测试与 QA

测试、调试、验证和质量工作流

837 个 Skills 可用

Skills 列表

camsnap

camsnap

386Ktesting-qa

Capture frames or clips from RTSP/ONVIF cameras.

openclaw avataropenclaw
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camsnap

camsnap

385Ktesting-qa

抓取 RTSP/ONVIF 摄像头的单帧快照或视频片段。

steipete avatarsteipete
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feature-flags

feature-flags

249Ktesting-qa

Use when feature flag tests fail, flags need updating, understanding @gate pragmas, debugging channel-specific test failures, or adding new flags to React.

react avatarreact
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test

test

249Ktesting-qa

Use when you need to run tests for React core. Supports source, www, stable, and experimental channels.

react avatarreact
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contract-first

contract-first

244Ktesting-qa

当多个消费方和提供方需要演进 API 或事件模式,且不希望出现字段漂移、集成意外或某一方静默重新定义接口时使用。

affaan-m avataraffaan-m
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loop-design-check

loop-design-check

243Ktesting-qa

Design a goal-oriented agent loop, and review it for the ways loops go wrong — spinning and burning tokens, Goodhart-gaming the verifier, or running a wrong answer to completion. Two actions: (1) WRITE a loop — gate whether to build it, define a machine-decidable goal, pick the loop type, pick a skeleton; (2) REVIEW a loop — run it past five failure modes plus decidability, boundaries, fallback, judge independence, and keep-judgment-with-the-human red lines. Use when designing an autonomous agent loop, or when you already have one and worry it will spin, cheat, or run a wrong answer to the end. Complements the mechanism-layer loop skills (autonomous-loops, continuous-agent-loop) by covering the judgment layer they don't. 中文触发:写 loop、设计 loop、做一个 loop、检查 loop 对不对、loop 体检、loop 会不会跑飞、可判定目标、五个崩法、plan build judge。English triggers: design an agent loop, write a loop, check a loop, loop review, prevent a runaway loop, goal-oriented loop, decidable goal, plan/build/judge.

affaan-m avataraffaan-m
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mailtrap-email-integration

mailtrap-email-integration

241Ktesting-qa

Guides agents through integrating transactional email sending via Mailtrap's Email API, including sandbox testing, domain verification, and API authentication. Use when implementing email-sending features, debugging delivery issues, or setting up safe dev/staging email testing.

affaan-m avataraffaan-m
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generating-python-installer

generating-python-installer

240Ktesting-qa

Commercial-grade Python installer expert for Windows: Nuitka extreme compilation, dist slimming, DLL footprint analysis, and Inno Setup packaging to ship the smallest, fastest installers. Use when a Python app must ship as a minimal, fast-starting Windows installer; not for basic script-to-exe conversion. 中文触发:Nuitka 极限优化、Python 商业打包、极限编译 Python、dist 瘦身、DLL 分析、最小安装包、最快启动、商业级打包风格

affaan-m avataraffaan-m
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benchmark-optimization-loop

benchmark-optimization-loop

240Ktesting-qa

Use when the user asks to make something faster, try many variants, run recursive optimization, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.

affaan-m avataraffaan-m
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terminal-ops

terminal-ops

240Ktesting-qa

Evidence-first repo execution workflow for ECC. Use when the user wants a command run, a repo checked, a CI failure debugged, or a narrow fix pushed with exact proof of what was executed and verified.

affaan-m avataraffaan-m
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csharp-testing

csharp-testing

240Ktesting-qa

C# and .NET testing patterns with xUnit, FluentAssertions, mocking, integration tests, and test organization best practices. Use when writing or reviewing xUnit tests, mocks, or integration tests in a C# / .NET project.

affaan-m avataraffaan-m
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hookify-rules

hookify-rules

240Ktesting-qa

This skill should be used when the user asks to create a hookify rule, write a hook rule, configure hookify, add a hookify rule, or needs guidance on hookify rule syntax and patterns.

affaan-m avataraffaan-m
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windows-desktop-e2e

windows-desktop-e2e

240Ktesting-qa

E2E testing for Windows native desktop apps (WPF, WinForms, Win32/MFC, Qt) using pywinauto and Windows UI Automation. Use when writing E2E tests for a Windows native desktop app with pywinauto or UI Automation.

affaan-m avataraffaan-m
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mle-workflow

mle-workflow

240Ktesting-qa

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

affaan-m avataraffaan-m
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quarkus-tdd

quarkus-tdd

240Ktesting-qa

Test-driven development for Quarkus 3.x LTS using JUnit 5, Mockito, REST Assured, Camel testing, and JaCoCo. Use when adding features, fixing bugs, or refactoring event-driven services.

affaan-m avataraffaan-m
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quarkus-verification

quarkus-verification

240Ktesting-qa

Verification loop for Quarkus projects: build, static analysis, tests with coverage, security scans, native compilation, and diff review before release or PR.

affaan-m avataraffaan-m
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fsharp-testing

fsharp-testing

240Ktesting-qa

F# testing patterns with xUnit, FsUnit, Unquote, FsCheck property-based testing, integration tests, and test organization best practices. Use when writing F# tests with xUnit, FsUnit, Unquote, or FsCheck.

affaan-m avataraffaan-m
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kotlin-patterns

kotlin-patterns

239Ktesting-qa

Idiomatic Kotlin patterns, best practices, and conventions for building robust, efficient, and maintainable Kotlin applications with coroutines, null safety, and DSL builders.

affaan-m avataraffaan-m
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regex-vs-llm-structured-text

regex-vs-llm-structured-text

239Ktesting-qa

Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.

affaan-m avataraffaan-m
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swift-protocol-di-testing

swift-protocol-di-testing

239Ktesting-qa

Protocol-based dependency injection for testable Swift code — mock file system, network, and external APIs using focused protocols and Swift Testing.

affaan-m avataraffaan-m
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bun-runtime

bun-runtime

239Ktesting-qa

Bun as runtime, package manager, bundler, and test runner. When to choose Bun vs Node, migration notes, and Vercel support.

affaan-m avataraffaan-m
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ai-regression-testing

ai-regression-testing

239Ktesting-qa

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.

affaan-m avataraffaan-m
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pytorch-patterns

pytorch-patterns

239Ktesting-qa

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

affaan-m avataraffaan-m
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kotlin-testing

kotlin-testing

239Ktesting-qa

Kotlin testing patterns with Kotest, MockK, coroutine testing, property-based testing, and Kover coverage. Follows TDD methodology with idiomatic Kotlin practices.

affaan-m avataraffaan-m
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