Daniel sent us this one about pre-flight checks in agentic AI plugins. He's been building a lot of these, and he's noticed something interesting — there's a split between people cranking out lots of basic plugins and people hand-crafting two or three really polished, production-grade ones. And when you're in that second camp, pre-flight checks start to matter a lot.
Right, and his core question is when to use them and how to write them well. Because the obvious trap is you slap a pre-flight check on every single skill, and suddenly every invocation costs you an extra round trip of the agent checking whether a volume is mounted that's been mounted for six months.
Which is the tension. You want reliability, but you don't want to pay the latency tax on every single call when ninety-nine times out of a hundred everything's fine.
That latency tax isn't just annoying for the user — it compounds. If you've got an agent that chains five skills together and each one has an unnecessary pre-flight check, you've just added five round trips. On a slow model, that could be an extra ten or fifteen seconds of the user staring at a spinner.
Users staring at spinners is how you get support tickets. So the question becomes — how do you decide which skills actually justify that cost?
By the way — DeepSeek V four Pro is generating today's script, so if anything sounds unusually coherent, that's why.
I was going to say, you sound suspiciously well-organized today.
I'll take that as a compliment and move on. So, pre-flight checks. Daniel's characterization is basically right — before the agent commits to a course of action, it verifies that certain prerequisites are in place. Secrets are accessible, a volume is mounted, a required binary is on the path, whatever. But I think the more interesting question is what makes a good pre-flight check versus a bad one, and how you decide whether a given skill even needs one.
Let's start with the "when" question before we get to the "how." When does a skill cross the threshold where a pre-flight check makes sense?
I'd say there are three signals. One, the skill has external dependencies that can fail in ways the agent can't recover from mid-execution. Two, the cost of failure is high — either in tokens burned, time wasted, or actual damage done. And three, the failure mode is non-obvious. If the agent will immediately discover the problem on the first command and can report it clearly, you might not need a separate check.
That third one is subtle. Give me an example of an obvious failure mode that doesn't need a pre-flight check.
Say you've got a skill that renames files in a directory. If the directory doesn't exist, the first command fails instantly with "no such file or directory," the agent sees the error, and it can tell the user. That's basically free. Adding a pre-flight check that stats the directory first just duplicates what the OS is going to tell you anyway.
Whereas if you're about to run a database migration that takes forty minutes and requires three different environment variables to be set correctly, and one of them being wrong means you get a cryptic error at minute thirty-eight —
That's where you want the check. And here's the thing Daniel's absolutely right about — the miniaturization model matters. In a traditional CI pipeline, you'd have a YAML file with a pre-flight stage, and it'd run every time, and nobody cares because it's a machine running it. But with an agent, every check is tokens. Every check is a round trip to the model. You're spending intelligence on verification, and intelligence is the expensive resource.
The art is figuring out what's worth spending intelligence on. And I think that's actually a harder problem than it sounds, because the cost of intelligence isn't fixed. Different models have different pricing, different context windows, different latencies. What's a trivial check on a fast cheap model might be a real burden on a slower, more expensive one.
It means the "when" question might have different answers depending on your deployment context. If you're running against a local model with effectively zero marginal cost per token, maybe you're more liberal with pre-flight checks. If you're paying per token to a cloud API, you're going to be a lot more selective.
You're optimizing for different things — latency versus cost versus reliability — and the right balance shifts depending on what you're building and who's paying the bill.
And I think there's a useful framework here that I've seen in a few different plugin implementations. You categorize your checks into three buckets. Bucket one: static checks that can be cached. Bucket two: dynamic checks that need to run each time. Bucket three: checks you skip entirely because they're implicit in the skill's execution.
Walk me through bucket one — what's a static check that you can cache?
"Is this required binary installed?" On most systems, the answer doesn't change between invocations. You check once, you store the result, and you only re-check if the user updates their environment or if a certain amount of time has passed. Some of the Claude Code plugin patterns I've seen do this with a simple dotfile — they write the check result to a cache file with a timestamp, and if it's less than twenty-four hours old, they skip the check.
That's clever, but doesn't that introduce its own failure mode? The cache says the binary is there, but someone uninstalled it yesterday?
