ML Ops
Machine learning operations
replicate-cli
by rawveg
This skill provides comprehensive guidance for using the Replicate CLI to run AI models, create predictions, manage deployments, and fine-tune models. Use this skill when the user wants to interact with Replicate's AI model platform via command line, including running image generation models, language models, or any ML model hosted on Replicate. This skill should be used when users ask about running models on Replicate, creating predictions, managing deployments, fine-tuning models, or working with the Replicate API through the CLI.
openrouter
by rawveg
OpenRouter API - Unified access to 400+ AI models through one API
mineru-extract
by blessonism
Use the official MinerU (mineru.net) parsing API to convert a URL (HTML pages like WeChat articles, or direct PDF/Office/image links) into clean Markdown + structured outputs. Use when web_fetch/browser can’t access or extracts messy content, and you want higher-fidelity parsing (layout/table/formula/OCR).
run-llms
by av
Guide for setting up and running local LLMs using Harbor. Use when user wants to run LLMs locally, set up Ollama, Open WebUI, llama.cpp, vLLM, or similar local AI services. Covers full setup from Docker prerequisites through running models, configuration, profiles, tunnels, and advanced features.
qlora
by itsmostafa
Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.
lora
by itsmostafa
Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.
pytorch
by itsmostafa
Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
rlhf
by itsmostafa
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
mlx
by itsmostafa
Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.
transformers
by itsmostafa
Loading and using pretrained models with Hugging Face Transformers. Use when working with pretrained models from the Hub, running inference with Pipeline API, fine-tuning models with Trainer, or handling text, vision, audio, and multimodal tasks.
iso-42001-ai-governance
by mastepanoski
AI governance audit using ISO 42001 standard. Ensures AI systems are developed and deployed responsibly with risk management, ethics, security, transparency, and compliance best practices.
agent-cost-optimizer
by adaptationio
Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs, preventing budget overruns, optimizing spend, measuring ROI, or ensuring cost-effective AI operations.
advanced-evaluation
by shipshitdev
Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.
voice-ai
by ScientiaCapital
"Production voice AI agents with sub-500ms latency. Groq LLM, Deepgram STT, Cartesia TTS, Twilio integration. No OpenAI. Use when: voice agent, phone bot, STT, TTS, Deepgram, Cartesia, Twilio, voice AI, speech to text, IVR, call center, voice latency."
business-model-canvas
by ScientiaCapital
"Business model design using Alexander Osterwalder's 9 building blocks. Use when: business model, canvas, value proposition, customer segments, revenue streams, startup planning, analyze business, business strategy."
openrouter-skill
by ScientiaCapital
"Orchestrate Chinese LLMs (DeepSeek, Qwen, Yi, Moonshot) through OpenRouter API with LangChain. Use when: openrouter, chinese llm, deepseek, qwen, moonshot, yi model, model routing, auto router, llm orchestration."
trading-signals
by ScientiaCapital
"Technical analysis patterns - Elliott Wave, Wyckoff, Fibonacci, Markov Regime, and Turtle Trading with confluence detection. Use when analyzing charts, identifying trading signals, or calculating technical levels."
runpod-deployment
by ScientiaCapital
"Deploy GPU workloads to RunPod serverless and pods - vLLM endpoints, A100/H100 setup, scale-to-zero, cost optimization. Use when: deploy to RunPod, GPU serverless, vLLM endpoint, scale to zero, A100 deployment, H100 setup, serverless handler, GPU cost optimization."
dojo-test
by dojoengine
Write tests for Dojo models and systems using spawn_test_world, cheat codes, and assertions. Use when testing game logic, verifying state changes, or ensuring system correctness.
Business Model Frameworks
by ThepExcel
q-educator
by TyrealQ
Course content development skill. Produces lecture outlines, demo outlines, student emails, assignment prompts, and per-group feedback using an interview-driven, projects-first teaching philosophy with domain-specific analogies. Use when developing or iterating on university course materials.
q-topic-finetuning
by TyrealQ
"Fine-tune and consolidate topic modeling outputs (BERTopic, LDA, etc.) into a theory-driven classification framework for academic manuscripts. Use when processing topic modeling results that need topic consolidation, theoretical classification, domain-specific preservation, multi-category handling, data verification, or Excel updates with final labels."
ai-pricing
by chadboyda
"When the user wants to price an AI product, choose a charge metric, design pricing tiers, or optimize margins. Also use when the user mentions 'AI pricing,' 'usage-based pricing,' 'consumption pricing,' 'outcome pricing,' 'BYOK,' 'bring your own key,' 'per-seat pricing,' 'pricing tiers,' 'AI margins,' 'cost per token,' or 'pricing model.' This skill covers pricing strategy, packaging, and margin management for AI-native products."
3d-cv-labeling-2026
by curiositech
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).