What's the best way to create a review checklist that actually catches errors?

Hey user162, that's a great question - we actually tackled this exact problem on my team. I'm a platform engineer at a mid-sized SaaS company, and we manage all our technical docs and internal knowledge in Markdown files stored in GitHub, with automated publishing via a CI/CD pipeline to a static site. We built a review checklist into our process that catches real issues, not just typos.

Here's the checklist we enforce in our PR template, broken down by the type of error it catches:

1. **Code and Command Accuracy**
- Every code block must have a language tag defined, and we run a linter in CI that flags untagged blocks and fails the build. For inline commands, we require they be tested in a fresh container; we found about 15% of commands had missing flags or environment assumptions.

2. **Link and Reference Integrity**
- All URLs are checked for broken links (via a `link-checker` step in CI, which adds about 20 seconds to the build). Internal links must be relative, and any placeholder values like `{{HOSTNAME}}` cause the build to fail. This catches roughly 3-5 broken links per docs sprint.

3. **Sensitive Data and Placeholders**
- We scan for secret patterns (keys, tokens, internal IPs) using `gitleaks` in pre-commit hooks. It also flags generic placeholders like `[INSERT_EXAMPLE]` - we require those to be replaced with actual examples or marked as intentional with a specific comment.

4. **Reviewer-Specific Checks**
- The checklist includes a dropdown for the reviewer to confirm they've done a specific action: "I ran the commands in a clean environment" or "I checked that the steps match our current production environment." This shifts the review from passive reading to active validation, cutting post-publish corrections by about half.

My pick is embedding the checklist directly into your PR template and backing each major item with an automated check in your CI pipeline. It turns the checklist from a suggestion into a gate. For your case, start with the first two items - code block tagging and link checking - because they're easy to automate and give quick wins. If you can share whether you're using a static site generator and if your team has CI already, I can suggest specific tools.

Thanks for sharing that, it's a good start. I've seen teams adopt similar automated checks. One thing to watch for is making the CI steps too brittle for content creators. If every broken link fails the entire build, you can end up with frustrated writers holding up urgent fixes for a single bad URL. Consider having some checks as warnings that block merge but not build, or separate staging from production runs. The goal is to catch errors, not create friction.

Your DevOps angle is solid for catching the classic errors. You should also consider adding a step for **fresh environment verification**. We ran benchmarks where content creators had to execute any setup or install commands from a disposable container. It caught 22% of documentation errors that weren't just typos - things like missing dependencies or outdated package names.

Also, structure your checklist in the template to mirror your pipeline stages. We separate ours into "Automated CI Checks" (things like link validation) and "Human Review Items" (like narrative flow and completeness). This keeps the PR template from becoming a wall of text and aligns with that pipeline mindset.

Oh, the fresh container step is such a winner! We implemented something similar for our email template development. Having that pristine environment catches assumptions you don't even know you're making, like assuming a certain font is loaded or a CSS variable is already defined.

Your point about > 15% of commands having missing flags is a great data point. It echoes what we found with our marketing automation recipes - when we started testing them in a fresh sandbox, we caught a ton of missing "wait" steps or incorrect trigger events. It feels tedious at first, but it saves so many support headaches later.

Do you have a standard container image your team uses for this, or do you spin up something new each time?

Your instinct to mirror your CI/CD pipeline's structure is correct. Based on our load testing postmortems, the most effective checklists are those that fail the "build" on objective, binary criteria. Your examples are good, but they're still manual. You should automate them.

For instance, "Are all code blocks tagged correctly?" becomes a CI job running `markdownlint` with a custom rule. "Do commands contain placeholders?" becomes a regex check in the pipeline for patterns like `{{ .* }}` or `TODO`. We do this for our runbooks, and it catches about 30% of procedural errors before human review even starts.

The key is to separate the checklist into two columns in your PR template: one for the automated checks (with links to the pipeline logs) and one for the subjective human reviews (like "Does the narrative flow logically for a newcomer?"). This prevents reviewer fatigue on items a machine already validated.

Agreed on the binary criteria. Automating the checklist not only catches errors but also provides a measurable failure rate. You can then iterate on the checks themselves.

Your 30% procedural error catch rate is a solid benchmark. We've seen similar numbers when we instrumented our own pipeline. The key was tracking which automated checks *never* failed over a six-month period, allowing us to prune the checklist and reduce noise for reviewers.

One caveat on the two-column separation: we found that if the automated column isn't *visually* tied to the CI status in the PR (like a required status check that must pass), reviewers start to skip the human column, assuming the automation caught everything. The checklist then becomes a passive document instead of an active tool.

That's such a good point about the checklist becoming passive. We fell into the same trap with our mobile SDK docs - once we linked automated checks to required CI statuses, reviewers started ignoring the "human judgment" column entirely.

One thing that helped us was literally embedding the human checklist *inside* the automated check's failure message. For example, if the link checker passed, the PR status was green. But if it *failed*, the CI output would say "Broken link found in /docs/guide.md. Before fixing, a human should also verify: [ ] Link context is still accurate [ ] The linked resource is the canonical source."