Sure, and that's the tradeoff. But here's the thing — the failure mode of a stale cache is that you skip a check you should have run, and then the skill fails on execution. That failure is usually fast and obvious. So you're trading a small chance of a fast, obvious failure for a guaranteed savings on every other invocation. That math works out in a lot of cases.
Unless the thing that changed is something where the failure isn't fast and obvious.
Right, and that loops back to the "when to use pre-flight checks" question. If the failure mode of a stale cache is catastrophic, don't cache that check. If it's just that the agent tries to run a command and gets an error, maybe the cache is fine.
I'm thinking of an example here. Say you're caching a check that a particular directory is writable. The cache says yes, so you skip the check. But between then and now, a disk filled up, or permissions changed, or the mount point disconnected. Now your skill tries to write a file and fails — but it fails silently, or it writes to a buffer that doesn't flush, or it corrupts something.
That's exactly the kind of scenario where you don't want to cache. Filesystem state is volatile enough that caching it for twenty-four hours is risky. Maybe you cache it for five minutes, or you tie the cache invalidation to specific events — like, if the user mentions they just changed permissions, you invalidate. But that requires the agent to be tracking that context, which is a whole other layer of complexity.
The caching strategy isn't just "cache everything that's static." You have to think about how static it actually is and what the consequences of staleness are.
So what about bucket two — the dynamic checks? What has to run every time?
Anything that's inherently stateful and volatile. "Is this API key still valid?" You can't cache that. "Is the remote server reachable?" Network topology can change between invocations. "Is there enough disk space for this operation?" That's a function of what else has happened on the system since the last check.
These are the expensive ones, right? Because they involve actual I/O.
They're expensive in terms of wall clock time, but interestingly, they're often cheap in tokens. The agent issues a command, gets a boolean or a status code back, and makes a decision. It's not burning a lot of context on analysis. The real token cost of pre-flight checks is when the check fails and the agent has to diagnose why and suggest remediation.
Which is actually where the value is, if you think about it. The whole point of a pre-flight check is to catch failures early and give the agent a chance to fix things or tell the user what's wrong, rather than barreling ahead and making a mess.
And this is where I see a lot of plugin authors get the implementation wrong. They write a pre-flight check that says "check failed" and then the agent just reports that to the user. That's barely better than letting the skill fail on its own. A good pre-flight check includes enough diagnostic information that the agent can actually do something with the failure.
Give me a concrete example of a well-written pre-flight check versus a lazy one.
Okay, lazy version: "Check if the AWS credentials are set. If not, fail with 'AWS credentials not found.'" Better version: "Check if AWS credentials are set. If not, check whether the AWS CLI is installed, whether a config file exists at the default path, whether any environment variables starting with AWS_ are present, and whether the user has run aws configure previously. Report all of that back so the agent can say 'It looks like you have the CLI installed but you've never run aws configure — would you like me to walk you through that?
That's a much richer interaction. And it's the difference between the agent being a gatekeeper and being a guide.
The pre-flight check isn't just a gate — it's a diagnostic probe.
And that's a much better way to think about it. You're not just saying "stop, you shall not pass." You're gathering intelligence about the environment so that if there's a problem, the agent has context for solving it.
This reminds me of something Daniel mentioned in his prompt — this bifurcation between people doing lots of basic plugins and people doing a few really curated ones. For the basic plugin author, you're probably not writing elaborate diagnostic pre-flight checks. You're doing the minimum to avoid catastrophic failures.
That's fine. If you're writing a plugin that fifty people might use, and the worst-case failure is that it doesn't work and the user tries something else, you don't need a sophisticated pre-flight system. The calculus changes when you're building something that's going to run in production, potentially unattended, where a failure has real consequences.
There's also an interesting question about where the pre-flight check lives. Is it part of the skill definition? Is it a separate hook that the agent framework calls? Because those have different implications for how the agent interacts with the check results.
This is where the hooks architecture Daniel alluded to gets interesting. In a lot of the agent frameworks I've looked at — and Claude Code has this pattern — you can register hooks at different points in the agent's lifecycle. Pre-execution, post-execution, on-error. A pre-flight check is essentially a pre-execution hook that has the ability to abort or modify the execution.
