Testdriver:Test Results Json logo

Testdriver:Test Results Json

Organization
testdriverai
testdriver:test-results-json

Per-test JSON result files with metadata, versions, and infrastructure details

Overview

Publishertestdriverai
Repositorytestdriverai
Skill nametestdriver:test-results-json
Stars
465
Forks
39
Bundled files
Instructions only
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 testdriverai on GitHub. Read the source before you install it.

Installation

Install the Testdriver:Test Results Json 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/testdriverai/testdriverai/tree/main/ai/skills/testdriver-test-results-json
  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/testdriverai/testdriverai.git /tmp/testdriverai
mkdir -p .claude/skills
cp -r /tmp/testdriverai/ai/skills/testdriver-test-results-json .claude/skills/testdriverai-testdriver-test-results-json
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testdriver:Test Results Json 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 Testdriver:Test Results Json 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 Testdriver:Test Results Json 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.

Overview

TestDriver writes a JSON result file for each test case after it finishes. These files have full metadata about the test run. This includes the SDK version, the runner version, infrastructure details, interaction statistics, and links to recordings.

TestDriver writes the result files to:

.testdriver/results/<testFile>/<testName>.json

For example, a test file tests/login.test.mjs with a test named "should log in" produces:

.testdriver/results/tests/login.test.mjs/should_log_in.json

Enabling

You do not need configuration. The TestDriver Vitest reporter plugin writes the JSON files automatically each time the tests run.

JSON Schema

Each result file is organized into logical groups:

versions

FieldTypeDescription
versions.sdkstring | nullTestDriver SDK version (e.g. "7.8.0")
versions.viteststring | nullVitest version used to run the test
versions.apistring | nullTestDriver API server version
versions.runnerBeforestring | nullRunner version at sandbox start
versions.runnerAfterstring | nullRunner version after auto-update
versions.runnerWasUpdatedbooleanWhether the runner was auto-updated during provisioning

test

FieldTypeDescription
test.filestring | nullRelative path to the test file
test.namestring | nullName of the test case
test.suitestring | nullName of the parent describe block
test.passedbooleanWhether the test passed
test.caseIdstring | nullDatabase ID for this test case
test.runIdstring | nullDatabase ID for the overall test run
test.errorstring | nullError message if the test failed
test.errorStackstring | nullError stack trace if the test failed

urls

FieldTypeDescription
urls.apistring | nullAPI root URL used for this test
urls.consolestring | nullTestDriver console base URL
urls.vncstring | nullVNC URL for the sandbox
urls.testRunstring | nullDirect link to this test case in the console

replay

The replay object contains the recording replay URL and derived embed links. The gifUrl and embedUrl are generated automatically from the replay URL.

FieldTypeDescription
replay.urlstring | nullRecording replay URL
replay.gifUrlstring | nullAnimated GIF thumbnail of the recording
replay.embedUrlstring | nullEmbeddable replay URL (appends &embed=true)
replay.markdownstring | nullReady-to-use Markdown embed with GIF linking to the replay

The replay.markdown field produces a clickable GIF badge you can paste directly into PR comments, README files, or issue descriptions:

markdown
[![Test Recording](https://api.testdriver.ai/replay/abc123/gif?shareKey=xyz)](https://console.testdriver.ai/replay/abc123?share=xyz)

date

FieldTypeDescription
datestringISO 8601 timestamp when the test finished

team

FieldTypeDescription
team.idstring | nullTeam ID from the sandbox
team.sessionIdstring | nullSDK session ID

infrastructure

FieldTypeDescription
infrastructure.sandboxIdstring | nullSandbox instance ID
infrastructure.instanceIdstring | nullInstance ID
infrastructure.osstring | nullOperating system of the sandbox ("linux" or "windows")
infrastructure.amiIdstring | nullAWS AMI ID used for provisioning
infrastructure.e2bTemplateIdstring | nullE2B template ID used for provisioning
infrastructure.imageVersionstring | nullSandbox image version

realtime

FieldTypeDescription
realtime.channelstring | nullAbly channel name used for communication
realtime.messageCountnumberNumber of messages published to the realtime channel

interactions

FieldTypeDescription
interactions.totalnumberTotal number of interactions recorded
interactions.cachednumberNumber of interactions served from cache
interactions.byTypeobjectBreakdown of interactions by type (e.g. find, click, assert)

