JC Meteor钧宸流星
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Project hub

JC Meteor

Build testing systems that can be repeated, observed, and improved.

A project-led engineering portfolio centered on repeatable testing systems, voice interaction products, local execution loops, and practical agent workflows.

Explore MeteorTest Open MeteorTest Web Preview View GitHub
01AI operation entry
02Project and suite import
03Task creation
04Local Agent
05Reports
06AI next action
AI-first execution model

This is a simplified AI-assisted operation model, not a screenshot of the current MeteorTest web console.

Flagship project

MeteorTest

Automation testing platform for projects, suites, local executors, reports, and AI-assisted operations.

MVP / active development

A testing control plane that connects project contracts, AI-assisted operations, local execution, and report context.

Open MeteorTest Open Web Preview Try demo GitHub
MeteorTest Web dashboard preview screenshot
Projectyunlu-ios
Suiteapi_smoke
Executorphase9-local-agent
Reportoutput.log + Allure
AI Operation Flow
  1. ImportProject contract and suites
  2. PrepareTask, environment, build
  3. ExecutePrivate Local Agent
  4. AnalyzeReport context and next action
Validation and preview status
Local validationSucceeded
Smoke suite6 passed
Web previewOnline
ExecutionPrivate Agent
Preview online
Project substance

Why MeteorTest exists

MeteorTest is built around one practical loop: let a platform or AI assistant import test projects, register suites, create tasks, trigger local execution, and bring logs, reports, and failure context back into one place.

Problem

  • Automation scripts often live in separate repositories without a shared entry point.
  • Task execution is easy to start manually, but hard to trace later.
  • Reports, logs, app artifacts, environments, and task context are usually disconnected.
  • AI assistants are less useful when they can only comment on logs instead of operating with project, suite, task, and report context.

What it does

  • Imports projects and suites from a test project's meteortest.yml contract.
  • Lets the AI assistant help create tasks, select suites, inspect reports, and summarize failures from platform context.
  • Lets a Python Local Agent claim tasks and execute pytest, Appium, or Locust commands.
  • Collects status, logs, Allure artifacts, and AI-assisted operation history.

Current status

  • MVP and active development.
  • The platform-to-agent-to-test-repository execution path has been validated with iOS-Automation-Framework.
  • The API smoke suite can now produce real pass/fail results against the iOS-Automation-Framework local mock API.
  • The MeteorTest Web preview is online at meteortest.jcmeteor.com; Local Agent execution remains private.

Validation progress

  • The public site keeps the demo interactive and mock-data based.
  • The separate MeteorTest Web preview is online for console surface validation.
  • Use the local mock API as public-safe validation results for API smoke assertions.
  • MeteorTest Local Agent has run the same mock-backed smoke suite and collected task-specific logs/report artifacts.
Validated local run

MeteorTest ran the mock-backed API smoke suite locally

A MeteorTest Local Agent task executed iOS-Automation-Framework `api_smoke` against the deterministic local mock API and collected task-specific logs plus Allure result artifacts.

Taskphase9-api-smoke-001
ResultSucceeded
Pytest6 passed
Exit code0

Run summary

  • Suite: api_smoke
  • Environment: local-mock-api
  • Runtime: iOS-Automation-Framework/.venv on Python 3.13
  • Selection: 6 smoke cases selected, 16 non-smoke cases deselected
  • Artifacts: output.log and Allure results collected under task-specific report paths

The public website still uses a browser-side mock demo, and the live Web preview keeps execution private. Public connected execution is a later design topic after authentication, data isolation, and executor safety are designed.

Companion test project

iOS-Automation-Framework

The first practical test-code carrier and platform integration sample for MeteorTest.

GitHub
project: yunlu-ios
suites:
- id: api_smoke
- command: python -m pytest
report: allure
A compact engineering ecosystem

Projects that support the same loop

MeteorTestAutomation testing platform for projects, suites, local executors, reports, and AI-assisted operations.MeteorVoiceA bilingual voice conversation coach for English speaking practice across mobile and web sessions.iOS-Automation-FrameworkThe first practical test-code carrier and platform integration sample for MeteorTest.junchen-meteorA bilingual personal website that presents JC Meteor's project hub, technical identity, and public project ecosystem.LensportA local MCP server that gives AI coding agents the ability to see your screen — no cloud, no telemetry, no background recording.AgentShotA local-first screenshot bridge that turns clipboard screenshots into AI-ready prompts for terminal coding agents.TimeTrackerA backend service for tracking users, devices, events, and daily sessions.idea-to-app-specAn AI workflow skill that turns rough ideas into specs, plans, one-shot prompts, and implementation handoff artifacts.senior-engineering-guardrailsA small discipline layer for coding agents that keeps implementation work focused, simple, and verifiable.douyin-video-toolkitA toolkit for turning authorized Douyin live replay footage into reviewed vertical short-video drafts.
Engineering focus

What this site is about

Automation testing platforms

Build control planes that connect projects, suites, tasks, local executors, logs, reports, and follow-up actions.

Project contractsTask queuesReport context

iOS automation infrastructure

Keep UI automation and API smoke tests maintainable through clear fixtures, runtime isolation, and platform integration contracts.

pytest/AppiumAllure outputRuntime ownership

AI-assisted engineering workflows

Use AI as an operation partner that can import projects, prepare tasks, inspect results, guide voice practice, and suggest concrete next actions.

Agent guardrailsVoice feedbackHuman review

Voice-first learning products

Build spoken interaction loops where ASR, endpointing, TTS, session state, and feedback controls stay observable across web and mobile.

ASR provider layerTTS routingMobile session control

Repeatable local execution

Prefer small, observable execution loops that can run locally first, then become website validation results or platform workflows later.

Local-first validationEnvironment checksDemo readiness
Contact & collaboration

Open project channels first

JC Meteor is currently organized around public engineering work. GitHub is the best place to understand the projects, follow progress, and discuss project-specific questions.

Good topics to discuss
  • Automation testing platforms and local execution loops.
  • Voice AI practice products, ASR/TTS provider routing, and mobile session control.
  • iOS automation, pytest/Appium integration, and test infrastructure.
  • AI-assisted development workflows that can operate on real project context.
GitHub ProfileStart here for the public project hub, repositories, and profile updates.Open link MeteorTest IssuesUse this for platform questions, bugs, roadmap feedback, and testing workflow ideas.Open link MeteorVoice IssuesUse this for voice practice, ASR/TTS routing, mobile session, and speech feedback questions.Open link iOS Automation IssuesUse this for test project integration, pytest/Appium, and sample execution questions.Open link

For private details such as credentials, internal URLs, device identifiers, or test accounts, use a private channel when one is published.