You need a CMS for your project. You find three promising options on GitHub, all with 10k+ stars. You have 15 minutes before your next meeting to make a recommendation. How do you evaluate which one is production-ready? Do you clone all three repos and read the code? Check the commit history going back months? Read every open issue?
No. You follow a checklist that takes five minutes per repository and reveals whether maintainers built something solid or something that looks good from a distance. Stars don’t indicate whether the project is maintained. Forks don’t tell you whether it’s well architected. The README, directory structure, test setup, and recent activity tell you everything you need to know about whether this software will work in production.
Most developers skip this evaluation and regret it six months later, when they discover the “active” project hasn’t had a commit in four months, unanswered issues are piling up, and the missing feature they need is trapped behind architectural decisions that can’t be changed without rewriting core code. Five minutes of focused evaluation prevents months of technical debt. Here’s the checklist that actually works.
The Five-Minute Evaluation Framework
Minutes 1-2: README Analysis. The README is where maintainers show whether they care about developer experience. A well-crafted README means maintainers think about users. A sloppy README means they don’t.
Minute 3: Project Structure. Directory organization reveals whether the codebase is maintainable. Good structure means someone thought about how developers would navigate and contribute.
Minute 4: Tests and CI. Test presence and CI configuration indicate whether the team values quality. No tests means no confidence in changes. No CI means manual processes that don’t scale.
Minute 5: Activity and Community. Recent commits, issue responses, and PR merges indicate whether the project is active. A repo can have 50,000 stars and still be effectively dead if no one maintains it.
This framework works because it focuses on signals that correlate with production readiness. Code quality, maintenance commitment, and community health all surface through these indicators. Let’s break down what to look for in each minute.
First Stop: The README
Open the README and spend two minutes evaluating it. The first paragraph should clearly state what the project does and why it exists. If you finish the opening and still don’t understand the use case, that’s a red flag. Good projects front-load clarity because they respect your time.
Look for installation instructions within the first three scrolls. Commands should be copy-pasteable. Ghost provides Docker commands, npm install steps, and links to one-click deployments. You know in 30 seconds how to get started. Compare that to READMEs that say “installation is straightforward” without showing actual commands. Vague promises mean documentation debt.
Check for badges at the top—build status, test coverage, version number. Mattermost shows green CI badges, npm version, and test coverage percentage. Those badges link to the actual systems that run checks. If badges exist but fail, that’s worse than no badges—it means the team knows their builds are broken and hasn’t fixed them. If builds have been failing for weeks, the project is effectively unmaintained.
Scroll to the configuration section. Production-ready projects explain how to customize behavior through environment variables, config files, or admin panels. Strapi documents its database configuration, admin credentials, and plugin system. You see immediately what you can control. If a configuration option is mentioned but not documented, expect to read the source code to determine the options.
Look for the license. No license or an unclear license is a legal risk. Most production projects use MIT, Apache 2.0, or GPL variants. The license should be stated in the README and present as a LICENSE file in the root. If you have to guess the license by reading the code or searching issues, walk away—you can’t legally use unlicensed code.
Finally, check the version number or release information. Active projects prominently display their current version and link to release notes. If the README references version 2.x but the latest release is 1.5 from two years ago, the documentation is stale, which suggests everything else might be too.
Project Structure Reveals Maintainability
Directory organization takes 60 seconds to evaluate, but reveals whether the codebase is navigable. Open the repository root and count the number of files and directories. Fewer than 20 items is reasonable. More than 50 items means everything lives in the root, which indicates the project grew organically without refactoring.
Look for standard directories: src/ or lib/ for source code, tests/ or test/ for test files, docs/ for documentation, config/ or .config/ for configuration. Directus organizes its monorepo with packages/ containing separate modules, tests/ for test suites, and docs/ for documentation. You understand the structure immediately.
Check the source directory. Is code separated by feature, by layer, or dumped together? Feature-based organization (like src/auth/, src/api/, src/database/) indicates modular design. Layer-based organization (like src/controllers/, src/models/, src/routes/) indicates MVC thinking. Both work. What doesn’t work is 200 files in src/ with no subdirectories, or subdirectories named stuff/, helpers/, utils/ where the boundaries aren’t clear.
Look at the test organization. Tests should mirror the source structure or live in a parallel directory tree. If source files and test files are interleaved without a pattern, contributing tests become confusing. If there are no test files, you know quality assurance is manual at best and nonexistent at worst.