It's not baked into the skill itself. It's a separate thing that wraps the skill.
And that separation is important because it means the agent can decide whether to run the checks at all. If the user says "just do the thing, I know the environment is set up," the agent can skip the pre-flight and go straight to execution.
Which is another way to address the latency problem Daniel raised. If the user is confident, they can bypass the checks.
That requires the agent to expose that affordance to the user, and it requires the user to know enough to make that call. Most users won't. So the default should probably be to run the checks, and the optimization should be in making the checks fast and cacheable, not in asking the user whether they want to skip them.
Although I can imagine a hybrid approach where the agent tracks how often a given check actually catches a problem, and if it's been clean for the last hundred invocations, it starts asking the user "hey, this check hasn't found anything in a while — want me to skip it by default?
That's an interesting idea — adaptive pre-flight checks that learn from their own hit rate. But that's also adding state and complexity. You're now maintaining a database of check outcomes and making decisions based on historical data. For a production system, that might be worth it. For most plugins, probably overkill.
So let's talk about actually writing one. Daniel asked for practical guidance. If I'm building a plugin and I've decided this skill needs a pre-flight check, what does the implementation actually look like?
I'd structure it as a function that returns a structured result — not just pass or fail, but a data structure that the agent can reason about. Something like: here's what I checked, here's the result of each check, here's the severity of each failure, and here's suggested remediation for each failure.
Critical, warning, and info. Critical means the skill cannot run. Warning means it can run but might behave unexpectedly. Info is just context that might be useful. The agent can use those severity levels to decide whether to abort or proceed with caution.
A warning might be something like "you're running Python three point eight and this skill was tested on three point ten — it'll probably work, but some features might be missing"?
The skill can still run, the agent can still proceed, but the user should know there's a version mismatch. And if something goes wrong later, the agent can look back at that warning and say "this might be related to the Python version issue we flagged earlier.
The agent sees this structured output and decides what to do with it.
And this is where the intelligence of the model actually shines. You don't need to program every possible failure response. You just need to give the model good diagnostic data and trust it to figure out the right thing to say to the user. That's the whole promise of agentic AI — the model is the reasoning layer, and your job as the plugin author is to give it good inputs.
The pre-flight check is really an information-gathering step that feeds the model's reasoning, not a decision-making step in itself.
And that's a shift in thinking from traditional pre-flight checks in CI pipelines, where the check makes a binary decision and the pipeline stops or continues. Here, the check surfaces information, and the model makes the decision. The check is a sensor, not a gate.
Which means you should err on the side of providing more information rather than less. The model can always ignore irrelevant diagnostics, but it can't act on information it doesn't have.
And this connects to something I've been thinking about — the difference between validation and exploration. A traditional pre-flight check validates that the environment matches a known good state. An agentic pre-flight check can be more exploratory — "let me poke around and see what's available, and then I'll figure out what I can do with it.
That's a much more flexible model. But it also sounds more expensive in tokens.
It can be, if you're not careful. The key is to structure the exploration so it's targeted. Don't run "ls slash" and dump the whole filesystem into context. Run specific checks for specific dependencies and report only what's relevant.
What are the most common things people should be checking for in a pre-flight?
Based on what I've seen in the plugin ecosystem, the big ones are: authentication and secrets availability, required binaries on the path, network connectivity to required services, filesystem permissions for the directories you need to read from or write to, and version compatibility for key dependencies.
Version compatibility is an interesting one. How do you check that without getting into dependency hell?
You check the minimum viable version, not exact compatibility. "Is Python three point nine or later available?" Not "is Python exactly three point eleven point four with these specific patch versions?" The latter is a recipe for constant false negatives.
If you need a specific minor version because of a known bug in earlier versions?
Then you check for that specific constraint, but you document why. "Python three point eleven point two or later required due to CVE-whatever." That way, when someone's pre-flight fails and they're on three point eleven point one, they understand why and they know exactly what to upgrade to.
What about secrets? Daniel mentioned that specifically — making sure the agent has access to the secrets it needs.
This is tricky because you don't want to actually read the secrets into context. That's a security risk — secrets in agent context can leak. So your pre-flight check should verify that secrets exist and are accessible without actually retrieving their values. Check that the environment variable is set, not what it's set to. Check that the secret manager responds to a ping, don't fetch the actual secret.
