Results for “qa”

Spec-driven manual QA testing and Playwright E2E code generation. Orchestrates browser skills to navigate apps like a human tester, producing structured test reports or production-ready .spec.ts files. Always prompts user for credentials — never guesses or stores auth. Use for QA testing, E2E generation, login flow testing, or exploratory testing from a URL. NOT for AI-autonomous flows (see mk:agent-browser).

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実装済みサイトのデザイン品質を体系的にチェックする。配置・余白・比率・レスポンシブなどデザイン的な問題を検出し、修正は行わず問題のリストアップに集中する。 Trigger: デザインQA, デザインチェック, レイアウト確認, デザイン検証, 表示崩れ確認, /design-e2e-qa

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Run ongoing quality checks across OpenClaw subsystems, reproduce bugs, and verify fixes to their underlying causes. Track real environment tests, stress tests, independent reviews, and confirmed merges in an evidence report that supports resuming work.

• Investigate bugs across multiple OpenClaw subsystems
• Verify fixes through regression tests and independent review
• Check real provider, UI, and package behavior

Launch GStack Browser, a visible Chromium browser controlled by an AI agent. Watch actions in a sidebar activity feed and use sidebar chat to request page navigation, clicks, form entry, and reading page content.

• Watching an AI agent operate a browser
• Requesting page navigation and form entry through sidebar chat
• Viewing browser actions during QA or design review

Take implementation work through small, independently mergeable pull requests. Use a separate working directory for each PR, record manual QA evidence, write English review descriptions, resolve CI and review findings, then merge and clean up.

• Deliver implementation work through pull requests
• Split a large task into small, independently mergeable PRs
• Build independent PRs concurrently

Evaluate 3D point clouds and movement trajectories against reference data with CloudAnalyzer. Prepare inputs, measure differences, apply quality thresholds, inspect results visually, and track baseline history for mapping, localization, and perception workflows.

• Compare a generated point cloud with reference data
• Measure errors in estimated movement trajectories
• Check ground segmentation against quality thresholds

Create stock research notes combining analyst forecasts, reported financials, price history, and economic conditions. Compare market expectations with business performance, present valuation measures, and summarize an investment case with positive scenarios, risks, and potential catalysts.

• Preparing an initial research note on a stock
• Comparing analyst forecasts with reported results
• Reviewing financial trends and stock valuation

Coordinate a small AI development team with planning, implementation, and optional independent testing roles. Adapt the process to the work’s complexity, prepare pull requests, and preserve decisions and next steps so later sessions can continue.

• Starting development or taking over an existing project
• Coordinating implementation and independent testing
• Comparing perspectives on product or architecture decisions

Turn feature requirements into test plans, step-by-step manual tests, regression suites for checking existing features, and reproducible bug reports. The skill also supports comparing implemented interfaces with Figma designs and documenting visual differences.

• Plan tests for a new feature
• Write manual checkout test cases
• Organize regression checks before a release

Review unresolved game bugs by separating impact from urgency, then recommend repair priorities and sprint assignments, meaning work planned for a development cycle. Reports also highlight recurring problems, older issues, and bugs in previously completed work.

• Prioritizing open bugs at the start of a sprint
• Organizing new bugs after a team QA review
• Reviewing a backlog with more than ten open bugs

Check new or updated pull requests in isolated checkouts, run the full test suite, and verify temporary test deployments through their public delivery path. Post pass or fail results with evidence to GitHub.

• Checking newly opened or updated pull requests
• Rechecking after a force-push or base branch change
• Finding failures in routing, caching, or redirects

Compares a prototype’s rendered screens with its source design to find differences in typography, spacing, colors, images, and text. Produces prioritized fixes and records visual evidence from follow-up checks before handoff.

• Check a prototype against its Figma source before handoff
• Compare an image-based build with the original visual
• Review visual differences across screen sizes and states

Check code, documents, API responses, and other outputs against a defined quality bar. Receive a score, a pass, revise, or fail verdict, and practical suggestions, with observed behavior informing checks where applicable tools are available.

