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Found 430 Skills
Help users create custom batch image generation Skills through interactive Q&A. Users don't need to write code; they can generate fully functional image generation Skills just by answering questions. Triggered when users say "Help me create an image generation Skill", "I want to make an image matching Skill", "Create a batch image generation Skill", "How to make an image generation Skill", or "Help me make an AI image generation Skill". Supports any image scenarios such as article illustrations, Logo design, storyboards, social media images, posters, etc.
TDD feature build loop: spec (RED) → implement (GREEN) → refactor. Pass the feature name as argument.
Comprehensive quality assurance skill for validating and verifying project deliverables and educational content. This skill should be used when establishing quality standards, performing verification and validation activities, conducting reviews and audits, implementing continuous improvement processes, or ensuring deliverables meet requirements and stakeholder expectations. Provides systematic approaches to prevent defects and ensure excellence.
Generate test and suite specifications in the strict FinalRun YAML format. Handles automated test planning, folder grouping by feature, repo app configuration, environment-specific overrides in .finalrun/env/*.yaml, and validation via finalrun check.
Generate and curate evaluation datasets — structured generation via dimensions-tuples-NL, quick from description, expansion from existing data, plus dataset maintenance through deduplication, rebalancing, and gap-filling. Use when creating eval data, expanding test coverage, or cleaning datasets. Do NOT use when sufficient real production data exists (use analyze-trace-failures instead). Do NOT use for evaluator creation (use build-evaluator).
Create and deliver effective technical presentations, demos, and talks. Provides frameworks for structuring content, designing slides, and handling live demos.
Agent skill for tester - invoke with $agent-tester
Document Q&A with RAG using Supabase pgvector store.
Write, review, or debug end-to-end tests using Playwright. Use when asked to 'write e2e tests', 'add Playwright tests', 'test this user flow', 'fix flaky tests', 'create a test suite', or 'debug this e2e failure'. Invoke with /playwright-e2e or when user mentions e2e tests, Playwright, or test automation. Do NOT use for live browser interaction via MCP tools — use playwright-mcp for that. Do NOT use for unit/integration tests — use tdd-guide agent instead.
昇腾（Ascend）推理生态开源代码仓库智能问答专家旨在为 vLLM、vLLM-Ascend、MindIE-LLM、MindIE-SD、MindIE-Motor、MindIE-Turbo 以及 msModelSlim (MindStudio-ModelSlim) 等仓库提供专家级且易于理解的解释。在处理昇腾（Ascend）推理生态相关项目的用户询问时，务必触发此技能（Skill），可解答使用方法、部署流程、支持模型、支持特性、系统架构、配置管理、调试、测试、故障排查、性能优化、定制开发、源码解析以及其他技术问题。支持中英文双语回复，并可借助 deepwiki MCP 工具检索仓库知识库，生成具备上下文感知且基于证据的回答。Ascend inference ecosystem open-source code repository intelligent question-and-answer (Q&A) expert. Provide expert-level yet comprehensible explanations for repositories such as vLLM, vLLM-Ascend, MindIE-LLM, MindIE-SD, MindIE-Motor, MindIE-Turbo, and msModelSlim (MindStudio-ModelSlim). Use this skill when addressing user inquiries related to these Ascend inference ecosystem projects, including topics such as usage, deployment process, supported models, supported features, system architecture, configuration management, debugging, testing, troubleshooting, performance optimization, custom development, source code analysis, and any other technical issues about these projects. Support responses in both Chinese and English. Use deepwiki MCP tools to query repository knowledge bases and generate context-aware, evidence-based responses.
Create and run orq.ai experiments — compare configurations against datasets using evaluators, analyze results, and generate prioritized action plans. Use when evaluating LLM agents, deployments, conversations, or RAG pipelines end-to-end. Do NOT use without a dataset and evaluators. Do NOT use for cross-framework comparisons with external agents (use compare-agents).
Research GitHub, GitLab, and Bitbucket repositories using DeepWiki MCP server. Use when exploring unfamiliar codebases, understanding project architecture, or asking questions about how a specific open-source project works. Provides AI-powered repo analysis and RAG-based Q&A about source code. NOT for fetching library API docs (use fetching-library-docs instead) or local files.