statistical-analysis
K-Dense-AIGuided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
statsmodels
K-Dense-AIStatistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
hugging-face-model-trainer
huggingfaceThis skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
mcp-management
mrgoonieManage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
agent-development
openclawDesign and build custom Claude Code agents with effective descriptions, tool access patterns, and self-documenting prompts. Covers Task tool delegation, model selection, memory limits, and declarative instruction design. Use when: creating custom agents, designing agent descriptions for auto-delegation, troubleshooting agent memory issues, or building agent pipelines.
customize
microsoftInteractive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
preset
microsoftIntelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
openrouter-routing-rules
Implement intelligent model routing based on request characteristics. Use when optimizing for cost, speed, or quality per request. Trigger with phrases like 'openrouter routing', 'model selection', 'smart routing', 'dynamic model'.
engineering-features-for-machine-learning
This skill empowers Claude to perform feature engineering tasks for machine learning. It creates, selects, and transforms features to improve model performance. Use this skill when the user requests feature creation, feature selection, feature transformation, or any request that involves improving the features used in a machine learning model. Trigger terms include "feature engineering", "feature selection", "feature transformation", "create features", "select features", "transform features", "improve model performance", and similar phrases related to feature manipulation.
perplexity-multi-env-setup
Configure Perplexity Sonar API across development, staging, and production environments. Use when setting up multi-environment search integrations, managing model selection per environment, or controlling cost through sonar vs sonar-pro routing by env. Trigger with phrases like "perplexity environments", "perplexity staging", "perplexity dev prod", "perplexity environment setup", "perplexity model by env".
modeling-nosql-data
This skill enables Claude to design NoSQL data models. It activates when the user requests assistance with NoSQL database design, including schema creation, data modeling for MongoDB or DynamoDB, or defining document structures. Use this skill when the user mentions "NoSQL data model", "design MongoDB schema", "create DynamoDB table", or similar phrases related to NoSQL database architecture. It assists in understanding NoSQL modeling principles like embedding vs. referencing, access pattern optimization, and sharding key selection.
openrouter-model-routing
Implement advanced model routing with A/B testing. Use when optimizing model selection or running experiments. Trigger with phrases like 'openrouter a/b test', 'model experiment', 'openrouter routing', 'model comparison'.
cursor-model-selection
Configure and select AI models in Cursor. Triggers on "cursor model", "cursor gpt", "cursor claude", "change cursor model", "cursor ai model". Use when working with cursor model selection functionality. Trigger with phrases like "cursor model selection", "cursor selection", "cursor".
openrouter-multi-provider
Execute work with multiple providers through OpenRouter. Use when comparing providers or building provider-agnostic systems. Trigger with phrases like 'openrouter providers', 'openrouter multi-model', 'compare models', 'provider selection'.
mistral-cost-tuning
Optimize Mistral AI costs through model selection, token management, and usage monitoring. Use when analyzing Mistral billing, reducing API costs, or implementing usage monitoring and budget alerts. Trigger with phrases like "mistral cost", "mistral billing", "reduce mistral costs", "mistral pricing", "mistral expensive", "mistral budget".
external-model-selection
MadAppGangChoose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via Claude, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.
agent-creator
hotovoCreate and configure AiderDesk agent profiles by defining tool groups, approval rules, subagent settings, and provider/model selection. Use when setting up a new agent, creating a profile, or configuring agent tools and permissions.
claudish-usage
MadAppGangCRITICAL - Guide for using Claudish CLI ONLY through sub-agents to run Claude Code with any AI model (OpenRouter, Gemini, OpenAI, local models). NEVER run Claudish directly in main context unless user explicitly requests it. Use when user mentions external AI models, Claudish, OpenRouter, Gemini, OpenAI, Ollama, or alternative models. Includes mandatory sub-agent delegation patterns, agent selection guide, file-based instructions, and strict rules to prevent context window pollution.
agent-development
jezwebDesign and build custom Claude Code agents with effective descriptions, tool access patterns, and self-documenting prompts. Covers Task tool delegation, model selection, memory limits, and declarative instruction design. Use when: creating custom agents, designing agent descriptions for auto-delegation, troubleshooting agent memory issues, or building agent pipelines.
sadd:do-in-parallel
NeoLabHQLaunch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection and quality-focused prompting
sadd:launch-sub-agent
NeoLabHQLaunch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
sadd:do-in-steps
NeoLabHQExecute complex tasks through sequential sub-agent orchestration with intelligent model selection, and LLM-as-a-judge verification
thermodynamic-model-selector
a5c-aiAutomated thermodynamic property method selection based on component characteristics and operating conditions
embedding-optimization
ancolemanOptimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
bio-phylo-modern-tree-inference
GPTomicsBuild maximum likelihood phylogenetic trees using IQ-TREE2 and RAxML-ng. Use when inferring publication-quality trees with model selection, ultrafast bootstrap, or partitioned analyses from sequence alignments.
