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Found 5,159 Skills
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. This skill covers the Interactions API, the recommended way to use Gemini models and agents in Python and TypeScript.
CRITICAL: Use for writing and editing agent-spec .spec files. Triggers on: write spec, create spec, edit spec, new spec, spec authoring, task contract, .spec file, BDD scenario, acceptance criteria, completion criteria, test selector, boundary, constraint, intent, decision, out of scope, "how to write a spec", "spec format", "spec syntax", "contract quality", 写 spec, 创建规格, 编辑合约, 任务合约, 验收标准, 完成条件, BDD 场景, 测试选择器, 约束, 意图, 决策, 边界, 排除范围, "怎么写 spec", "spec 格式", "spec 语法", "合约质量"
The complete AI web agency toolkit. One skill to run a full client website project — from intake to design to build to deploy. Orchestrates sub-skills and sub-agents for fast, high-quality delivery.
Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.
Modern TypeScript patterns your AI agent should use. Strict mode, discriminated unions, satisfies operator, const assertions, and type-safe patterns for TypeScript 5.x.
Integration patterns for Mapbox MCP Server in AI applications and agent frameworks. Covers runtime integration with pydantic-ai, mastra, LangChain, and custom agents. Use when building AI-powered applications that need geospatial capabilities.
This skill is used when the user requests 'review my prompt', 'analyze my conversation history', 'diagnose my understanding level', or when it is invoked via /prompt-review. It reads past AI Agent conversation histories (Claude Code, GitHub Copilot Chat, Cline, Roo Code, Windsurf, Antigravity), estimates the user's technical understanding level, prompting patterns and AI dependency, then generates a corresponding report.
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
Creates VS Code custom agent files (.agent.md) for specialized AI personas with tools, instructions, and handoffs. Use when scaffolding new custom agents, configuring agent workflows, or setting up agent-to-agent handoffs.
Form a committee of two high-reasoning agents to step back, do root cause analysis, and produce a plan. Use when stuck, looping, tunnel-visioning, or facing a hard planning problem.
Instrument a Java application with the Elastic Distribution of OpenTelemetry (EDOT) Java agent for automatic tracing, metrics, and logs. Use when adding observability to a Java service that has no existing APM agent.
Migrate a Java application from the classic Elastic APM Java agent to the EDOT Java agent. Use when switching from elastic-apm-agent.jar to elastic-otel-javaagent.jar.