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Found 365 Skills
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing repositories, models, datasets, and Spaces on the Hugging Face Hub. Replaces now deprecated `huggingface-cli` command.
Local execution tools for X/Twitter hosted collection workflows, including actor runs, dataset normalization, tweet ranking, account ranking, audience graph construction, and language clustering.
Dune CLI for querying blockchain and on-chain data via DuneSQL, searching decoded contract tables, managing saved queries, and monitoring credit usage on Dune Analytics. Use when user asks about blockchain data, on-chain analytics, token transfers, DEX trades, smart contract events, wallet balances, Ethereum/EVM chain queries, DuneSQL, or says "query Dune", "search Dune datasets", or "run a Dune query".
Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.
Implement robust batch processing systems with job queues, schedulers, background tasks, and distributed workers. Use when processing large datasets, scheduled tasks, async operations, or resource-intensive computations.
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
Use when validating golden dataset quality. Runs schema checks, duplicate detection, and coverage analysis to ensure dataset integrity for AI evaluation.
Use when adding, reading, registering, or organizing research sources such as PDFs, arXiv papers, Zotero items, proposals, datasets, reports, archives, web pages, BibTeX, or source metadata.
Implement background job processing systems with task queues, workers, scheduling, and retry mechanisms. Use when handling long-running tasks, sending emails, generating reports, and processing large datasets asynchronously.
Find and read academic papers: disambiguate queries, discover papers (search, citation traversal, recommendations, arXiv monitoring, trending, GitHub search), evaluate (TLDR, citations, code, SOTA), and read with structured analysis (3-level strategy). Use when: finding papers, reading a paper, related work, citation analysis, research trends, SOTA results, datasets. Do NOT use for generating literature survey reports (use research-survey), generating research ideas (use research-ideation), writing a paper's Related Work section (use paper-writing), comparing/ranking research ideas (use research-ideation), or planning paper structure (use paper-planning).
Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Use this skill when the user needs to audit financial data for fraud indicators, validate data integrity, or detect fabricated numbers — even if they say 'data manipulation detection', 'first digit test', or 'accounting fraud screening'.
Remote SLURM GPU cluster execution over SSH with sbatch/srun, Pyxis/Enroot containers, and Lustre-backed results. Use when running TAO training/eval/inference jobs on an on-prem or DGX SLURM cluster. Trigger phrases include "run on SLURM", "submit sbatch", "DGX SLURM cluster", "Pyxis/Enroot container", "Lustre dataset".