detail.
Migration curve. If the decision doesn't work out, what's the cost of leaving? Libraries that follow conventions already established in the language or ecosystem tend to be cheaper to swap out later. This isn't a reason to never adopt something more opinionated — it's information that feeds into the risk calculation before the decision, not after it.
From Checklist to Time-Boxed Spike
A checklist like this doesn't replace technical judgment; it organizes what needs to be verified before the judgment happens. And the right way to verify isn't to read about the library — it's to run a spike: a short period, defined in advance, dedicated to installing the library, integrating it into a real use case of the project (not the documentation's "hello world"), and confronting each checklist item with what actually happened in practice.
The time-box matters here as much as the checklist. Without a deadline, the investigation tends to stretch until you "feel confident," and there's rarely enough information to eliminate all uncertainty about a new library. With a half-day or one-day spike, the goal changes: it's not to reach absolute certainty, it's to gather enough evidence on the checklist items to decide — or to know exactly which question still needs answering.
The result of a spike like this isn't "I liked it" or "I didn't like it." It's a short list: what was verified, what each checklist item showed, and a recommendation. That artifact is what becomes a recorded decision — and what anyone else on the team can review without repeating the work.
Where SpikeMe Fits In
After running that spike, SpikeMe generates the structured document — criteria compared side by side, a recommendation and a next-steps plan — from your project's package.json and the topic of the decision. To generate one from your stack, see spikeme.io.