Découvrez les skills devops llm ai, comparez leurs dépôts et consultez leurs instructions.
Setup Sentry AI Agent Monitoring in any project. Use this when asked to add AI monitoring, track LLM calls, monitor AI agents, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI. Automatically dete...
Use when explicitly asked to run the code-reviewer subagent or when another skill requires the code-reviewer agent card.
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcp__memory knowledge graph for persistent facts
Use when explicitly asked to run the security-reviewer subagent or when another skill requires the security-reviewer agent card.
Use when the user wants to design Codex agent equivalents (specialized workers/profiles/prompt files), define triggering conditions, or build reusable agent prompts and validation tools.
Progressive Code-Mode MCP integration for Factory Droid. Discover tools incrementally, hydrate schemas on demand, and run procedural workflows that call MCP tools outside the LLM loop.
Auto-loads when CLAUDE_CODE_TEAM_NAME environment variable is set. Provides guidance for Claude Code teammates working in swarms - identity awareness, communication patterns, task management, and coordination p...
Text processing, sentiment analysis, LLMs, and NLP frameworks. Use for text classification, named entity recognition, or language models.
Q-learning, DQN, PPO, A3C, policy gradient methods, multi-agent systems, and Gym environments. Use for training agents, game AI, robotics, or decision-making systems.
Image processing, object detection, segmentation, and vision models. Use for image classification, object detection, or visual analysis tasks.
Quantization, pruning, AutoML, hyperparameter tuning, and performance optimization. Use for improving model performance, reducing size, or automated ML.
Complete pre-production workflow that chains research, project creation, script generation, and presentation in one seamless flow. Use when starting a new video from competitor research all the way through to a...
Create new Claude Code skills with proper SKILL.md format, frontmatter, and best practices. Use when building, scaffolding, or generating a new skill for Claude Code.
LLM 모델 자동 업데이트 시스템 - 항상 최신 모델 정보 유지
生成、维护、修剪AICTXT文档，保持在CRAFT大小限制内。当AICTXT创建和更新时使用。
This skill provides guidance for implementing robust Qdrant vector database integration with RAG (Retrieval Augmented Generation) systems, including proper async client handling, error management, and performan...
Extract and consolidate key learnings, insights, and actionable takeaways from the current conversation session. Use when the user wants to capture, summarize, or document what was learned during the chat, crea...
Always use this skill to provide context on the codebase. Automatically initializes and references the Speculate documentation framework (coding rules, shortcuts, templates, and project specs) to ensure agents...
Augmented cognition layer that makes users smarter by connecting conversations to their persistent knowledge tree. Use proactively when topics arise that might have prior knowledge, and when users ask to rememb...
Analyze AI coding session telemetry for usage patterns, token efficiency, and workflow optimization. Supports current session or historical time periods.
Compact the conversation context by summarizing what was accomplished and updating CLAUDE.md with any learnings. Use when context is getting long or when transitioning between work sessions.
Research and generate Azure deployment guide with EasyAuth (Google + Facebook) and CosmosDB. This skill searches for current documentation and screenshots to produce an up-to-date setup guide at .docs/azure-set...
Focused task planning for ONE specific idea. Uses narrow→broad pattern - starts at user's focus point, expands context only as needed. Creates detailed task via task-board skill.
Expert knowledge for building AI agents with Argentic - a Python microframework for async MQTT-based agents with multi-LLM support, custom tools, and multi-agent orchestration
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.