transcription
MadAppGangAudio/video transcription using OpenAI Whisper. Covers installation, model selection, transcript formats (SRT, VTT, JSON), timing synchronization, and speaker diarization. Use when transcribing media or generating subtitles.
agent-coordination-discipline
MadAppGangUse when deciding whether to launch an agent, selecting which agent to use, or coordinating multiple agents. Covers delegation criteria, external-model patterns, task isolation, and agent selection strategies.
task-complexity-router
MadAppGangComplexity-based task routing for optimal model selection and cost efficiency. Use when deciding which model tier to use, analyzing task complexity, optimizing API costs, or implementing tiered routing. Trigger keywords - "routing", "complexity", "model selection", "tier", "cost optimization", "haiku", "sonnet", "opus", "task analysis".
fabric
ynulihaoIntelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
architecture-selection
rsmdtSystem architecture patterns including monolith, microservices, event-driven, and serverless, with C4 modeling, scalability strategies, and technology selection criteria. Use when designing system architectures, evaluating patterns, or planning scalability.
creative-direction
dylanfeltusImage prompt templates, model selection guidance, and anti-generic patterns for generating visual assets. Use when the user needs AI-generated images for landing pages, marketing, or products. Covers hero images, feature illustrations, OG cards, icons, and backgrounds.
fabric
MicrockIntelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
ring:pre-dev-data-model
LerianStudioGate 5: Data structures document - defines entities, relationships, and ownership before database technology selection. Large Track only.
opencode-config
serejarisUse when configuring OpenCode CLI - changing default model, adding providers, setting baseURL, or troubleshooting model selection issues
creating-claude-agents
pr-pmUse when creating or improving Claude Code agents. Expert guidance on agent file structure, frontmatter, persona definition, tool access, model selection, and validation against schema.
agent-builder
pr-pmUse when creating, improving, or troubleshooting Claude Code subagents. Expert guidance on agent design, system prompts, tool access, model selection, and best practices for building specialized AI assistants.
image-to-video
inference-shStill-to-video conversion guide: model selection, motion prompting, and camera movement. Covers Wan 2.5 i2v, Seedance, Fabric, Grok Video with when to use each. Use for: animating images, creating video from stills, adding motion, product animations. Triggers: image to video, i2v, animate image, still to video, add motion to image, image animation, photo to video, animate still, wan i2v, image2video, bring image to life, animate photo, motion from image
letta-development-guide
letta-aiComprehensive guide for developing Letta agents, including architecture selection, memory design, model selection, and tool configuration. Use when building or troubleshooting Letta agents.
llm-inference-batching-scheduler
letta-aiGuidance for implementing batching schedulers for LLM inference systems with compilation-based accelerators. This skill applies when optimizing request batching to minimize cost while meeting latency thresholds, particularly when dealing with shape compilation costs, padding overhead, and multi-bucket request distributions. Use this skill for tasks involving batch planning, shape selection, generation-length bucketing, and cost-model-driven optimization for neural network inference.
mteb-retrieve
letta-aiThis skill provides guidance for semantic similarity retrieval tasks using embedding models (e.g., MTEB benchmarks, document ranking). It should be used when computing embeddings for documents/queries, ranking documents by similarity, or identifying top-k similar items. Covers data preprocessing, model selection, similarity computation, and result verification.
mcp-management
binjuhorManage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
ml-cv-specialist
alirezarezvaniDeep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.
rag-implementation
applied-artificial-intelligenceComprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.
claude-code-agents
vasilyu1983Create and maintain Claude Code agents/subagents (.claude/agents/*.md) with YAML frontmatter (name/description/tools/model/permissionMode/skills/hooks), least-privilege tool selection, delegation patterns (Task), context budgeting, and safety best practices.
ai-ml-data-science
vasilyu1983End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
API Design Fundamentals
melodic-softwareUse when designing APIs, choosing between REST/GraphQL/gRPC, or understanding API design best practices. Covers protocol selection, resource modeling, and API patterns.
subagent-development
melodic-softwareCentral authority for Claude Code subagents (sub-agents). Covers agent file format, YAML frontmatter, tool access configuration, model selection (inherit, sonnet, haiku, opus), automatic delegation, agent lifecycle, resumption, command-line usage (/agents), Agent SDK programmatic agents, priority resolution, and built-in agents (Plan subagent). Assists with creating agents, configuring agent tools, understanding agent behavior, and troubleshooting agent issues. Delegates 100% to docs-management skill for official documentation.
model-selection
melodic-softwareChoose appropriate model for custom agent tasks. Use when selecting between Haiku, Sonnet, and Opus for agents, optimizing cost vs quality tradeoffs, or matching model capability to task complexity.