Example Output

json
{
  "sdkVersion": "7.8.0",
  "vitestVersion": "4.0.0",
  "apiVersion": "1.45.0",
  "runnerVersionBefore": "2.1.0",
  "runnerVersionAfter": "2.1.1",
  "wasUpdated": true,
  "apiUrl": "https://api.testdriver.ai",
  "consoleUrl": "https://console.testdriver.ai",
  "testRunLink": "https://console.testdriver.ai/runs/abc123/def456",
  "dashcamUrl": "https://app.dashcam.io/replay/abc123",
  "vncUrl": "wss://sandbox-123.testdriver.ai/vnc",
  "date": "2025-01-15T14:30:00.000Z",
  "team": {
    "id": "team_abc123",
    "sessionId": "sess_xyz789"
  },
  "infrastructure": {
    "sandboxId": "sandbox-123",
    "instanceId": "i-abc123",
    "os": "linux",
    "amiId": "ami-0abc123",
    "e2bTemplateId": null,
    "imageVersion": "v2.1.0"
  },
  "realtime": {
    "channel": "sandbox:sandbox-123",
    "messageCount": 42
  },
  "interactions": {
    "total": 15,
    "cached": 3,
    "byType": {
      "find": 8,
      "click": 5,
      "assert": 2
    }
  }
}

Using Result Files in CI

Result files are useful for extracting test metadata in CI pipelines without parsing log output.

GitHub Actions Example

Use fromJSON to parse a result file into a GitHub Actions expression you can reference in subsequent steps:

yaml
- name: Run tests
  run: npx vitest run tests/login.test.mjs

- name: Parse result
  id: result
  run: |
    # Read the first JSON result file
    FILE=$(find .testdriver/results -name '*.json' | head -n 1)
    echo "json=$(cat "$FILE")" >> "$GITHUB_OUTPUT"

- name: Comment on PR
  if: fromJSON(steps.result.outputs.json).test.passed == false
  uses: actions/github-script@v7
  with:
    script: |
      const result = ${{ steps.result.outputs.json }};
      await github.rest.issues.createComment({
        owner: context.repo.owner,
        repo: context.repo.repo,
        issue_number: context.issue.number,
        body: [
          `❌ **${result.test.name}** failed`,
          ``,
          `Error: ${result.test.error}`,
          ``,
          result.replay.markdown,
          ``,
          `[View full recording](${result.urls.testRun})`
        ].join('\n')
      });

You can also load all results into a matrix or iterate over them:

yaml
- name: Run tests
  run: npx vitest run tests/*.test.mjs

- name: Collect results
  id: results
  run: |
    # Merge all result files into a JSON array
    echo "json=$(find .testdriver/results -name '*.json' -exec cat {} + | jq -s '.')" >> "$GITHUB_OUTPUT"

- name: Summary
  run: |
    echo '## Test Results' >> $GITHUB_STEP_SUMMARY
    RESULTS='${{ steps.results.outputs.json }}'
    echo "$RESULTS" | jq -r '.[] | "| \(.test.name) | \(if .test.passed then "✅" else "❌" end) | \(.urls.testRun) |"' >> $GITHUB_STEP_SUMMARY

Reading Results Programmatically

javascript
import fs from "fs";
import path from "path";

const resultsDir = ".testdriver/results";

function readResults(dir) {
  const results = [];
  for (const testDir of fs.readdirSync(dir, { recursive: true })) {
    const fullPath = path.join(dir, testDir);
    if (fullPath.endsWith(".json") && fs.statSync(fullPath).isFile()) {
      results.push(JSON.parse(fs.readFileSync(fullPath, "utf-8")));
    }
  }
  return results;
}

const results = readResults(resultsDir);
const passed = results.filter(r => r.test.passed);
const failed = results.filter(r => !r.test.passed);

console.log(`${passed.length} passed, ${failed.length} failed`);
for (const r of failed) {
  console.log(`  FAIL: ${r.test.name} — ${r.test.error}`);
  console.log(`  Recording: ${r.urls.testRun}`);
  console.log(`  Embed: ${r.replay.markdown}`);
}

Frequently asked questions

What does the Testdriver:Test Results Json AI skill do?

Per-test JSON result files with metadata, versions, and infrastructure details

Why use Testdriver:Test Results Json on TypingMind?

Because you install it once and use it with any model. Testdriver:Test Results Json 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 Testdriver:Test Results Json in TypingMind?

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

Which AI models can use Testdriver:Test Results Json?

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 Testdriver:Test Results Json?

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

Is the Testdriver:Test Results Json AI skill free?

It is published on GitHub by testdriverai. Check the repository for licensing terms. 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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