llm-ai
8.6K
add-ai-integration
Add a new AI provider integration to the Sentry JavaScript SDK. Use when contributing a new AI instrumentation (OpenAI, Anthropic, Vercel AI, LangChain, etc.) or modifying an existing one.
getsentry
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
llm-ai
8.6K
asset-spec
Generate per-asset visual specifications and AI generation prompts from GDDs, level docs, or character profiles. Produces structured spec files and updates the master asset manifest. Run after art bible and GDD/level design are approved, before production begins.
Donchitos
data-ai
llm-ai
8.3K
memos-memory-guide
Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Available tools: memory_search, memory_get, memory_write_public, memory_share, memory_unshare, task_summary, skill_get, skill_search, skill_install, skill_publish, skill_unpublish, network_memory_detail, network_skill_pull, network_team_info, memory_timeline, memory_viewer.
MemTensor
data-ai
llm-ai
8.1K
higress-openclaw-integration
Deploy and configure Higress AI Gateway for OpenClaw integration. Use when: (1) User wants to deploy Higress AI Gateway, (2) User wants to configure OpenClaw to use more model providers, (3) User mentions 'higress', 'ai gateway', 'model gateway', 'AI网关', (4) User wants to set up model routing or auto-routing, (5) User needs to manage LLM provider API keys.
alibaba
data-ai
llm-ai
8.1K
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
Jeffallan
data-ai
llm-ai
8.1K
rag-architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.
Jeffallan
data-ai
llm-ai
7.5K
comprehensive-research-agent
Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls
muratcankoylan
data-ai
llm-ai
7.3K
half-clone
Clone the later half of the current conversation, discarding earlier context to reduce token usage while preserving recent work.
ykdojo
data-ai
llm-ai
7K
app-builder
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
vudovn
data-ai
llm-ai
6.9K
andrej-karpathy-perspective
Andrej Karpathy的思维框架与表达方式。基于20+篇博文、16段深度访谈、100+条X帖子的系统蒸馏， 提炼6个核心心智模型、8条决策启发式、完整的中文输出适配和经典句式速查。 用途：作为思维顾问，用Karpathy的视角分析AI技术可靠性、学习方法、行业趋势、产品设计。 当用户提到「用Karpathy的视角」「Karpathy会怎么看」「卡帕西」「karpathy模式」时使用。 也适用于：Software 2.0/3.0讨论、vibe coding话题、神经网络训练、AI炒作判断、LLM能力边界。 即使用户只是说「从工程现实主义角度」「march of nines」「构建即理解」「锯齿状智能」也可触发。 不在用户只是普通问AI相关问题时触发——只在明确想要Karpathy式思维框架时激活。
alchaincyf
data-ai
llm-ai
6.9K
naval-perspective
Naval Ravikant的思维操作系统。基于著作、播客、推文、决策记录和外部批评的深度调研， 提炼5个核心心智模型、8条决策启发式和完整的表达DNA。 激活后沉浸式扮演Naval，直接以「我」的视角回应问题。 当用户提到「用Naval的视角」「Naval会怎么看」「纳瓦尔模式」「Naval perspective」「切换到Naval」时使用。 即使用户只是说「这份工作有杠杆吗」「什么是specific knowledge」「什么是真正的财富」「欲望太多怎么办」「无需许可的路径」也可触发。 不要在用户只是问「我该怎么选择」「帮我想清楚」等一般性决策问题时触发——只在涉及杠杆/特定知识/欲望管理/财富定义等Naval核心概念时激活。
alchaincyf
data-ai
llm-ai
6.9K
zhang-yiming-perspective
张一鸣（字节跳动/TikTok创始人）的思维框架与表达方式。基于6个维度（著作、深度访谈、 表达DNA、他者视角、决策记录、时间线）的调研，涵盖32个访谈片段、12个重大决策案例， 提炼5个核心心智模型、7条决策启发式和完整的表达DNA。 用途：作为思维顾问，用张一鸣的视角分析产品、组织、全球化、人才和个人成长问题。 当用户提到「用张一鸣的视角」「张一鸣会怎么看」「一鸣的思路」「zhang yiming perspective」时使用。 即使用户只是说「帮我用张一鸣的角度想想」「如果是字节会怎么做」「切换到张一鸣」也应触发。 即使用户说「字节怎么看」「头条的逻辑」「一鸣怎么选择」「一鸣」也应触发。
alchaincyf
data-ai
llm-ai
6.6K
generate-image
Generate or edit images using AI models (FLUX, Gemini). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that is not a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.
K-Dense-AI
data-ai
llm-ai
6.6K
stable-baselines3
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
K-Dense-AI
data-ai
llm-ai
6.6K
pufferlib
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
K-Dense-AI
data-ai
llm-ai
6.6K
autoresearch
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
Orchestra-Research
data-ai
llm-ai
6.6K
prompt-guard
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
Orchestra-Research
data-ai
llm-ai
6.6K
evolving-ai-agents
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
Orchestra-Research
data-ai
llm-ai
6.6K
crewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
Orchestra-Research
data-ai