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Guidance for using Generator.Equals — a C# source generator for auto-generating Equals, GetHashCode, operators, and Diff/Inequalities methods via attributes.

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Use when the user needs a professional marketer's operating mindset for any marketing, growth, or client-acquisition task — finding first customers, writing an ad or landing page, designing ad creatives and visuals (how the eye works: background, scene, hero, movement), evaluating an idea, deciding whether to do X to get Y, positioning or launching a B2B or SaaS product, running cold outreach, setting up ads, or writing copy — not a tactical template.

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Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-context.

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Review EXISTING test code for quality, smells, and testability issues. Detects test smells across six dimensions — readability, reliability, diagnostic value, design, AI-generated, and coverage — analyzes testability of application code, and backs the qualitative smells with mutation testing. Use when: "review my tests," "test quality audit," "test smells," "testability analysis," "are these tests any good." Not for: generating new tests — use `ai-test-generation`. Not for: testing AI features in your product — use `ai-system-testing`. Related: unit-testing, shift-left-testing, coverage-analysis, ai-test-generation.

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Write effective unit tests with Jest, Vitest, or pytest. Covers the test-doubles taxonomy (stub/spy/mock/fake), Arrange-Act-Assert, coverage threshold configuration and CI gating, snapshot testing, fake timers, and mutation testing with Stryker/mutmut. Use when: "unit test," "Jest," "Vitest," "pytest," "mock," "coverage threshold," "test doubles," "mutation testing," "fake timers," "snapshot test." Not for: interpreting coverage reports or finding coverage gaps — use coverage-analysis; AI generating the test code for you — use ai-test-generation; auditing existing tests for smells — use ai-qa-review; browser/component rendering assertions — use cypress-automation or visual-testing. Related: coverage-analysis, ci-cd-integration, ai-test-generation, shift-left-testing.

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Runtime per-test healing with evidence: multi-attribute selector healing, environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. Use when: "flaky test," "test stability," "self-healing locator," "broken locator recovery," "unreliable test," "quarantine flaky test." Not for: bulk regenerating selectors after a planned UI refactor — use selector-drift-recovery (this skill heals ONE test at runtime; that one rewrites many tests offline). Not for: classifying/clustering CI failures into bug reports — use ai-bug-triage. Related: playwright-automation, selector-drift-recovery, ci-cd-integration, qa-metrics, ai-bug-triage.

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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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Write production-grade Playwright tests in TypeScript: Page Object Model, fixtures, auto-waiting, user-facing locators, parallel execution, CI integration, sharding, and 2025-2026 feature awareness. Includes an explicit "do not" list for AI agents. Use when: "Playwright," "write E2E test," "page object," "new Playwright suite," "Playwright config." Not for: fixing one flaky test at runtime — use test-reliability. Not for: bulk regenerating selectors after a UI refactor — use selector-drift-recovery. Not for: visual baseline creation/management — use visual-testing. Not for: deep WCAG/axe audits — use accessibility-testing. Related: visual-testing, ci-cd-integration, api-testing, test-reliability, selector-drift-recovery, accessibility-testing.

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Defines God Committee member behavior and responsibilities with oversight authority. Use when operating as a committee member. Triggers on: god committee, committee observation, council discussion.

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Apply when integrating a new requirement into an existing design. Redesign as if the requirement had been a foundational assumption from day one, instead of bolting it on.

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Klaviyo API and developer integration expertise. Event tracking, SDKs, webhooks, rate limits, OAuth, catalog sync, and code patterns. Use when the user asks about Klaviyo API, integrating with Klaviyo, tracking events, building custom integrations, webhook handling, or developer implementation. For marketing strategy, flow optimization, and campaign auditing, see the klaviyo-analyst skill.

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Generate Word documents programmatically with the docx library — create paragraphs, tables, images, headers, footers, numbered lists, and styled content. Use when tasks involve generating contracts, proposals, resumes, or any .docx output from application data in Node.js.

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Build, deploy, and update React Native apps with Expo. Covers file-based routing with Expo Router (stacks, tabs, drawers, deep linking), EAS Build and Submit, over-the-air updates, and 50+ SDK modules for camera, location, notifications. Use when setting up Expo, adding navigation, configuring builds, or integrating native device APIs. Trigger words: expo, expo router, eas build, react native expo, file-based routing mobile, deep linking.

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Parse and generate CSV files with the csv package — stream large files, handle custom delimiters, transform records, validate data, and generate CSV output from objects. Use when tasks involve data import/export, ETL pipelines, processing uploaded CSV files, or generating downloadable reports.

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Implement GDPR and ePrivacy-compliant cookie consent: a banner with equal Accept and Reject buttons, per-category choices, stored consent records, consent-gated analytics with Google Consent Mode v2, and Global Privacy Control. Use when adding a cookie banner, managing marketing consent, achieving GDPR compliance for EU users, or integrating consent with analytics platforms.

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Draw 2D graphics with the HTML5 Canvas API — shapes, text, images, gradients, transformations, pixel manipulation, and offscreen rendering. Use when tasks involve generating images server-side (with node-canvas), creating charts, image compositing, watermarking, or browser-based drawing applications.

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Assists with using Bun as an all-in-one JavaScript/TypeScript runtime, package manager, bundler, and test runner. Use when building HTTP servers, managing packages, running tests, or migrating from Node.js. Trigger words: bun, bun serve, bun install, bun test, bun build, javascript runtime, bun runtime.

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Automates invoice generation, billing workflows, payment tracking, and revenue recognition for SaaS and service businesses. Use when building billing pipelines, generating invoice PDFs, usage-based invoicing, subscription management, payment reminders, or financial reporting. Trigger words: invoice, billing, payment, subscription, usage billing, revenue recognition, accounts receivable, dunning, proration, Stripe billing, generate invoice, create bill.

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Assists with managing versioning and changelogs in JavaScript/TypeScript monorepos using Changesets. Use when automating semantic versioning, generating changelogs from PRs, coordinating multi-package releases, or publishing to npm. Trigger words: changesets, versioning, changelog, npm publish, monorepo release, semver.

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Reference for architectural dimensions, proportions, and building codes for AI-generated 3D/2D models. Use when: generating floor plans, creating 3D buildings, validating architectural designs, ensuring correct human-scale proportions in models.

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Create ZIP and TAR archives programmatically with Archiver — bundle files, directories, and streams into compressed archives with progress tracking. Use when tasks involve generating downloadable bundles, backing up directories, creating deployment packages, or building export features that zip multiple files together.

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AG2 (formerly AutoGen) is an open-source Python framework for building AI agents and multi-agent systems that use tools, human approval and structured conversations. Use it when you need agents that call functions, collaborate in a group chat, execute code, or when you are migrating AutoGen or pyautogen code to AG2 v1 or ag2-classic.

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Build AI agents that automate scientific research — hypothesis generation, experiment design, data analysis, and paper writing using agentic tree search. Use when: automating research workflows, generating and testing hypotheses, building AI-powered research assistants.

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Patterns for designing, building, and operating microservices architectures. Covers service decomposition, inter-service communication, resilience patterns, data consistency, and observability in distributed systems. Use when ", " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。