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Impact Gate Impact Gate Impact Gate

Quick Start

Core workflow

Learn how to install Impact Gate, run diff-aware impact analysis, and turn the result into a release-ready test plan in minutes. The same commands work for pull requests, release branches, and previous shipped tags.

What this gets you

Go from diff to test plan in one short loop

All three steps below are free-tier, require no API key, and work for pull requests, release branches, and previous shipped tags.

Three commands

The default path stays deterministic

npx impact-gate impact --path . --since origin/main npx impact-gate plan --path . --since origin/main npx impact-gate gate --threshold 80 --path .

Step 1: Install

01

Install the package into the repo

Add the CLI as a dev dependency so impact analysis and planning run next to the test suite.

Terminal window
npm install -D @yasserkhanorg/impact-gate

Step 2: Run Impact Analysis

02

Compare the current branch to a known baseline

Start with the base branch for pull requests, then swap in the previous shipped tag when you want release-readiness planning.

Point the tool at your project and diff against your base branch:

Terminal window
npx impact-gate impact --path . --since origin/main

This parses the git diff, maps changed files to route families, and reports which E2E test flows are impacted.

For release readiness, point the same workflow at the previous shipped tag or release branch:

Terminal window
npx impact-gate impact --path . --since v2.1.0

Step 3: View the Results

03

Turn the same diff into a written coverage plan

The plan command writes the artifacts you can inspect in CI, release review, and downstream automation.

The impact command prints a deterministic summary to stdout. To write coverage artifacts with gap analysis, run plan:

Terminal window
npx impact-gate plan --path . --since origin/main

This produces:

  • .e2e-ai-agents/plan.json — structured plan with run sets and confidence scores
  • .e2e-ai-agents/ci-summary.md — markdown summary suitable for PR comments

Use the exact same command for a release-diff test plan:

Terminal window
npx impact-gate plan --path . --since v2.1.0

That gives you a release-focused view of impacted flows, current coverage, and what still needs tests or manual validation before shipping.

Why The AI Path Is Safer Than Raw Generation

Deterministic first

The plan exists before the model does

Diff analysis, impact mapping, and coverage planning happen before generation enters the loop.

Repo grounding

Prompts are constrained by local API knowledge

Page objects, helpers, signatures, and inherited methods are discovered before prompting.

Detection

Suspicious calls are flagged after generation

Invented methods and fabricated helpers are caught instead of silently landing in the main suite.

Verification

Generated specs still need to earn trust

Compile checks and smoke runs are used before a generated spec counts as verified.

When you later enable generation or healing, impact-gate does not just trust whatever the LLM writes.

  • The diff and coverage plan are established first with deterministic analysis
  • Generation prompts are grounded in your repository’s discovered page objects and helpers
  • The generator is told to use only known methods and fall back to raw Playwright selectors when needed
  • Suspicious method calls are detected after generation and blocked into generated-needs-review/
  • Written specs are compile-checked and smoke-run before they count as verified

What Next?

CI Integration Release Readiness AI Guardrails CLI Reference
Confidence

Make the deterministic path trustworthy first

  • Run train --no-enrich to improve route-family accuracy
  • Add gate --threshold 80 in CI once plan output looks trustworthy
  • Add a CI integration to gate PRs on coverage
Expansion

Layer in graph bootstrap and optional AI later

  • If you already have an Understand-Anything knowledge graph, run bootstrap instead of train
  • Try crew --workflow quick-check for strategy recommendations on top of the core CI loop
  • Set up cost controls before enabling AI features