• Checking code against acceptance criteria
• Reviewing a document for missing content
• Checking API responses against expectations

Check a Kiln branch or pull request through real browser interactions. Multiple agents test separate feature areas in isolated environments, then combine findings into a severity-ranked Markdown report with reproduction steps and expected versus actual behavior.

• Checking all new behavior in a branch or pull request
• Testing multiple feature areas in parallel
• Finding UI text issues and inconsistent interactions

Use when the user asks to "set up conversion values so tROAS optimizes profit not orders", "map margin onto my purchase value", "build value rules for lead / phone / signup conversions", or "stop bidding to revenue when I care about profit"; defines and QAs the conversion VALUE model — per-conversion values, margin/net-value adjustment, static-vs-dynamic value rules, proxy values for non-revenue actions, and a value-vs-count sanity check — as a value-model spec plus a pre-launch value QA sheet. Not for whether the tag fires or UTMs are clean — use conversion-signal-qa; not for cross-platform double-count de-dup — use attribution-reconciler; not for scoring R1/R2 — that is a scored veto in ad-account-auditor. 付费广告转化价值建模/利润出价/价值规则QA

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Browser-based QA combining Chrome MCP control with Python analysis tools. Use when performing browser QA testing, visual regression tracking, WCAG accessibility auditing, performance profiling, or health-scoring web applications.

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Report-only QA — same browser-based testing as qa but documents bugs with screenshots and repro steps only, no code changes. Health score formula weighted across 8 categories. Outputs to .gstack/qa-reports/.

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Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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Measure and improve test coverage meaningfully. Covers Istanbul/V8/coverage.py configuration, coverage gap analysis by risk, coverage-as-ratchet in CI (never let it decrease), PR coverage diff checks, mutation testing for assertion quality, and distinguishing meaningful from vanity coverage. Use when: "code coverage," "coverage gap," "Istanbul," "coverage threshold," "coverage report," "branch coverage." Not for: writing the tests that raise coverage — use unit-testing; coverage as a tracked KPI trend over time — use qa-metrics. Related: unit-testing, ci-cd-integration, qa-metrics, ai-qa-review.

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Audit a whole regression suite and prune/restructure it with evidence: per-test coverage fingerprinting, AST near-duplicate clustering, CI-history mining for never-failing and flaky tests, prune decision rules (redundant/obsolete/low-value/keep), smoke/core/extended tiering by risk and defect-detection history, and a defensible "what we deleted and why" record. Deletion is destructive — quarantine and human sign-off are mandatory. Use when: "audit the test suite," "prune redundant tests," "find duplicate tests," "which tests can we delete," "restructure into smoke/core/extended," "is this test pulling its weight," "shrink the regression suite." Not for: Judging whether an individual test is WELL-WRITTEN (smells, assertions) — that is ai-qa-review. Healing one flaky test at runtime — that is test-reliability. Bulk selector regeneration after a UI refactor — that is selector-drift-recovery. Related: ai-qa-review, coverage-analysis, test-reliability, risk-based-testing, qa-project-context.

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Author and maintain MANUAL and hybrid test cases and suites in TestRail, Xray (Jira), Zephyr Scale, and Qase. Covers test-case anatomy (title, preconditions, steps, expected results, test data), suite/section organization, bulk authoring from user stories and acceptance criteria, ambiguous-step linting, CSV/API import-export payloads per tool, requirement traceability and coverage gaps, review hygiene, and when a manual case should graduate to automation. Use when: "write a test case," "manual test case," "TestRail case," "Xray test," "Zephyr Scale case," "Qase case," "import CSV into TestRail," "lint these steps," "traceability report," "should this be automated." Not for: Generating automated TEST CODE — that is ai-test-generation. Sprint-level WHAT-to-test selection — that is test-planning. Related: ai-test-generation, test-planning, exploratory-testing, qa-project-context.

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Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.

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