Embeddings
Vector embeddings and similarity
literature-search
by fl-sean03
Search and retrieve scientific literature. Use when asked to find papers, research a topic, find citations, get paper abstracts, or conduct literature reviews. Accesses Semantic Scholar, arXiv, and other academic databases.
ai-sdk
by fellipeutaka
'Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".'
perplexity-search
by drshailesh88
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model's knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
Knowledge Pipeline Skill
by drshailesh88
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design-postgres-tables
by M4n5ter
Comprehensive PostgreSQL-specific table design reference covering data types, indexing, constraints, performance patterns, and advanced features
resource-acquisition
by fl-sean03
Find and acquire computational science resources autonomously. Use when you need force field parameters, pseudopotentials, crystal structures, or any other scientific data. You are a researcher - you find what you need.
hono
by fellipeutaka
Efficiently develop Hono applications using Hono CLI. Supports documentation search, API reference lookup, request testing, and bundle optimization.
jira
by odyssey4me
Search and manage Jira issues using JQL queries, create/update issues, and manage workflows. Use when working with Jira project management.
lammps-simulation
by fl-sean03
Run LAMMPS molecular dynamics simulations. Use when asked to run MD simulations, energy minimization, equilibration, production runs, or calculate properties like diffusion, RDF, MSD. Supports both CPU and GPU execution.
tanstack-pacer
by fellipeutaka
TanStack Pacer best practices for execution control in React — debouncing, throttling, rate limiting, queuing, and batching. Use when implementing search inputs, scroll handlers, API rate limits, task queues, bulk operations, or any scenario requiring controlled execution timing with reactive state.
deep-learning-core
by levy-n
Explains neural network fundamentals: the Three Pillars (Model, Loss, Optimizer), backpropagation, gradient descent variants (SGD, Adam), regularization (Dropout, BatchNorm), and MLP architecture design. Use when learning how neural networks work, debugging training issues, or when user asks about 'backpropagation', 'vanishing gradients', 'learning rate', 'loss function', 'overfitting', 'underfitting', 'activation functions', 'why isn\'t my model learning', 'gradient descent', 'Adam', 'Dropout', 'BatchNorm', 'autoencoder', 'denoising autoencoder', or 'latent space'.
ml-knowledge-index
by levy-n
Routes ML/DL questions to specialized skills. Use FIRST when unsure which skill applies, when user asks broad ML questions, or when multiple topics might be relevant. Maps: regression/classification → ml-fundamentals, ensembles/clustering → ml-advanced, TF-IDF/Word2Vec → nlp-classical, training/backprop → deep-learning-core, PyTorch → pytorch-mastery, CNNs/images → cnn-vision, LSTM/time-series → sequence-models, BERT/HuggingFace → transformers-llm, RAG/embeddings → rag-retrieval, APIs/PDF-parsing → data-pipeline, LoRA/QLoRA/PEFT → fine-tuning-peft, MLflow/W&B/Optuna → mlops-experiment, SHAP/Grad-CAM → model-interpretability, Q-learning/PPO/DQN → reinforcement-learning, GAN/VAE/diffusion → generative-models, explanations → ml-teaching-assistant.
nlp-classical
by levy-n
Implements traditional NLP techniques before transformers. Covers text vectorization (TF-IDF, Bag-of-Words), word embeddings (Word2Vec, FastText, GloVe, Doc2Vec), topic modeling (LDA, Gensim), and text similarity (Jaccard, Cosine, FuzzyWuzzy, record linkage). Use when building text classifiers without deep learning, doing topic extraction, entity matching, or when user mentions 'TF-IDF', 'Word2Vec', 'topic modeling', 'LDA', 'text similarity', 'n-grams', 'document clustering', 'GloVe', 'Doc2Vec', 'FuzzyWuzzy', or 'record linkage'.
docs-discovery
by samhvw8
"Technical documentation discovery via context7 and web search. Capabilities: library/framework docs lookup, topic-specific search. Keywords: llms.txt, context7, documentation, library docs, API docs. Use when: searching library documentation, finding framework guides, looking up API references."
x-algo-ml
by CloudAI-X
Explain the Phoenix ML model architecture for X recommendations. Use when users ask about embeddings, transformers, how predictions work, or ML model details.
content-strategy
by aiaiohhh
When the user wants to plan a content strategy, decide what content to create, or figure out what topics to cover. Also use when the user mentions "content strategy," "what should I write about," "content ideas," "blog strategy," "topic clusters," or "content planning." For writing individual pieces, see copywriting. For SEO-specific audits, see seo-audit.
free-tool-strategy
by aiaiohhh
When the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," or "free resource." This skill bridges engineering and marketing — useful for founders and technical marketers.
rag-retrieval
by levy-n
Implements RAG (Retrieval-Augmented Generation) pipelines. Covers embedding APIs (OpenAI, Gemini, Sentence-Transformers), vector stores (FAISS, ChromaDB, Pinecone), RAG variants (Query Rewrite, Conversational, Multi-hop), and evaluation (RAGAS, Faithfulness). Use when building knowledge bases, semantic search, chatbots with documents, or when user mentions 'RAG', 'embeddings', 'vector store', 'FAISS', 'ChromaDB', 'similarity search', 'retrieval', 'chunking', 'hallucination reduction', 'semantic search', or 'knowledge base'.
mem0-fastapi-integration
by vanman2024
Memory layer integration patterns for FastAPI with Mem0 including client setup, memory service patterns, user tracking, conversation persistence, and background task integration. Use when implementing AI memory, adding Mem0 to FastAPI, building chat with memory, or when user mentions Mem0, conversation history, user context, or memory layer.
firecrawl
by founderjourney
Web scraping, search, and data extraction using Firecrawl API. Use when users need to fetch web content, discover URLs on sites, search the web, or extract structured data from pages.
UI Design Reference - Searchable Pattern Database
by samhvw8
search-enhancer
by CuriousLearner
Enhanced code search with semantic understanding, pattern matching, and intelligent query interpr...
lookalike-customer-finder
by gked2121
Input your best customers and find 100+ companies that match the profile. Uses firmographic data, tech stack, growth signals, and similarity scoring to identify ideal prospects. Use when building target account lists or expanding to new markets.
pentest-hacktricks-finder
by crtvrffnrt
Search and retrieve pentesting, red teaming, and security research information from the HackTricks wiki (book.hacktricks.wiki). Use for payloads, methodologies, bypasses, and edge-case behaviors across web, network, cloud, and application security topics.