Boost CI: Fast Schema Validation & Prompt Precedence Tests

Hey guys, let's talk about something super important for any robust development pipeline: CI workflow optimization. We're always striving to make our continuous integration systems smarter, faster, and more reliable, right? Nobody wants to deal with silent failures or last-minute surprises when a new release is just around the corner. That's why we're diving deep into some crucial updates for our CI to ensure our LexRunner tool is always consuming Lex canon assets correctly and, critically, honoring the prompt precedence. Think of it like a superhero gaining a new, more sensitive spider-sense – we want our CI to feel when something's off way before it becomes a major problem. These enhancements are all about reducing release risk for 0.5.0 by implementing quick CI smoke checks and dedicated workflow steps. This means catching missing package pins, misresolved prompts, or any other funky configuration issues early, saving us a ton of headaches, wasted time, and potential late-night debugging sessions. It’s about building a solid foundation, ensuring that every piece of our intricate system, especially how Lex assets are handled and prioritized, is working in perfect harmony, giving us confidence with every commit. We're talking about a significant leap in preventing those frustrating hidden issues from ever reaching production, safeguarding our code and, ultimately, our users' experience. This proactive approach to schema validation and prompt precedence testing is a game-changer for maintaining stability and accelerating our development cycles. It's truly about working smarter, not just harder.

Diving Deep: Understanding the Problem (Define Phase)

Alright, let's get real about the core problem we're tackling, guys. Our existing CI, while good, has a sneaky vulnerability: it can silently drift. Imagine a ship slowly veering off course without anyone noticing until it's almost too late – that's what happens when critical dependencies like @smartergpt/lex go missing or our crucial precedence logic breaks without immediate feedback. This silent drift means that issues might not surface until late failures in integration testing, or worse, in production, which, let's be honest, is a nightmare scenario. We’re talking about situations where LexRunner might be working with outdated or incorrectly resolved prompts, leading to unexpected behavior, or even critical system malfunctions. The problem isn't just a technical glitch; it's a release risk. If LexRunner isn't consuming Lex canon assets as expected, or if our carefully defined prompt hierarchy isn't respected, the downstream implications can be significant, affecting everything from user interactions to core system responses. That's why our primary deliverable here is to build robust CI steps that assert package presence, ensuring @smartergpt/lex is always there and correctly pinned. Furthermore, these steps will run a smoke loader test, a super quick check to confirm basic functionality, and crucially, they'll print precedence diagnostics. This means we’ll get immediate, clear feedback on how prompts are being resolved, giving us crystal-clear visibility into our Lex asset handling. This isn't just about fixing a bug; it's about fundamentally strengthening our CI pipeline to be self-aware and proactive, significantly reducing the chances of any nasty surprises down the line, and ensuring our schema validation and prompt resolution are always on point. Our goal is to shift from reactive fixes to proactive prevention, making our development process smoother and more reliable than ever before.

Measuring Success: How We'll Know We're Winning (Measure Phase)

So, how do we know if all this effort is actually paying off, folks? Right now, our current state in CI has a significant blind spot: there's no fast-fail smoke step. This means if something fundamental breaks with how LexRunner interacts with our Lex canon assets, we're often left dealing with late failures in integration, or even worse, during the final stages of release, which is incredibly frustrating and costly. Imagine spending hours debugging a complex integration failure only to discover the root cause was a simple misconfiguration or a missing dependency that could have been caught in seconds. That's the pain point we're targeting. Our success metric is clear, quantifiable, and awesome: we want our smoke test to run in under 2 minutes and, most importantly, to fail early on configuration issues. This isn't just about speed; it's about value. Catching issues like incorrect package pins, problems with schema validation, or hiccups in prompt precedence within a couple of minutes after a commit means we identify and fix problems when they're small and cheap, not large and expensive. We're talking about instant feedback that tells us if @smartergpt/lex is correctly resolved, if our canon/prompts are accessible, and if our precedence rules are being honored. This rapid feedback loop is invaluable for developers, allowing them to iterate faster and with greater confidence. It transforms our CI from a passive checker into an active guardian, constantly ensuring the integrity of our core components and the correct behavior of our LexRunner. This proactive approach not only significantly boosts our development velocity but also drastically reduces the stress and uncertainty associated with releases, making our entire team more productive and our software more robust. We're setting ourselves up for a future where potential issues are flagged almost instantly, making our CI an indispensable ally in quality assurance.

The Game Plan: Analyzing and Improving Our CI Workflow (Analyze & Improve Phases)

Alright team, let’s talk strategy! Our mission here is to shore up our CI, making it robust against those pesky silent drifts we discussed. When we analyze what needs a tweak, we're looking primarily at a few key files and processes. First off, .github/workflows/lint-test.yml (or whichever CI workflow is responsible for our core tests) is going to be our main target for modification. This is where we’ll embed our new fast-fail checks. Next, we’ll be adding a brand-new script, scripts/ci-smoke.mjs, which will be the brains behind our quick diagnostic checks. Finally, our package.json needs a minor update to expose a new ci-smoke script, making it easily callable within our CI environment. On a cross-repo note, it’s also crucial to double-check and ensure that the pinned @smartergpt/lex version is not only present but also the acceptable and expected version across all related projects. This proactive check prevents version mismatches that could lead to subtle but frustrating bugs. This implementation checklist guides us through getting our hands dirty and actually making these improvements. First, we'll add scripts/ci-smoke.mjs. This Node.js script (running on Node 20, by the way, because we're modern!) is designed to be lean, mean, and super fast. It will resolve @smartergpt/lex, ensuring the package is indeed installed and findable. Then, it'll check for a known canon prompt and schema path, like canon/prompts/remember.md within that package, verifying that our critical Lex canon assets are where they should be. Most importantly, it will print the precedence chain tested, giving us explicit confirmation that our prompt precedence logic is being correctly applied. This script will exit with 0 on success or a non-zero code on failure, making it perfect for CI. Second, we'll add an npm run ci-smoke script to our package.json. This makes calling our new diagnostic a breeze from the command line or, more relevantly, from our CI workflow. Third, and this is crucial, we'll update our CI workflow in .github/workflows/lint-test.yml. Before any heavy build steps kick off, we'll insert a quick sequence: npm ci (to ensure all dependencies are correctly installed) followed immediately by npm run ci-smoke. The magic here is the