所有 Skills

core-data-expert

293testing-qa

Expert Core Data guidance (iOS/macOS): stack setup, fetch requests & NSFetchedResultsController, saving/merge conflicts, threading & Swift Concurrency, batch operations & persistent history, migrations, performance, and NSPersistentCloudKitContainer/CloudKit sync.

avdlee

layers-conceptual-model

Techniques for defining the product's objects, relationships, states, and vocabulary independently of any interface — the most load-bearing layer

layers-intro

Framework orientation for Layers of Product Design — load this first; provides the context all other skills depend on

layers-surface

Techniques for auditing and deciding the surface against the layers below — vocabulary, object consistency, completeness, feedback, hierarchy, accessibility

layers-product-strategy

293testing-qa

Techniques for connecting user opportunities to business outcomes and solution bets, and testing the riskiest assumptions cheaply

layers-orient

Diagnostic audit across all seven layers — identifies the bottleneck layer and recommends where to focus

layers-interaction-flow

Techniques for mapping interaction structure and flow — places, affordances, edge cases, and failure paths — without committing to visual form

llm-security

293agent-workflows

Security guidelines for LLM applications based on OWASP Top 10 for LLM 2025. Use when building LLM apps, reviewing AI security, implementing RAG systems, or asking about LLM vulnerabilities like 'prompt injection' or 'check LLM security'. IMPORTANT: Always consult this skill when building chatbots, AI agents, RAG pipelines, tool-using LLMs, agentic systems, or any application that calls an LLM API (OpenAI, Anthropic, Gemini, etc.) — even if the user doesn't explicitly mention security. Also use when users import 'openai', 'anthropic', 'langchain', 'llamaindex', or similar LLM libraries.

semgrep

leanspec

293writing-content

The spec-coding methodology for AI-assisted development. Use when planning features, creating/refining/implementing/verifying specs, or organising a project. Works with whatever spec backend your team already uses — local markdown, GitHub Issues, Azure DevOps, Jira — by delegating platform-specific details to a LeanSpec adapter.

codervisor

vitest-midscene-e2e

292testing-qa

Enhances Vitest with Midscene for AI-powered UI testing across Web (Playwright), Android (ADB), and iOS (WDA). Scaffolds new projects, converts existing projects, and creates/updates/debugs/runs E2E tests using natural-language UI interactions. Triggers: write test, add test, create test, update test, fix test, debug test, run test, e2e test, midscene test, new project, convert project, init project, 写测试, 加测试, 创建测试, 更新测试, 修复测试, 调试测试, 运行测试, 新建工程, 转化工程.

web-infra-dev

csv-excel-merger

291productivity

Merge multiple CSV/Excel files with intelligent column matching, data deduplication, and conflict resolution. Handles different schemas, formats, and combines data sources. Use when users need to merge spreadsheets, combine data exports, or consolidate multiple files into one.

onewave-ai

screenshot-to-code

291frontend

Convert UI screenshots into working HTML/CSS/React/Vue code. Detects design patterns, components, and generates responsive layouts. Use this when users provide screenshots of websites, apps, or UI designs and want code implementation.

onewave-ai

html-slides

290mcp-integrations

使用 reveal.js 创建惊艳的 HTML 演示文稿。

claude-office-skills

ppt-visual

290design-ui

设计演示文稿视觉和幻灯片布局。创建视觉概念，推荐图形，并为有影响力的PowerPoint幻灯片提供设计规范。

claude-office-skills

deep-research

289research-knowledge

Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.

lingzhi227

databricks-core

289database

Databricks CLI 操作以及 Databricks CLI 使用的父级/入口技能：身份验证、配置文件选择和 bundle。对于 CLI、身份验证、配置文件和 bundle 任务，请先加载此技能，然后加载匹配的产品技能。对于查找或探索数据、回答有关数据的问题或生成 SQL，请加载 databricks-data-discovery 技能（它会路由到 Genie One）。包含 Databricks 相关 CLI 任务的最新指南。

databricks

data-analysis

288research-knowledge

Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.

lingzhi227

ralph-wiggum

288agent-workflows

Autonomous AI coding with spec-driven development. Implements Geoffrey Huntley's iterative bash loop methodology where agents work through specs one at a time, outputting a completion signal only when acceptance criteria are 100% met.

fstandhartinger

cross-border-ecommerce

286marketing-seo

Cross-border e-commerce expansion advisor. Scores target markets on 8 weighted dimensions (market size, ecommerce penetration, competition, regulatory complexity, logistics infrastructure, payment ecosystem, cultural distance, IP protection), compares 5 fulfillment models with cost and transit data, provides country-by-country tax/duty compliance guides (EU VAT/IOSS, UK VAT, US sales tax, CA GST, AU GST, JP consumption tax), maps local payment preferences by market, and builds a phased expansion roadmap. No API key required.

nexscope-ai

skill-doctor

286agent-workflows

Grades agent skills by scoring agent conversations against efficiency and code-quality rubrics, then drafts concrete skill edits and a shareable report. Use when the user wants their agent setup graded from real conversation history, or asks which of their installed skills are actually working.

warpdotdev

math-reasoning

285research-knowledge

Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.

lingzhi227