Configuration files at the root should be well-named and minimal. Package managers (package.json, Cargo.toml, pyproject.toml), linters (eslintrc, .prettierrc), CI configs (.github/workflows/), Docker files, and maybe a Makefile. If you see 15+ config files, the tooling stack is complex. Not necessarily bad, but be prepared for that complexity when you deploy.
Tests and CI: Non-Negotiable Quality Signals
Navigate to the tests directory. Does it exist? Are there test files? Payload CMS maintains comprehensive test coverage across unit, integration, and end-to-end tests. You see tests for core features, edge cases, and error handling. That coverage means changes are verified automatically rather than manually tested each time.
Check the test-to-source ratio. If the project has 500 files and 20 test files, coverage is thin. If it has 500 files and 200 test files, someone invested in quality. The ratio doesn’t need to be 1:1, but test presence correlates with production readiness. Projects with 10% or more of their files dedicated to tests generally maintain higher quality.
Look for the CI configuration. Check .github/workflows/ for GitHub Actions or .gitlab-ci.yml for GitLab CI. Mattermost runs automated tests, linting, and build verification on every pull request. That CI pipeline catches issues before they reach the main branch. If CI exists but only runs on merge (not on PRs), quality gates are weaker.
Examine what CI actually does. Good pipelines run tests, lint code, check types, verify builds, and sometimes run security scans. Minimal pipelines just check if the code compiles. The more checks in CI, the more confidence you have that changes won’t break production. If CI is configured but disabled (common when tests are flaky), the project’s quality infrastructure isn’t working.
Check for test coverage reporting. Some projects display coverage percentages on badges or in CI output. Coverage above 50% is decent, above 70% is strong, and above 90% is exceptional. Low coverage isn’t disqualifying—some projects test critical paths intensely rather than aiming for percentage targets—but knowing coverage helps you assess risk.
Activity and Community Health
Check the last commit date. Click on the commit history. If the most recent commit is within the past week, the project is actively maintained. Within the past month is acceptable. Beyond three months raises questions—is this an intentional pause, or has development stopped? Beyond six months means the project is effectively abandoned unless it’s explicitly in maintenance mode.
Look at commit frequency over time. Click “Insights” then “Commit activity” (or check the graph on the main page). Ghost shows consistent weekly commits across months. Spiky activity—bursts followed by silence—suggests either a small team with time constraints or a project that gets attention when fires need extinguishing rather than steady maintenance.
Check recent releases under the “Releases” tab. Production projects ship versions regularly—monthly, quarterly, or upon completion of major features. Strapi adheres to semantic versioning and maintains detailed changelogs for each release. You see what changed, what broke, and what’s new. If the last release was a year ago but commits are recent, version management is informal, which makes upgrades unpredictable.
Open the Issues tab and sort by recent activity. Pick 5-10 recent issues and check response times. Are maintainers responding within days? Are issues being triaged (labeled, assigned)? Are questions being answered? Projects with hundreds of open issues signal that the maintainers are overwhelmed or have moved on. Projects where every issue gets a response within 48 hours signal active maintainers who care about their users.
Check pull requests the same way. Are PRs being reviewed and merged? Or are they sitting open for months? How many PRs are open versus closed? A healthy ratio might be 10-20 open PRs with hundreds closed. An unhealthy ratio is 200 open PRs to 50 closed. That backlog means contributions aren’t being accepted, which kills community momentum.
Look for community links—Discord servers, Slack workspaces, forums. Active projects link to these spaces in the README. The presence of community infrastructure indicates the project supports users beyond GitHub issues. Visit these spaces briefly if you have time—active conversations mean active users.
Red Flags That Disqualify Immediately
Some signals mean walk away, regardless of how promising the project looks otherwise.
No license or ambiguous licensing: You can’t legally use code without a clear license. If the LICENSE file is missing or the README doesn’t state the license, you’re taking a legal risk. Corporate legal teams will block adoption.
Abandoned by maintainers: No commits in six months on a project that positions itself as “active” means it’s not active. Check whether a fork has taken over maintenance; otherwise, avoid building on abandoned foundations.
Security vulnerabilities unaddressed: GitHub displays security alerts on repositories with known vulnerable dependencies. If alerts exist and haven’t been addressed in months, security isn’t a priority. That’s disqualifying for production use.
Hardcoded credentials: Search for “password”, “api_key”, “secret” in the code. If you find hardcoded credentials (even example credentials that look like they might be real), the project doesn’t follow security basics. Real projects use environment variables or config files that aren’t committed.
