How We Built a Formal Verification System for Git Automation Pipelines

The Problem: Silent Failures in Git Automation

A few months ago, we started seeing subtle but nasty bugs in Git Context’s automation pipelines—cases where a user would define a sequence of git operations, only to find that the final state was inconsistent or worse, partially applied. These weren’t crashes. They were silent logic errors: a branch created in one step but referenced before it existed, or a commit ID from one context accidentally used in another. Debugging them meant sifting through logs, replaying steps, and guessing where the assumptions broke down.

The root cause? Our pipeline engine trusted the input too much. We treated pipeline definitions as "well-formed by convention," relying on developers to follow patterns and avoid footguns. But as Git Context grew—adding branching strategies, multi-repo sync, and conditional steps—those conventions became harder to enforce. We needed a way to prove, at parse time, that a pipeline wouldn’t violate its own invariants.

That’s when we decided to build a formal verification layer.

The Three Pillars of Pipeline Integrity

We didn’t reach for full-blown theorem provers or dependent types. Instead, we designed a lightweight, static verification system focused on three key guarantees:

1. Step Preconditions: Each step must declare what it expects to be true before it runs—like "this branch must exist" or "this working directory must be clean."
2. Cross-Step Invariants: The pipeline as a whole must preserve certain properties across steps—like "no two steps modify the same file without a merge strategy."
3. ID Alignment Rules: All references to commits, branches, or worktrees must point to entities created within the same logical context—no mixing IDs from different repos or orphaned references.

These rules are enforced during pipeline compilation, before any git command runs. That means if you try to git checkout $NEW_BRANCH two steps before git branch $NEW_BRANCH is executed, you’ll get a clear error: Step 3: Cannot checkout branch 'feature/x'—no prior step creates it.

Here’s a simplified version of how we model this in code:

interface PipelineStep {
  id: string;
  requires?: {
    branchExists?: string;
    commitExists?: string;
    workingDirClean?: boolean;
  };
}

function verifyPipeline(steps: PipelineStep[]): VerificationResult {
  const createdBranches = new Set<string>();
  const createdCommits = new Set<string>();

  for (const step of steps) {
    if (step.requires?.branchExists && !createdBranches.has(step.requires.branchExists)) {
      return fail(`Step ${step.id}: branch '${step.requires.branchExists}' is required but not created`);
    }
    if (step.requires?.commitExists && !createdCommits.has(step.requires.commitExists)) {
      return fail(`Step ${step.id}: commit '${step.requires.commitExists}' does not exist`);
    }

    // Register side effects
    if (step.type === 'create-branch') {
      createdBranches.add(step.outputBranch);
    }
    if (step.type === 'commit') {
      createdCommits.add(step.outputCommit);
    }
  }

  return success();
}

This isn’t rocket science—but it’s systematic. Every step declares its dependencies and effects, and the verifier walks the chain, building up a model of what’s valid at each point.

Integrating Verification Into the Lifecycle

We didn’t want to make this feel like a separate, academic exercise. So we baked verification directly into the pipeline execution flow:

1. Parse the pipeline definition
2. Run static verification (the step above)
3. If verification passes, proceed to execution
4. If it fails, return a structured error with step numbers and suggested fixes

We also added a --dry-run=verify flag so users can test their pipelines locally before pushing them to CI. It’s been a game-changer for catching typos, copy-paste errors, and logic gaps early.

One trade-off we debated was performance. Running a full verification pass adds a few milliseconds to pipeline startup—negligible for most use cases, but we optimized the checker to short-circuit on first failure and cache results when possible. Correctness won out: we’d rather fail fast than let a pipeline run for 10 minutes only to blow up at step 8.

Developer ergonomics mattered too. We spent time making error messages actionable. Instead of "invariant violated," you get "Step 5 modifies 'package.json' but step 3 already modified it without a merge strategy—add a rebase or lock step."

Why This Matters Now

As Git Context takes on more complex workflows—like automated release branching, PR sync, and cross-repo dependency updates—the cost of a misconfigured pipeline has gone up. What used to be a local hiccup can now cascade across repos and teams. Formal verification gives us a foundation to build more ambitious automation safely.

This isn’t about eliminating all bugs—it’s about eliminating the predictable ones. The kind that follow patterns, that we can catch with simple rules. And by catching them early, we spend less time debugging and more time shipping.

If you’re building automation tools, especially around Git, I’d encourage you to ask: what are the invariants in your system? And are you checking them before things go wrong?