Use when testing Windows 11 desktop apps (WinForms/WPF/UWP) via UFO UIA/Win32 automation MCP. Triggers on "test this Windows app", "QA the app", "run smoke test", "click the button", "fill the form", "check the UI", "Windows automation", "UFO QA", "verify the dialog", or any Windows desktop UI testing task. Not for web/browser testing (use Playwright), mobile testing, or non-Windows platforms.
npx skills add https://github.com/CodeAlive-AI/ai-driven-development --skill windows-qa-engineer
You are an AI-QA operator on the SAME Windows 11 desktop as the SUT.
All automation uses UFO's real MCP tools (UICollector, HostUIExecutor, AppUIExecutor) -- no mocks.
If UFO tools are NOT available as MCP tools, run setup before QA work:
python "<skill-dir>/scripts/skill_installer.py" --project-dir "<project-root>"success is true, tell user to restart Claude CodeFollow this sequence for every test run. Do not skip steps.
qa_refresh_and_list_windows()select_application_window(id, name) (HostUIExecutor)capture_window_screenshot() (UICollector) -- baseline screenshotqa_refresh_controls(field_list=["label","control_text","control_type","automation_id","control_rect"])id + control_text / automation_id when the returned tree is usableclick_input(id, name), set_edit_text(id, name, text), keyboard_input(id, name, keys)texts(id, name) and compare against expectedqa_wait_for_text_contains(id, name, expected, timeout_s=10) over sleepscapture_window_screenshot()| Tool | Server | Purpose |
|------|--------|---------|
| qa_refresh_and_list_windows | QA helper | Refresh + list all windows |
| select_application_window | HostUIExecutor | Select SUT by id+name |
| get_app_window_controls_info | UICollector | Raw control tree; use only when helper output is insufficient |
| capture_window_screenshot | UICollector | Screenshot selected window |
| click_input | AppUIExecutor | Click control by id+name |
| set_edit_text | AppUIExecutor | Type into control |
| keyboard_input | AppUIExecutor | Send keystrokes |
| texts | AppUIExecutor | Read control text |
| qa_wait_for_text_contains | QA helper | Poll until text matches |
| qa_refresh_controls | QA helper | Re-collect control tree with fail-soft parsing |
User says: "Test the login flow on MyApp"
1. qa_refresh_and_list_windows() → find "MyApp - Login"
2. select_application_window(id="3", name="MyApp - Login")
3. capture_window_screenshot() → baseline
4. qa_refresh_controls(field_list=["label","control_text","control_type","automation_id","control_rect"])
→ find username (id=12), password (id=14), login button (id=16)
5. set_edit_text(id="12", name="Username", text="testuser")
6. set_edit_text(id="14", name="Password", text="pass123")
7. click_input(id="16", name="Login")
8. qa_wait_for_text_contains(id="20", name="WelcomeLabel", expected_substring="Welcome", timeout_s=10)
→ {"ok": true, "text": "Welcome, testuser"}
9. capture_window_screenshot() → post-login
10. Report: PASS
No windows found: Re-check the SUT is running. Call qa_refresh_and_list_windows() again. If still empty, ask the user to confirm the app is open.
Empty control tree: The window may not have finished loading. Wait 2-3 seconds, then qa_refresh_controls(field_list=[...]). If still empty, try CONTROL_BACKEND=win32 (see setup.md). For large or legacy WinForms apps, avoid repeated full UIA subtree scans and use screenshot plus targeted coordinates.
Control not clickable / action fails: Re-collect controls (the tree may have changed after navigation). If the control lacks a usable id, fall back to coordinate-based action and document why.
MCP tools not found: Run auto-setup first (see Auto-Setup above). If auto-setup fails, direct the user to references/setup.md and run doctor.ps1.
See references/qa-workflows.md for more examples, locator strategy, and common patterns.
See references/setup.md for UFO installation, MCP configuration, and diagnostics.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take codealive-ai/windows-qa-engineer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.