python-debug
Use when you need to inspect detailed execution flow of a Python program, stepping through functions and inspecting variable contents with pdb. Triggers include requests to debug Python scripts, trace execution...
发现 测试与安全 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。
Use when you need to inspect detailed execution flow of a Python program, stepping through functions and inspecting variable contents with pdb. Triggers include requests to debug Python scripts, trace execution...
Summarize CPython test failures from cpython_full_test_run.log (and cpython_full_test_run_summary.txt if present), compute passed/total counts for test files and test cases, and group failures by likely root ca...
Capture a quick, reproducible snapshot of Docker/Compose logs and container state for debugging. Use when smoke tests fail or you need attachable evidence.
Bring up a Docker Compose stack reliably and verify basic service/container status. Use for local smoke tests, live testing, and staging-like bring-up.
Run quick HTTP health/readiness smoke checks against one or more endpoints. Use to validate a service is up before running deeper E2E tests.
Smoke test an audio upload endpoint using multipart/form-data. Use for quick end-to-end validation of transcription pipelines.
Generate a tiny non-sensitive WAV file using only the Python standard library. Use for deterministic smoke tests without committing real user audio.
Assert CPU-only runtime inside a container using PyTorch (torch.cuda.is_available()==False) and optional env var checks. Use to prevent accidental GPU execution during CPU smoke tests.
Quick scan for version strings across common files (README, pyproject, requirements, package manifests) to avoid release blockers from inconsistent versions. Use before tagging or publishing.
Run analyzer MCP tools (Ruff + optional dead-code scan) as a quality gate before QA/UAT/release.
Run Python pytest suites and capture reproducible evidence (command, environment, output) into agent-output artifacts. Use for QA verification, debugging, and pre-release confidence.
Create comprehensive implementation plans using Codex (GPT 5.2) for complex features. Use when user says "use codex plan mode", "codex plan", "plan with codex", or wants detailed implementation planning with fi...
Patterns for delegating tasks to Codex CLI (GPT 5.2) experts including bug analysis, code review, and plan revision via the mcp__codex__codex tool.
Conduct online research with source attribution, Notion integration, and persistent memory. Organizes findings by project in research/ folder.
TypeScript patterns for types, interfaces, and type guards in viya-app. Use when defining types, working with generated API types, or creating type-safe utilities.
Update and maintain GitHub issues during development. Add progress comments, update project board status, handle blockers, link PRs, and close issues with summaries. Use when user asks to update an issue, chang...
PR workflow, release notes, build management, and GitHub conventions. Use when creating PRs, writing commit messages, or preparing releases.
Start Penpot MCP servers and work with designs. Enables AI-assisted design workflows with Penpot.
Write product strategy documents for Viya TMS product teams. Uses Playing to Win framework, cascading from company strategy to team-level strategic choices.
Headless browser debugging for QA failures and visual testing. Use when investigating UI bugs, taking screenshots, or inspecting DOM and network requests.
Create and improve product documentation for Viya TMS. Supports user guides, feature docs, API references, and release notes with consistent style and structure.
Writing clear, user-facing documentation for non-technical users. Use when creating in-app docs, help content, or feature documentation.
Senior engineer code review patterns for Vue/TypeScript projects. Use when reviewing PRs, verifying checklists, or writing release notes.
Understanding viya-app project structure and where to find things. Use when exploring the codebase, finding files, or understanding how the app is organized.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。