Issues and PRs ignored: If recent issues receive no responses and PRs remain unaddressed for months, the maintainers have checked out. The community is talking to a wall. Don’t add your effort to that wall.
No releases or version tags: How do you know what’s stable? Projects without version tags or releases make it impossible to pin dependencies. You’re left tracking commit SHAs, which makes updates unpredictable and rollbacks difficult.
Build failing indefinitely: If the CI badge shows red and the build has been failing for weeks, the team either doesn’t notice or doesn’t care. Either way, the development process is broken.
Green Flags That Build Confidence
Some signals indicate that the project is not only functional but also exceptionally well maintained.
Semantic versioning with changelogs: Version numbers that follow Semantic Versioning (major.minor.patch) and detailed changelogs for each release indicate that the team manages breaking changes carefully. Directus documents every release, including added features, fixed bugs, and highlighted breaking changes. You know exactly what changed between versions.
Multiple active maintainers: Check “Insights” > “Contributors” to see how many people commit regularly. Projects with 5+ active contributors have lower bus factor risk. If one maintainer leaves, the project continues. Single-maintainer projects are vulnerable to life changes.
CONTRIBUTING.md and CODE_OF_CONDUCT.md: These files indicate that the project welcomes contributors and has considered community dynamics. CONTRIBUTING.md explains how to submit PRs, run tests, and follow conventions. Its presence means the team wants help.
Sponsors or funding: Check for GitHub Sponsors, Open Collective, or Patreon links. Funded projects have resources to pay maintainers, which improves sustainability. Sponsorship indicates that users value the project enough to fund it.
Production use cases documented: READMEs that list companies using the software in production (with logos or names) provide social proof. Mattermost lists enterprises, government agencies, and open source communities running it. That adoption reduces your risk—you’re not the first one betting on this tool.
Docker and Kubernetes configurations: Prebuilt Docker images and example Kubernetes manifests indicate that the project prioritizes deployment ease. If docker-compose.yml exists and works, setup complexity drops dramatically.
Security policy (SECURITY.md): Describes how to report vulnerabilities and how the team handles security issues. Its presence indicates that security is taken seriously and that there’s a process for responsible disclosure.
Automated dependency updates: Dependabot or Renovate configurations that keep dependencies up to date demonstrate that the team maintains technical hygiene. Stale dependencies create security risks and friction during upgrades.
Putting It Into Practice: A Real Evaluation
Let’s walk through a five-minute evaluation of Strapi as if we’d just discovered it.
Minute 1-2: README First paragraph: “Design APIs fast, manage content easily.” Clear use case—headless CMS. Docker command in the Quick Start: npx create-strapi-app@latest my-project --quickstart. Installation is one line. Badges show CI passing, npm version 4.x, and test coverage. License is MIT, stated clearly. The configuration section explains database options and environment variables. README quality is strong.
Minute 3: Structure Root directory has packages/ (monorepo), examples/, docs/, and standard config files. Inside packages/, code is organized by feature: core/admin/, core/content-manager/, core/strapi/. Tests live alongside the source in __tests__ directories. Structure is clean and modular. Navigation is intuitive.
Minute 4: Tests & CI Tests exist throughout the monorepo. .github/workflows/ contains multiple CI pipelines—tests, linting, and build verification. CI runs on every PR. Test coverage is visible in CI output. Quality infrastructure is comprehensive.
Minute 5: Activity. Last commit: 2 days ago. The commit graph shows daily activity. Latest release: two weeks ago with a detailed changelog. Recent issues get responses within 24-48 hours. PRs are reviewed and merged regularly. Community Discord linked in README. The project is actively maintained.
Verdict: Production-ready. Scores well on all signals. Would deploy in production with confidence.
Compare that to a hypothetical project with 15k stars, no commits in three months, 500 open issues with no maintainer responses, a README that says “docs coming soon,” and no test directory. The five-minute check reveals it’s abandoned despite popularity.
The Evaluation Habit
This checklist becomes automatic after you’ve used it a few times. You develop an instinct for what production-ready looks like. You recognize patterns that indicate quality versus patterns that indicate tech debt. The five minutes you invest upfront save you hours spent troubleshooting projects that aren’t maintained, architectures that don’t scale, or codebases with no tests.
Use this checklist before adding any dependency. Use it when evaluating alternatives. Use it when someone recommends a tool based solely on GitHub stars. Stars measure popularity. This checklist measures production readiness. They’re not the same thing, and knowing the difference prevents bad decisions.