Automated quality signals before shipping

← Learning Atlas · Path 20 of 28 · Learn the automated checks that catch bugs before shipping: QA scripts, static analysis, linting and type-checking, wired into CI/CD and GitHub Actions.

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Orbit-internal conceptsConcepts and methodologyBuild/deploy tooling

1. 1

   Step 1: QA Intelligence

   Why this matters here

   QA Intelligence starts the path because Orbit needs to surface quality risk before a shipped prompt becomes a user-facing problem.

   In short

   Automated scripts that run after prompts ship to verify nothing broke. Per-project scripts plus universal cross-project checks; results in the QA tab.

   Full definition of QA Intelligence →

   Next: Universal QA ↓

2. 2

   Step 2: Universal QA

   Why this matters here

   Universal QA follows because it turns that risk awareness into reusable checks across projects instead of one-off manual judgment.

   In short

   QA scripts shipped with Orbit that run against every project's codebase, regardless of project type. Project-agnostic, discovers entry points at runtime.

   Full definition of Universal QA →

   Next: Static analysis ↓

3. 3

   Step 3: Static analysis

   Why this matters here

   Static analysis is the first general technique underneath those checks, catching problems before the code runs.

   In short

   The automated process of inspecting source code to find bugs, security vulnerabilities, or anti-patterns without actually executing the program.

   Full definition of Static analysis →

   Next: Linting ↓

4. 4

   Step 4: Linting

   Why this matters here

   Linting is the first concrete static check, focused on style, suspicious patterns, and maintainability issues.

   In short

   A specific type of basic static analysis that flags stylistic errors, formatting inconsistencies, and suspicious constructs in source code.

   Full definition of Linting →

   Next: Type-checking ↓

5. 5

   Step 5: Type-checking

   Why this matters here

   Type-checking belongs after linting because it validates deeper contracts between values, functions, and modules.

   In short

   The process of verifying that the data types used in a program (like ensuring a number isn't treated as text) are strictly correct, preventing runtime crashes.

   Full definition of Type-checking →

   Next: CI/CD ↓

6. 6

   Step 6: CI/CD

   Why this matters here

   CI/CD is where those checks become repeatable gates instead of optional local habits.

   In short

   A set of automated practices where code changes are frequently built, tested, and securely deployed to production without manual human intervention.

   Full definition of CI/CD →

   Next: GitHub Actions ↓

7. 7

   Step 7: GitHub Actions

   Why this matters here

   GitHub Actions makes CI/CD concrete by showing one common place teams automate checks on commits and pull requests.

   In short

   An automation platform built directly into GitHub that allows developers to create custom CI/CD workflows triggered by repository events.

   Full definition of GitHub Actions →

   Continue with Moving between Windows and Unix shells →

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