Unless the secret's validity is what you're checking. Like, "is this API key still active?
Right, and that's a case where you have to make a call. Some APIs have a dedicated validation endpoint that doesn't consume quota and doesn't return sensitive data. If that exists, use it. If not, you might need to make a cheap API call that proves the key works without doing anything destructive. But you have to be careful — you don't want your pre-flight check to accidentally spin up a hundred dollar cloud instance just to verify credentials.
That would be a very expensive way to learn your key works.
I've seen it happen. Not in production, thankfully, but in testing. Someone wrote a pre-flight check that called "create instance" with a dry-run flag, except the API didn't actually support dry-run, and it created the instance. So, lesson learned — always test your pre-flight checks against a sandbox environment before you let them run against production.
That's a nightmare scenario. And it points to a broader principle — your pre-flight check should be side-effect-free. It should never mutate state. It should be a read-only operation.
And that's harder than it sounds with some APIs. "Read-only" isn't always clearly documented, and sometimes what looks like a read operation has side effects — logging, metrics, rate limiting, whatever. You really have to know the API surface you're checking against.
We've covered the "when" and the "how." Let's talk about the meta-question Daniel raised — this bifurcation in the plugin ecosystem. You've got people like Daniel who are building lots of plugins to cover a wide surface area, and then you've got people building a small number of highly polished, production-grade plugins. How does the pre-flight check strategy differ between these two approaches?
For the wide-surface-area approach, I think you want a standardized pre-flight template that you apply consistently across all your skills. You're not hand-crafting a bespoke check for each one — you're saying "every skill that touches the network gets this connectivity check, every skill that touches files gets this permissions check." It's a library of reusable check components that you compose per skill.
The curation approach?
For the curated, production-grade plugins, you're writing checks that are deeply specific to the domain. You're not just checking "can I reach the database" — you're checking "is the database schema at the expected version, are the indexes in place, is the connection pool configured correctly, are the query plans looking reasonable." You're encoding domain expertise into the pre-flight.
That sounds like a lot of work.
And that's why you only do it for the two or three skills that really matter. The ones where if they fail in production, it's a big deal. For everything else, the lightweight template approach is fine.
There's also a middle ground, I think. You start with the template approach, and over time, as you learn the common failure modes of a particular skill, you add specific checks for those. The pre-flight check evolves with your understanding of what breaks.
That's actually the most pragmatic approach for most teams. Don't try to anticipate every failure mode upfront — you'll over-engineer it and you'll probably guess wrong about what actually fails. Start minimal, monitor what breaks in practice, and add checks for those specific failures.
Which is basically the observability-driven development approach applied to agent plugins.
And that's a pattern I think we're going to see a lot more of as agentic AI matures. Right now, a lot of plugin development is still in the "ship it and see what happens" phase. As these things move into production, the feedback loop between observed failures and improved pre-flight checks is going to become a standard practice.
Let's talk about one more thing Daniel hinted at — the connection to existing development primitives. He mentioned that a lot of what we're seeing in agentic AI is just extensions of patterns that have been around forever. Pre-commit hooks, pre-deploy checks, CI pipeline stages. Pre-flight checks are basically that same idea, but now the consumer of the check result is an AI model instead of a build script.
And that changes the interface. A build script expects a zero or non-zero exit code. An AI model can handle a much richer response — natural language diagnostics, structured data, suggested remediation steps. So the pre-flight check can be more expressive.
There's a risk there too. If your pre-flight check is producing verbose output, it's consuming context window. Every token of diagnostic information is a token the model has to process and a token that's not available for the actual task.
Which is why structured, concise output matters. Don't write a paragraph when a JSON object will do. The model can parse structured data efficiently. It doesn't need natural language prose from a pre-flight check.
The output format matters. JSON or something similar, with clear field names and severity levels.
Consistent structure across all your checks, so the model develops a pattern for interpreting them. If every check returns a different format, the model has to figure out the schema each time, which wastes tokens and increases the chance of misinterpretation.
This feels like it's pointing toward a standardization opportunity. If the major agent frameworks agreed on a common pre-flight check interface, plugin authors could write checks once and have them work everywhere.
