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/playwright-test-results

Query Playwright CI test results from the aggregated DuckDB database. Answers questions about flaky tests, failure rates, slow tests, and per-run/SHA/PR results without hunting through GitHub artifacts.

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Install
$ npx -y skills add microsoft/playwright --skill playwright-test-results --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/playwright-test-results

Context preview

The summary Claude sees to decide when to auto-load this skill.

Query Playwright CI test results from the aggregated DuckDB database. Answers questions about flaky tests, failure rates, slow tests, and per-run/SHA/PR results without hunting through GitHub artifacts.

SKILL.md

playwright-test-results.SKILL.md
name: playwright-test-results
description: Query Playwright CI test results from the aggregated DuckDB database. Answers questions about flaky tests, failure rates, slow tests, and per-run/SHA/PR results without hunting through GitHub artifacts.
user_invocable: true

Playwright Test Results (DuckDB)

A single DuckDB file holds recent Playwright CI test results, so you can answer questions about failures, flakiness, and slow tests with plain SQL. It is refreshed every few hours.

Get the database

Download the latest snapshot:

npm ci                       # first time only, from the repo root
GITHUB_TOKEN=$(gh auth token) node utils/test-results-db/cli.ts download

The snapshot may be missing the newest runs. To top it up locally, run `update`:

GITHUB_TOKEN=$(gh auth token) node utils/test-results-db/cli.ts update --lookback-days 3

Query it through the bundled `@duckdb/node-api` binding — no separate DuckDB install needed, it ships in `node_modules` after `npm ci`:

node --input-type=module -e '
import { DuckDBInstance } from "@duckdb/node-api";
const conn = await (await DuckDBInstance.create("utils/test-results-db/test-results.duckdb")).connect();
console.table((await conn.runAndReadAll(process.argv[1])).getRowObjectsJson());
' "SELECT count(*) FROM test_results"

Integer columns come back as strings (JSON-safe), so do ranking and filtering in SQL, not in JS.

Schema

Single table `test_results`, one row per test result (**one row per retry**). The columns are inferred from the parquet the reporter emits (`tests/config/parquetReporter.ts`), plus two trailing columns this CLI adds:

| Column | Meaning | | --- | --- | | `run_id`, `run_attempt` | GitHub Actions run identity | | `run_started_at` | when the run started | | `workflow_name` | e.g. `tests 1` / `tests 2` / `tests others` / `MCP` | | `event` | `push` / `pull_request` | | `head_sha`, `head_branch`, `pr_number` | what was tested | | `bot_name` | e.g. `chromium-ubuntu-22.04-node20`, `webkit-macos-15-large` — the CI bot. **OS and arch are encoded here**; there is no separate os column. | | `project_name` | CI project = browser + suite, e.g. `chromium-page`, `webkit-library`, `playwright-test` | | `test_title` | title path within the file, joined by ` › ` (`describe › test`) | | `file`, `line`, `column_number` | source location (file is relative to repo root) | | `expected_status` | `passed` / `skipped` / ... | | `status` | actual result: `passed` / `failed` / `timedOut` / `skipped` / `interrupted` | | `retry` | 0 = first attempt | | `result_started_at` | when this attempt started | | `duration_ms` | result duration | | `error_message` | all errors joined, ANSI-stripped (NULL when none) | | `tags` | **list** of strings, e.g. `['@slow', '@flaky']` (use list functions / `list_contains`) | | `annotations` | list of `{type, description}` structs, e.g. `[{'type': 'skip', 'description': 'flaky on CI'}]` (empty list when none) | | `artifact_id` | the GitHub artifact this row came from (dedupe key) | | `ingested_at` | debug only — when this row was imported |

Notes:

  • **A test is identified by `(project_name, file, test_title)`** — group on that

tuple. (Playwright's `test_id` hash is deliberately not stored; those three columns are its pre-image.)

  • **Flakiness is derived**, not stored. The signal that matters most is

**cross-run**: a test whose *final* verdict (after retries) flips between runs — green in some, red in others. A separate **within-run** flake is a test a retry rescued inside a single run (`failed`→`passed`).

  • **Real failures vs intentional ones:** filter `expected_status = 'passed'`.

Tests marked `test.fail()` record `status='failed'` *with* `expected_status='failed'` and would otherwise dominate any "most failing" list.

  • The db is size-capped by **run count**: the oldest whole runs are evicted over

time, so it holds a recent window, not full history.

Example queries

Group tests by `(project_name, file, test_title)` and (for failure/flakiness) scope to `expected_status = 'passed'` so intentional `test.fail()` tests don't skew the results.

**Flaky across runs** — the test's final verdict flips between runs (this is what makes a red CI run ambiguous). `least(failed_runs, passed_runs)` ranks genuinely bimodal tests above both always-broken and one-off failures:

WITH per_run AS (
  SELECT project_name, file, test_title, run_id, run_attempt,
         arg_max(status, retry) AS final_status,
         any_value(expected_status) AS expected
  FROM test_results
  GROUP BY project_name, file, test_title, run_id, run_attempt)
SELECT project_name, test_title,
       count(*) AS runs,
       count(*) FILTER (WHERE final_status IN ('failed','timedOut')) AS failed_runs,
       count(*) FILTER (WHERE final_status = 'passed') AS passed_runs,
       round(100.0 * count(*) FILTER (WHERE final_status IN ('failed','timedOut'))
             / count(*), 1) AS fail_pct
FROM per_run
WHERE expected = 'passed'
GROUP BY project_name, test_title
HAVING failed_runs > 0 AND passed_runs > 0 AND runs >= 10
ORDER BY least(failed_runs, passed_runs) DESC, failed_runs DESC
LIMIT 20;

**Filter by tag** (`tags` is a list, not a string):

SELECT project_name, test_title, count(*) AS runs
FROM test_results
WHERE list_contains(tags, '@slow')
GROUP BY project_name, test_title
ORDER BY runs DESC
LIMIT 20;

Generate a linked emoji run history

For a compact result that drops straight into a GitHub comment, render each final run verdict as a linked square. Edit the four test identity fields, then run:

node --input-type=module <<'EOF'
import { DuckDBInstance } from "@duckdb/node-api";

const repository = "microsoft/playwright";
const test = {
  projectName: "firefox-library",
  file: "library/proxy.spec.ts",
  testTitle: "should exclude patterns",
  botName: "firefox-macos-15-large",
};

const conn = await (await DuckDBInstance.create(
  "utils/test-results-db/t
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