Expert-level AI system design, MLOps, architecture patterns, and AI infrastructure — from personamanagmentlayer/pcl
Expert-level AI system design, MLOps, architecture patterns, and AI infrastructure — from majiayu000/claude-skill-registry
Expert-level AI implementation, deployment, LLM integration, and production AI systems — from personamanagmentlayer/pcl
Expert-level AI implementation, deployment, LLM integration, and production AI systems — from majiayu000/claude-skill-registry
Create technical specifications, ADRs, and project context documentation that prevents technical debt.
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical…
Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits.
Retrieval-Augmented Generation systems, vector databases, embedding strategies, and production RAG architectures for enterprise LLM applications.
Batch embedding generation with caching, rate limiting, and multiple provider support
RAG systems analyst and architect skill. Collects and clarifies requirements through structured dialogue, then transforms unstructured business or developer descriptions into…
Seldon Core deployment skill for model serving, A/B testing, and canary deployments on Kubernetes.
Use when explaining code, concepts, patterns, or architecture: engineer-grade technical explanations with 3-tier depth control, ASCII diagrams, and execution traces.
Use when documenting an incident, outage, or production failure using the structured DERP model (Detection, Escalation, Recovery, Prevention).
Use when optimizing prompts, skill descriptions, or agent instructions for clarity, specificity, and behavioral effectiveness. — from majiayu000/claude-skill-registry
Audit a data pipeline for Veracity and Value. Dispatches data-scientist, compliance-auditor, and data-engineer agents with project context injected at dispatch time.
OneKey TradingView app bridge guide. Use when changing or debugging TradingView/WebView/iframe communication, chart URL params, kline/history/realtime messages, marks, Hyperliquid…
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion.
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion.
Create your LLMOps data engineering skill in one prompt, then learn to improve it throughout the chapter — from panaversity/claude-code-skills-lab
Agent-to-Agent (A2A) protocol — Google 2025, 150+ org backing. Agent Cards discovery, task lifecycle (submitted→working→completed), artifacts (text/structured/video), opaque task…
Guide for adding a new model to the Archon engine. Use when user wants to add support for a new HuggingFace model architecture in ArchonEngine.
Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.
Build a task-aware context bundle (relevant code + applicable standards + related past decisions) via the local RAG index, capped at a token budget.
Complete engineering management system — team building, 1:1s, performance, hiring, architecture decisions, incident management, and scaling.
Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems.
Expert AI engineer specializing in AI system design, model implementation, and production deployment.
Exposes Hermes self-learning architecture to allow CEO Kit agents to autonomously build new scripts (SKILL.md) and fine-tune their base model weights.
Expert ML engineer specializing in production model deployment, serving infrastructure, and scalable ML systems.
Expert ML engineer specializing in machine learning model lifecycle, production deployment, and ML system optimization.
Expert ML engineer specializing in machine learning model lifecycle, production deployment, and ML system optimization.
Production deployment and operationalization of AI agents on Databricks. Use when deploying agents to Model Serving, setting up MLflow logging and tracing for agents, implementing…
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model…
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK.
Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs.
Deterministic verification gate for agent task close-out. Reads scope contract, rule report, feedback log, and diff — emits a single verification_report.json verdict.
MASFT taxonomy of multi-agent failure modes (Berkeley 2025) — 14 modes in 3 categories. Five industry-recurring modes: hallucinated actions, scope creep, cascading errors, context…
Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory…
Decision SOP for serving LLMs with vLLM. Covers PagedAttention mental model, quantization/parallelism/batching tradeoffs, OOM triage, and when NOT to use vLLM.
Load-bearing design decisions for this repo as a contract you check before changing anything. Covers the asymmetric generation seam, source-of-truth per tree, hook runtime failure…
Lightweight playbook distilled from AI Architecture to keep dual-engine memory (.ai_context) and manifest dispatcher with minimal overhead; use when bootstrapping or porting the…
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI int — from…
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations.
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations.
Production-grade AI engineering for LLM applications, RAG systems, and agent architectures. Use when building AI features, designing model integrations, or implementing AI safety…
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI int — from…
MASTER AI: LLM Apps, Advanced RAG, Agents (ReAct/Plan), Prompting (CoT/Few-shot), LangGraph, VectorDBs, RAGAS Eval. Use for ANY AI/LLM task.
Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems.
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI int — from…
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications.
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications.
Authoritative source of truth on AI tooling and how to choose it - the knowledge behind building AI agents and AI systems.
Structured AI engineering curriculum — 382 skills + 99 prompts across 20 phases covering ML, deep learning, LLMs, agents, and production systems.
Practical guide for building production ML systems based on Chip Huyen's AI Engineering book. Use when users ask about model evaluation, deployment strategies, monitoring, data…
AI Engineering Hub — catálogo de proyectos y tutoriales de ingeniería de IA con Jupyter notebooks.
Claude ajan altyapısı — /ai-upgrade (repo kataloğu, araç yönetimi) + /ai-metodoloji (çalışma kalitesi denetimi). Stack bağımsız, her projede çalışır.
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval…
The write path for the ai-engineering tooling corpus - keeps it current, deduplicated, and honest about what we have actually seen. Three jobs.
AI gateways for LLM serving — provider routing, fallback, retries, rate limiting, secrets, observability, guardrails.
Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation, audio transcription,…
Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation, fine-tuning, and O — from…