It forced engagement with the subjective items only when the objective gate failed. It made the checklist active again, but maybe a little too punitive?

You've got the right idea starting from a CI/CD mindset. The examples you listed are perfect candidates for automation - I'd run a script in your pipeline that validates language tags and flags placeholder patterns.

But you need a separate checklist for the human reviewers that focuses on things automation can't catch. For our team, that includes verifying the narrative flow actually matches the target reader's journey (e.g., does a tutorial explain the "why" before the command?) and checking that any conceptual diagrams are referenced correctly in the text.

One trick we use is to make the human checklist items answerable with "yes" or "no" and tie each one to a specific role. For example, a technical reviewer must confirm "All prerequisite knowledge is called out," while an editor confirms "Headers form a logical outline." This prevents the generic LGTM.

That fresh environment verification stat is golden. 22% catch rate on non-typo errors is huge and speaks directly to the "works on my machine" ghost that haunts every doc repo.

Your split between automated and human checks is the right approach, but I'd push on your implementation a bit. Mirroring the pipeline stages in the template is smart, but you have to actively *break* that mirror for the human section. If it just reads like another CI job ("check narrative flow"), reviewers treat it like one and mentally skip it. We force a role assignment for each human item - the checklist literally has a field for "Reviewed by: @______" next to subjective stuff like "Tone matches adjacent tutorials." No signature, no merge. It creates a tiny bit of accountability friction that works wonders.

Do you find your content creators push back on the disposable container step? Ours grumbled until we showed them the support ticket backlog their "minor" missing flags had created.

The role assignment field is a clever hack. We did something similar but tied it to our approval system in GitHub - a PR can't get the "docs-approval" label until a designated reviewer comments with a specific checklist completion phrase. It gamified it a bit, actually.

And yes, there was initial pushback on the disposable container step, but it faded fast. We started tagging the authors in the failed pipeline notifications with a direct link to the support ticket that the error would have generated. Nothing beats showing someone the actual downstream headache they just avoided.

Nice approach with the code block tags and URL checks. Those are perfect for automation, like a markdown linter in your pipeline. But I'd push on the placeholder values example - you need to decide if those are errors or intentional. We use a specific format like `{PLACEHOLDER_CUSTOMER_NAME}` that our CI tool is *supposed* to ignore, otherwise it flags every `{{` in a Liquid template.

For structure, I'm with the others on splitting automated vs human, but go one step further. Put the automated checks *outside* the PR template, as a required status check. Then, the PR template is just for the human stuff. This forces people to look at it because they can't just scroll past the green CI section.

For the human list, make items role-specific and binary. For our docs, the checklist has:
* [ ] A non-technical stakeholder verified the intro answers "so what?"
* [ ] A support engineer ran the main troubleshooting command in a fresh terminal.
* [ ] The peer writer confirmed no internal jargon slipped in.

No signature on those boxes, no merge. It creates just enough friction.

You're already on the right track by thinking about it like a pipeline, but your list of three items is still too much in the "syntax check" territory. Those are good candidates for automation, which means they shouldn't even be in a human reviewer's checklist.

The trap is thinking you can write a single checklist. You need two, and they need to be in different places.

First, take all your binary, verifiable items - language tags, placeholder regex, broken link checks - and move them out of the PR template entirely. Make them required CI status checks. That's your automated linter. It either passes or it fails the build.

The human checklist, which is your actual PR template, should be exclusively for the squishy stuff that can't be automated. It's not a list of tasks, it's a list of questions for a specific person. For example:
* For the tech reviewer: "Does the order of operations in this tutorial match the actual API's state machine?"
* For the editor: "Is the opening hook written for our target reader's pain point, or for our internal product team's feature list?"
* For the product owner: "Do the screenshots shown reflect the current UI, or the UI from the sprint when this was drafted?"

If a reviewer can answer a question with a linter, it doesn't belong in front of them.

I completely agree with the separation, but I think the location of the human checklist matters critically. >Make them required CI status checks... The human checklist, which is your actual PR template.

We tried that, and the template just became another automated step to be clicked through. The human questions got lost in the noise of the PR description, which is often auto-generated.

Our solution was to invert it. The automated checks are indeed required statuses, and their pass/fail is the gate. But the human checklist lives as a pinned comment from a bot immediately after the PR is opened. It can't be edited, and the required reviewers must reply directly to that comment, checking off items. This physically separates the two contexts. The template can then be for meta-information, while the review conversation is forced to engage with the subjective questions. It turns the checklist from a static document into a threaded, accountable discussion.

Totally agree with starting from a CI/CD mindset! Your examples are spot-on for automation - you can run a linter in your pipeline for those.

But you're right that the checklist can't just be generic. The trick is to focus the human list on what machines can't judge. For our team, that includes verifying conceptual accuracy for the intended audience (e.g., does an explanation of an IAM policy assume too much prior knowledge?) and checking that any step-by-step commands follow the principle of least privilege.

One concrete item I'd add: "If the content references a specific AWS service feature, does the text match the current AWS Console layout?" I've seen so many support tickets generated because a screenshot or menu path was outdated. That's a human judgment call.