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Playwright Test Results

OrganizationPopular
microsoft
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.

Overview

Publishermicrosoft
Repositoryplaywright
Skill nameplaywright-test-results
Stars
96.9K
Forks
6.5K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Playwright Test Results AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/microsoft/playwright/tree/main/.claude/skills/playwright-test-results
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/playwright.git /tmp/playwright
mkdir -p .claude/skills
cp -r /tmp/playwright/.claude/skills/playwright-test-results .claude/skills/playwright-test-results
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Playwright Test Results in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Playwright Test Results on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Playwright Test Results is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
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:

bash
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:

bash
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:

ColumnMeaning
run_id, run_attemptGitHub Actions run identity
run_started_atwhen the run started
workflow_namee.g. tests 1 / tests 2 / tests others / MCP
eventpush / pull_request
head_sha, head_branch, pr_numberwhat was tested
bot_namee.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_nameCI project = browser + suite, e.g. chromium-page, webkit-library, playwright-test
test_titletitle path within the file, joined by › (describe › test)
file, line, column_numbersource location (file is relative to repo root)
expected_statuspassed / skipped / ...
statusactual result: passed / failed / timedOut / skipped / interrupted
retry0 = first attempt
result_started_atwhen this attempt started
duration_msresult duration
error_messageall errors joined, ANSI-stripped (NULL when none)
tagslist of strings, e.g. ['@slow', '@flaky'] (use list functions / list_contains)
annotationslist of {type, description} structs, e.g. [{'type': 'skip', 'description': 'flaky on CI'}] (empty list when none)
artifact_idthe GitHub artifact this row came from (dedupe key)
ingested_atdebug 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:

sql
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):

sql
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:

bash
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/test-results.duckdb"
)).connect();
const result = await conn.runAndReadAll(`
  WITH per_run AS (
    SELECT run_id, run_attempt,
           any_value(run_started_at) AS run_started_at,
           arg_max(status, retry) AS final_status,
           arg_max(expected_status, retry) AS expected_status,
           list(status ORDER BY retry) AS attempt_statuses
    FROM test_results
    WHERE project_name = $projectName
      AND file = $file
      AND test_title = $testTitle
      AND bot_name = $botName
    GROUP BY run_id, run_attempt
  )
  SELECT run_id, run_attempt, final_status, attempt_statuses
  FROM per_run
  WHERE expected_status = 'passed'
    AND final_status IN ('passed', 'failed', 'timedOut')
  ORDER BY run_started_at, run_id, run_attempt
`, test);

const markdown = result.getRowObjectsJson().map(row => {
  const rescued = row.final_status === "passed" &&
    row.attempt_statuses.some(status => status === "failed" || status === "timedOut");
  const emoji = rescued ? "🟧" : row.final_status === "passed" ? "🟩" : "🟥";
  const url = `https://github.com/${repository}/actions/runs/${row.run_id}/attempts/${row.run_attempt}`;
  return `[${emoji}](${url})`;
}).join("");

console.log(markdown);
EOF

The output is Markdown:

markdown
[🟩](https://github.com/microsoft/playwright/actions/runs/123/attempts/1)[🟧](https://github.com/microsoft/playwright/actions/runs/456/attempts/1)[🟥](https://github.com/microsoft/playwright/actions/runs/789/attempts/1)

Each square is one workflow run attempt, oldest first. Green means passed, orange means a retry rescued an earlier failure, and red means failed or timed out. arg_max(status, retry) picks the final verdict after retries, while grouping by (run_id, run_attempt) keeps retries from turning into extra squares. The /attempts/<n> URL links to the exact rerun that produced the result.

Fetching the full detail

The db stores per-result summaries. For the full step tree / attachments / stdio, fetch the original blob report for that run, if the run uploaded one. A row identifies it by run_id + bot_name: the run's blob artifact is named blob-report-<bot_name>.

bash
# List the run's blob artifacts and find the one for this bot_name:
gh api /repos/microsoft/playwright/actions/runs/<run_id>/artifacts \
  --jq '.artifacts[] | select(.name | startswith("blob-report")) | {id, name}'

# Download it (name == "blob-report-<bot_name>"):
gh api /repos/microsoft/playwright/actions/artifacts/<artifact_id>/zip > blob.zip

Blob and parquet artifacts have a 7-day retention, so this works only for recent runs; the db itself retains summaries longer (until run-count eviction).

Frequently asked questions

What does the Playwright Test Results AI skill do?

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.

Why use Playwright Test Results on TypingMind?

Because you install it once and use it with any model. Playwright Test Results is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Playwright Test Results in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/playwright/tree/main/.claude/skills/playwright-test-results. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Playwright Test Results?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Playwright Test Results?

As many as you like. As long as a model supports skills, you can use Playwright Test Results with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Playwright Test Results AI skill free?

Yes. It is published on GitHub by microsoft under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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