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Found 2,493 Skills
Audit whether an academic paper cites the necessary classic, closest, and recent concurrent work before submission. Use this skill whenever the user worries that references are incomplete, wants missing citations found, needs related work coverage checked, asks whether a paper cites classic work or recent arXiv/OpenReview work, or wants a citation coverage report for ML/AI venues such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, or similar conferences.
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.
Check which Rust lines are not covered by Rust tests.
Lavarage Protocol — leveraged trading on Solana for any SPL token. Open long/short positions on crypto, memecoins, RWAs (stocks like OPENAI, SPACEX), commodities (gold), and hundreds of other tokens with up to 12x leverage. Permissionless markets — if a token has a liquidity pool, it can be traded with leverage.
MUST be used whenever fixing test coverage for a Dune app to meet the 80% line coverage hard gate. This skill finds AND fixes coverage gaps — it configures tooling, writes missing tests, covers untested paths, and refactors code for testability. It does not just report. Triggers: test coverage, fix tests, write tests, add tests, coverage fix, 80% coverage, coverage gate, missing tests, testability, vitest coverage, jest coverage.
Complete file handling including upload flows, serving files via URL, storing generated files from actions, deletion, and accessing file metadata from system tables
Build with Firebase Cloud Storage - file uploads, downloads, and secure access. Use when: uploading images/files, generating download URLs, implementing file pickers, setting up storage security rules, or troubleshooting storage/unauthorized, cors errors, quota exceeded, or upload failed errors. Prevents 9 documented errors.
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.
Build Retrieval-Augmented Generation (RAG) applications that combine LLM capabilities with external knowledge sources. Covers vector databases, embeddings, retrieval strategies, and response generation. Use when building document Q&A systems, knowledge base applications, enterprise search, or combining LLMs with custom data.
Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization
Retrieval-Augmented Generation - chunking strategies, embedding, vector search, hybrid retrieval, reranking, query transformation. Use when building RAG pipelines, knowledge bases, or context-augmented applications.
Use when adding multi-format RAG ingest, chunk, embed, and retrieval pipelines; pair with architect-python-uv-batch or architect-python-uv-fastapi-sqlalchemy.