OMG QA is a quality-findings platform where QA engineers, automated test suites, and AI coding agents collaborate around one shared object — the Finding — from first failure all the way to a merged fix. This page explains the model, the lifecycle, and the three ways to get data in.
Instead of scattering bugs across spreadsheets, chat threads, and CI logs, everything funnels into a Finding. A Finding carries its type, severity, status, evidence, comments, AI analysis, and links to the pull requests and test runs that touch it.
Findings live in Projects inside your Organization (workspace).
Each finding gets a stable key like CHK-1042 so it's easy to reference in PRs,
commits, and conversations.
Every finding travels the same arc — in through one of four channels, through triage and review where agents and people work side by side, and out as a resolved, auditable record. Agents can do a lot of the legwork, but a human always stays in the loop and in control of what actually ships.
However a quality signal originates, it lands as a single Finding in one shared inbox. No path is privileged — an agent-filed bug and a human-filed risk are the same kind of object.
A test fails and the reporter opens a finding with trace, video, and screenshot — deduplicated by failure signature so repeats bump a counter, not your inbox.
Claude Code, Cursor, or Codex spots an anomaly while working and files it directly through the MCP server — checking for similar findings first.
A person flags a risk, observation, or bug by hand in the app, attaching evidence as they go. Human judgment is a first-class entry point.
Any other system — your own tooling, an incident pipeline, a monitor — can create findings over plain HTTP with a scoped API key.
Inside the platform a finding moves through a workflow (open → triage → in progress →
in review → resolved). Agents accelerate every step; humans review, approve, and decide.
Identical failures collapse by signature, and semantic embeddings surface similar findings even when the wording differs. Screenshots, traces, HAR files, and videos attach automatically to private storage — no copy-paste.
An agent reads the finding, checks duplicates, suggests a severity, and drafts a fix plan. Crucially, these are proposals — the agent advances the work but does not get the final say.
Agent writes can run read-only or behind a human-approval queue, so automation never gets excessive agency. A person confirms severity, approves or overrides transitions, comments, and decides what is real and what ships. Every action — human or agent — is recorded in the audit trail.
Connect GitHub, GitLab, or Azure DevOps and OMG QA links PRs/MRs, commits, and CI runs. When the fixing PR/MR merges, the linked finding moves to Ready for retest.
Not everything ends in code. A person can close as fixed, won't fix, accepted risk, or duplicate — with a reason captured for the record.
Resolution fans out in-app and via outbound webhooks, so the rest of your stack — Slack, dashboards, downstream tools — stays in sync.
The finding retains its complete history — who (or which agent) did what, when, and why — so a resolved finding is also a durable, reviewable record.
Add the reporter to playwright.config.ts. Every CI failure becomes a Finding
in seconds, with trace, video, and screenshot attached and deduplicated.
Create and manage findings, projects, evidence, and test runs from any system. Auth with a scoped API key as a Bearer token.
OMG QA runs a Model Context Protocol server. Claude Code, Cursor, or Codex can search, read, create, and transition findings directly.
Point your reporter at the ingestion API; failures are turned into findings and deduped by signature:
The reporter fails soft (a misconfigured or unreachable OMG QA never fails your test run), counts a test that passes on retry as passed (Playwright "flaky"), and reports each browser project separately. Under the hood it publishes the run to the ingestion API and failures are turned into deduplicated findings. The same works for Jest/Vitest, Cypress, pytest, and any JUnit-producing runner — or do it from any CI system with plain HTTP; see the API Reference.
Evidence (images, video, traces, HAR, logs) uploads directly to object storage via short-lived signed URLs — the API never proxies file bytes. Images get a thumbnail and the attachment moves to Processed. Downloads use short-lived signed URLs too, so files stay private to your organization.
Every project has a role-aware analytics surface that turns raw runs and findings into release decisions. All views are bounded by a date window and can be pulled as JSON or downloaded as CSV for a spreadsheet or BI tool.
Pass / fail / flaky trend and findings created-vs-resolved over time, bucketed by day or week.
Leaderboards rank the noisiest tests and the flakiest ones, each drilling straight into the linked finding.
Ingest coverage from CI (Cobertura, lcov, or a simple percent) and watch the trend per commit and branch.
One rollup — open criticals/highs, pass rate, flaky rate, coverage — resolves to a go / no-go verdict for a release.
Save any combination of project, type, status, and search query as a named view. Views are personal by default and can be shared with the whole organization.
An in-app notification inbox plus transactional email (with a findings digest, delivery suppression, and SSRF-guarded outbound webhooks) keep the right people in the loop.
Open a Playwright trace or HAR right inside a finding, and see every artifact and trace event laid out on one synchronized millisecond timeline — no separate tooling.
Owners/admins can export the whole organization — findings, evidence manifest, and history — as a portable document, re-import it, and run a verifiable offboarding. No lock-in.
Invite teammates by email with a role (owner, admin, QA lead, QA engineer, developer, viewer). The invite email signs them straight in — type your name and you're in the workspace; from then on sign-in is a passwordless one-time code.
OMG QA — Capture Every Finding. Fix Faster.