That would be nice, but I'm skeptical it'll happen soon. We're still in the fragmentation phase where every framework is figuring out its own patterns. Standardization usually comes later, once the winning patterns have emerged.
So for someone building today, the practical advice is: pick a format that works for your framework, be consistent, and don't over-engineer it.
Test your checks. Actually test them. Run them in environments where dependencies are missing, where permissions are wrong, where the network is flaky. Make sure the agent responds usefully to each failure mode.
That's the part I suspect most people skip. They write the check, they test the happy path where everything passes, and they ship it.
Guilty as charged, probably. But the value of a pre-flight check is almost entirely in the failure path. If everything's working, the check is just overhead. The only reason to have it is for when things aren't working. So you have to test the cases where things aren't working.
Document what the check actually checks. Because six months later, when someone else is using your plugin and the pre-flight fails, they need to understand what went wrong and how to fix it.
Documentation is the pre-flight check for the pre-flight check.
That's almost profound, in a deeply nerdy way.
I'll take it. So, to pull this together — Daniel asked when to use pre-flight checks and how to write them effectively. The "when" comes down to: does this skill have external dependencies that can fail in non-obvious ways, and is the cost of failure high enough to justify the overhead? For most basic skills, the answer is no, or at least keep it minimal. For production-grade skills that touch infrastructure, secrets, or critical data, the answer is probably yes.
The "how" is: write checks as diagnostic probes, not binary gates. Return structured, concise output with severity levels. Cache what you can, check dynamically what you must. Test the failure paths. And evolve your checks based on what actually breaks in practice.
One more thing I'd add: think about the user experience of a failed pre-flight check. If the agent says "pre-flight check failed: AWS credentials not found," that's not very helpful. If it says "I can't run this deployment because I don't have AWS credentials set up. Would you like me to help you configure them now?" — that's a completely different experience.
The pre-flight check is part of the user interface, not just a technical safeguard.
And that's the shift in thinking that agentic AI enables. The check isn't just preventing errors — it's creating opportunities for the agent to be helpful.
Alright, I think we've covered the territory. Should we bring it in for a landing?
Before we do, I want to mention one thing that's been rattling around in my head. There's a potential anti-pattern here that I think is worth flagging. It's tempting to use pre-flight checks as a substitute for proper error handling in the skill itself. "Oh, the pre-flight check will catch that, so I don't need to handle this edge case in the execution." That's a mistake. The pre-flight check is a best-effort early warning system. It can miss things. Your skill still needs to handle errors gracefully when they occur during execution.
The pre-flight check reduces the probability of failure, but it doesn't eliminate it. You still need defense in depth.
The pre-flight check is your first line of defense, not your only line.
I suppose there's also a risk of the pre-flight check becoming a crutch for poor skill design. If your skill is so fragile that it needs an elaborate pre-flight check to avoid catastrophe, maybe the skill itself needs to be more robust.
The best pre-flight check is the one you don't need because the skill handles failure gracefully on its own. But in the real world, with external dependencies and complex environments, that's not always achievable. So pre-flight checks fill the gap.
The hierarchy is: design skills to be robust and self-diagnosing, add pre-flight checks for the failure modes you can't handle gracefully during execution, and make those checks informative enough that the agent can actually help the user recover.
That's the framework. And I think that's a good place to wrap.
Before we do — and now, Hilbert's daily fun fact.
Hilbert: In ancient Iceland, prior to five hundred CE, early Norse settlers practiced a form of fish fermentation where Atlantic cod was buried in gravel above the high-tide line and left for up to twelve weeks, during which anaerobic bacteria converted trimethylamine oxide in the fish tissue into trimethylamine and dimethylamine, producing a compound with a pH of roughly nine point five — making it one of the few deliberately alkaline-preserved foods in human history.
Nine point five pH. soap-adjacent fish.
I have so many questions and I'm not sure I want any of them answered.
This has been My Weird Prompts. Thanks to our producer Hilbert Flumingtop. If you want more episodes, head over to myweirdprompts.
If you're building agent plugins, seriously — test your pre-flight checks on the failure paths. Your users will thank you.
See you next time.
