llm-ai
420
worktree-spawn
Spawn an independent Claude instance in a git worktree to work on a task in parallel. Use when the user wants to delegate a task to run independently while continuing the current conversation.
AztecProtocol
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
llm-ai
419
add-provider
Guide for adding new AI providers to ClaudeBar using TDD patterns. Use this skill when: (1) Adding a new AI assistant provider (like Antigravity, Cursor, etc.) (2) Creating a usage probe for a CLI tool or local API (3) Following TDD to implement provider integration (4) User asks "how do I add a new provider" or "create a provider for X"
tddworks
data-ai
llm-ai
411
session-management
Context preservation, tiered summarization, resumability
alinaqi
data-ai
llm-ai
411
agentic-development
Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
alinaqi
data-ai
llm-ai
411
llm-patterns
AI-first application patterns, LLM testing, prompt management
alinaqi
data-ai
llm-ai
404
claudish-usage
CRITICAL - Guide for using Claudish CLI ONLY through sub-agents to run Claude Code with any AI model (OpenRouter, Gemini, OpenAI, local models). NEVER run Claudish directly in main context unless user explicitly requests it. Use when user mentions external AI models, Claudish, OpenRouter, Gemini, OpenAI, Ollama, or alternative models. Includes mandatory sub-agent delegation patterns, agent selection guide, file-based instructions, and strict rules to prevent context window pollution.
MadAppGang
data-ai
llm-ai
385
agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
ruvnet
data-ai
llm-ai
376
prompt-mastery
Advanced LLM prompt engineering expertise for crafting highly effective prompts, system messages, and tool descriptions with Claude-specific techniques
taylorsatula
data-ai
llm-ai
365
youtube-digest
This skill should be used when the user asks to "유튜브 정리", "영상 요약", "transcript 번역", "YouTube digest", "영상 퀴즈", or provides a YouTube URL for analysis. Extracts transcript, generates summary/insights/Korean translation, and tests comprehension with 9 quiz questions across 3 difficulty levels. Optional Deep Research for web-based follow-up.
team-attention
data-ai
llm-ai
365
clarify
This skill should be used when the user asks to "clarify requirements", "refine requirements", "specify requirements", "what do I mean", "make this clearer", or when the user's request is ambiguous and needs iterative questioning to become actionable. Also trigger when user says "clarify", "/clarify", or mentions unclear/vague requirements.
team-attention
data-ai
llm-ai
365
session-analyzer
This skill should be used when the user asks to "analyze session", "세션 분석", "evaluate skill execution", "스킬 실행 검증", "check session logs", "로그 분석", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.
team-attention
data-ai
llm-ai
365
history-insight
This skill should be used when user wants to access, capture, or reference Claude Code session history. Trigger when user says "capture session", "save session history", or references past/current conversation as a source - whether for saving, extracting, summarizing, or reviewing. This includes any mention of "what we discussed", "today's work", "session history", or when user treats the conversation itself as source material (e.g., "from our conversation").
team-attention
data-ai
llm-ai
362
prompt-factory
World-class prompt powerhouse that generates production-ready mega-prompts for any role, industry, and task through intelligent 7-question flow, 69 comprehensive presets across 15 professional domains (technical, business, creative, legal, finance, HR, design, customer, executive, manufacturing, R&D, regulatory, specialized-technical, research, creative-media), multiple output formats (XML/Claude/ChatGPT/Gemini), quality validation gates, and contextual best practices from OpenAI/Anthropic/Google. Supports both core and advanced modes with testing scenarios and prompt variations.
alirezarezvani
data-ai
llm-ai
358
agent-development
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
fcakyon
data-ai
llm-ai
358
setup
This skill should be used when user asks to "configure statusline", "setup statusline", "show context usage", "display token count", "5H usage", "time until reset", "statusline colors", "statusline not working", or wants to change how the Claude Code status bar displays information.
fcakyon
data-ai
llm-ai
348
context-management
在多轮对话和多 Agent 协作场景下，帮助模型管理指令、项目状态和长上下文。通过外部文档与会话笔记实现可控的“记忆”、指令冲突检测和高质量交接。适用于任务跨多次调用、跨 Agent、需要稳定行为规范时使用。
No-Trade-No-Life
data-ai
llm-ai
318
growth-hacker
Generate growth strategies and ready-to-publish marketing content for product growth. Use when planning launches, assessing product-market fit, creating marketing content, designing experiments, optimizing activation/onboarding, or developing acquisition strategies. Incorporates proven frameworks from Sean Ellis (PMF testing, ICE prioritization) and Nikita Bier (viral consumer growth, activation optimization). Covers product-market fit assessment, activation optimization, viral loops, SEO, email marketing, social media, landing pages, and growth experiments.
kasperjunge
data-ai
llm-ai
304
system-prompt-writer
This skill should be used when writing or improving system prompts for AI agents, providing expert guidance based on Anthropic's context engineering principles.
aws-samples
data-ai
llm-ai
303
mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
khaneliman
data-ai