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Found 13,568 Skills
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
Generates beautiful, consistent Preline Theme CSS files. Agent interprets user request, runs build script, delivers complete CSS.
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration
Build applications with the Letta API — a model-agnostic, stateful API for building persistent agents with memory and long-term learning. Covers SDK patterns for Python and TypeScript. Includes 24 working code examples.
This skill should be used when users want to route LLM requests to different AI providers (OpenAI, Grok/xAI, Groq, DeepSeek, OpenRouter) using SwiftOpenAI-CLI. Use this skill when users ask to "use grok", "ask grok", "use groq", "ask deepseek", or any similar request to query a specific LLM provider in agent mode.
Use when encountering bugs or test failures - systematic debugging using debuggers, internet research, and agents to find root cause before fixing
Create custom tools using the @tool decorator for domain-specific agents. Use when building agent-specific tools, implementing MCP servers, or creating in-memory tools with the Agent SDK.
Orchestrate the full Platonic Coding workflow from conceptual design to RFC specs, implementation guides, code implementation, and spec-compliance review. Always shows current phase; uses interactive chat in Phase 0, invokes platonic-specs in Phase 1, platonic-impl-guide in Phase 2, coding agents in Phase 3, and platonic-code-review in Phase 4.
Create new agent skills following the Agent Skills specification. Use when asked to "create a skill", "add a new skill", "write a skill", "make a skill", "build a skill", or scaffold a new skill with SKILL.md. Guides through requirements, writing, registration, and verification.
Extracts key learnings from conversations, debugging sessions, and failed attempts. Use at session end or after solving complex problems to capture insights. Stores discoveries in memory (via amplihack.memory.discoveries), suggests PATTERNS.md updates, and recommends new agent creation. Ensures knowledge persists across sessions via Kuzu memory backend.
Maintains awareness across sessions. Spawns observer agent on start, loads context, notifies of evolution opportunities.
This skill should be used when users request comprehensive, in-depth research on a topic that requires detailed analysis similar to an academic journal or whitepaper. The skill conducts multi-phase research using web search and content analysis, employing high parallelism with multiple subagents, and produces a detailed markdown report with citations.