llm-ai
0
ai-auditability
Implementing comprehensive logging, tracking, and audit trails for AI systems to ensure compliance and enable debugging.
AmnadTaowsoam
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
llm-ai
0
creating-meta-prompts
Claude間のパイプライン用に最適化されたメタプロンプトを作成します。リサーチ、計画、実行の各段階を含む多段階ワークフローを設計します。複雑なタスクの段階的処理、Claude間の連携、構造化されたワークフローが必要な場合に使用してください。
sekka
data-ai
llm-ai
0
embedding-pipeline
Implement reusable embedding functions using Gemini embedding models via LangChain with proper error handling and batching for sitemap-crawled content.
nadeemsangrasi
data-ai
llm-ai
0
rag-chatbot-enhancement
Improves the RAG (Retrieval-Augmented Generation) chatbot for the Physical AI & Humanoid Robotics textbook with strict grounding, citation requirements, and performance optimization.
Fatima367
data-ai
machine-learning
0
finetune-llm
LLM fine-tuning 教練式引導工作流程 v2。 核心功能：主動探索使用者痛點、引導明確目標、多任務管理、資料來源追蹤、完整版本 lineage。 支援：LoRA/QLoRA/DoRA 微調、SFT/ORPO/DPO 對齊、資料準備、Benchmark 評估、HuggingFace 部署。 特色：教練式引導、可重現的資料管線、多任務版本追蹤。 觸發詞：「訓練模型」「fine-tune」「微調」「LoRA」「建立新任務」「改善模型」「優化準確率」「資料管線」「任務管理」
p988744
data-ai
llm-ai
0
moai-foundation-core
MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows
rdmptv
data-ai
llm-ai
0
strands-agent-architect
Design, build, deploy, and troubleshoot AI agents using AWS Strands Agents Framework (PRIMARY), Google ADK v1.0, A2A (Agent-to-Agent) protocols, and AWS Bedrock AgentCore. Use when creating new agents, implementing agent communication patterns, configuring AgentCore runtime, setting up RAG pipelines, integrating with Gemini 3.0, debugging deployment issues, or optimizing agent performance.
LPDigital-Agent
data-ai
machine-learning
0
evaluation-metrics
Automatically applies when evaluating LLM performance. Ensures proper eval datasets, metrics computation, A/B testing, LLM-as-judge patterns, and experiment tracking.
ricardoroche
data-ai
llm-ai
0
fabric-improve-prompt
Improve LLM prompts using prompt engineering best practices. Invoke when user wants to optimize prompts, improve slash commands, or apply prompt engineering.
theaj42
data-ai
llm-ai
0
using-claude-code-cli
Invoke Claude Code CLI from Python orchestrators and shell scripts. Use when asked to "spawn claude as subprocess", "automate claude cli", "run claude headless", "configure --allowedTools", "set up claude hooks", or "parallel claude invocation". Covers permissions, directory access (--add-dir), hooks, sandbox mode, and async patterns.
SpillwaveSolutions
data-ai
llm-ai
0
phase-intent
Phase 1 of assist workflow - Classify intent as CREATE, REFACTOR, or VERIFY
chkim-su
data-ai
llm-ai
0
gemini-3-pro-api
Gemini 3 Pro API/SDK integration for text generation, reasoning, and chat. Covers setup, authentication, thinking levels, streaming, and production deployment. Use when working with Gemini 3 Pro API, Python SDK, Node.js SDK, text generation, chat applications, or advanced reasoning tasks.
adaptationio
data-ai
llm-ai
0
validating-openai-api-implementations
Use when reviewing OpenAI API usage, answering questions about OpenAI endpoints, or validating implementations against the official API specification. Provides expert knowledge on chat completions, embeddings, models, audio, assistants, batch processing, and moderations endpoints. Essential for code reviews involving OpenAI integrations and determining if features/parameters are part of the official API.
bbrowning
data-ai
llm-ai
0
skill-creator
효과적인 SKILL을 제작하기 위한 가이드입니다. 특화된 지식, 워크플로우 또는 도구 통합을 통해 Claude의 기능을 확장하는 새로운 SKILL을 생성하거나 기존 SKILL을 업데이트하려는 경우 이 SKILL을 사용하세요.
icartsh
data-ai
llm-ai
0
rag-implementation
RAG (Retrieval Augmented Generation) implementation patterns including document chunking, embedding generation, vector database integration, semantic search, and RAG pipelines. Use when building RAG systems, implementing semantic search, creating knowledge bases, or when user mentions RAG, embeddings, vector database, retrieval, document chunking, or knowledge retrieval.
vanman2024
data-ai
llm-ai
0
example-skill
Describe when this skill should be invoked. Include trigger keywords, file patterns, and explicit use cases. The more specific, the better Claude can determine when to load this skill.
JaimeStill
data-ai
llm-ai
0
post-run-debrief
Converts telemetry and user notes into a structured reflection, confidence score, and next-step guidance.
nadavyigal
data-ai
llm-ai
0
memory
PMC memory system for persistent context across sessions. Store, search, and retrieve project knowledge using semantic vector search (OpenAI embeddings) and full-text keyword search. Use when: storing important context, searching for past solutions, retrieving project-specific knowledge, or building up institutional memory.
jayprimer
data-ai
llm-ai
0
claude-context
Carga el archivo CLAUDE_PROMPT.md como contexto para aplicar las instrucciones y mejores prácticas del proyecto. Úsalo cuando el usuario quiera aplicar las guías personalizadas, principios de trabajo, o cuando diga "load claude context", "usa el prompt", "aplica CLAUDE_PROMPT", "/claude" o "/bot".
vetacreativalaser
data-ai