FAQ
Everything you need to know about setup, workflow automation, integrations, and how teams use Tynkr in production.
Yes — Tynkr is built on top of Playwright and runs real browser execution. You can import existing .spec.ts or .spec.js files directly from GitHub or upload them manually. Tynkr automatically converts each test() block into an editable visual workflow, preserving your locators, assertions, and test structure without requiring any code changes. Once imported, workflows run on real Chromium, Firefox, and WebKit browsers.
You can import Playwright tests in two ways: upload .spec.ts, .spec.js, or .spec.py files directly from your machine, or connect your GitHub repository and import specs from any branch. Tynkr detects each test() block and translates common Playwright steps — including page.goto, locator actions, assertions, and network interceptions — into visual workflow steps automatically. The import takes under two minutes for most test suites.
No. You can build automation workflows visually using Tynkr's drag-and-drop editor without writing any code. You can also generate workflow steps from plain-text descriptions using AI, or import existing Playwright specs and edit them visually. If you prefer to work in code, you can still write Playwright specs and import them at any time — Tynkr works alongside your existing automation workflow, not as a replacement.
Tynkr integrates with Jira, GitHub, GitLab, Slack, Microsoft Teams, Azure DevOps, CircleCI, PostgreSQL, MySQL, and SQL Server. You can route failure notifications to Slack channels or Teams, create Jira or Azure DevOps tickets automatically on failure with evidence pre-filled, post check run results to GitHub and GitLab pull requests, trigger selective reruns in CircleCI, and query database state as a workflow step. Multiple integrations can be combined in a single workflow.
Yes. Tynkr classifies every test failure as either a real regression or an environment flake using root cause analysis and a pass-rate variance score. It clusters failures by error signature to identify recurring patterns, and labels flaky results separately so they do not pollute your release signal. Instead of masking flakiness with retries, Tynkr surfaces the root cause — selector fragility, race conditions, environment timing, or shared state — so your team can fix the actual problem.
Yes. Tynkr includes visual regression testing with baseline comparisons and pixel-level diff images as a built-in workflow step. You can set a baseline screenshot for any step, and Tynkr flags UI drift automatically on subsequent runs. Visual regression results appear alongside your functional test results in the same execution report — no separate tool or pipeline step required.
Yes. Tynkr includes accessibility checks powered by axe-core and Lighthouse audits for performance, SEO, and best practices. These run as steps in the same automation workflow as your functional tests. You can add an axe-core scan to any workflow, set WCAG rule thresholds, and fail a workflow run if accessibility violations exceed your defined threshold — making accessibility a first-class quality gate on every deployment.
When a workflow fails, Tynkr captures the full execution evidence automatically: screenshots at each step, execution logs, browser console output, network activity, and API call records. The failure is immediately reviewable in the Tynkr dashboard without needing to reproduce it manually. Depending on your integrations, the failure can also be routed to a Slack channel, a Jira ticket, or a GitHub pull request check — all with the evidence attached so the right engineer can act on it immediately.
No. While Tynkr is optimized for QA engineers and release testing workflows, it is used by developers, product teams, and operations teams as well. You can automate browser workflows for operational processes, run validation steps outside traditional QA, and use the orchestration engine for any multi-step automation that involves browser actions, API calls, or database queries — not just test suites.
Most teams have their first workflow running in under five minutes. You can start from a blank workflow using the visual editor, import an existing Playwright spec file from GitHub or your machine, or generate workflow steps from a plain-text description using AI. No infrastructure setup, no CLI installation, and no environment configuration is required — Tynkr runs your workflows in managed real browsers.
Running Playwright tests in CI gives you a pass/fail signal but no context around why a test failed or whether it is a real regression or a flake. Tynkr adds a layer on top of raw Playwright execution: it classifies failures by root cause, scores flakiness using pass-rate variance, captures step-by-step screenshots and network traces for every run, and routes actionable results to your existing tools like GitHub, Jira, and Slack. The result is that your team spends time fixing real bugs instead of triaging noise. You keep your existing Playwright specs — Tynkr manages execution, evidence, and reporting.
Most test automation platforms either require you to rewrite your tests in a proprietary format or focus only on test execution without helping you understand failures. Tynkr is built specifically for teams that already use Playwright: you import your existing .spec.ts or .spec.js files without modification, and Tynkr adds AI-powered flake detection, root cause analysis, full execution evidence — screenshots, logs, network activity — and direct integration with GitHub, Jira, Slack, and Azure DevOps. Unlike generic CI reporters, Tynkr separates real regressions from environment flakes and gives your team a clear, evidence-backed signal for every release decision.
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