250 free skills · page 5 of 5
AI & ML skills
Skills for AI/ML work — LLM prompt engineering, RAG pipelines, vector databases, fine-tuning, and agent orchestration.
Real files from public repositories — hand-curated, never invented. Sorted by installs.
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vaexk-dense-ai/scientific-agent-skillsUse this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.AI & ML1.3k
audio-transcribersickn33/agentic-awesome-skillsTransform audio recordings into professional Markdown documentation with intelligent summaries using LLM integrationAI & ML1.3kzarr-pythonk-dense-ai/scientific-agent-skillsChunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.AI & ML1.3kpysamk-dense-ai/scientific-agent-skillsPython/HTSlib workflows for genomic files. Use when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.AI & ML1.3k
avoid-feature-creepwaynesutton/convexskillsPrevent feature creep when building software, apps, and AI-powered products. Use this skill when planning features, reviewing scope, building MVPs, managing backlogs, or when a user says "just one more feature." Helps developers and AI agents stay focused, ship faster, and avoid bloated products.AI & ML1.3kgtarsk-dense-ai/scientific-agent-skillsUse Gtars for local genomic interval models and set algebra, overlaps and counts, consensus and coverage, tokenization, fragment processing, and refget/BEDbase planning across Python, Rust, and the CLI.AI & ML1.3k
rag-retrievalyonatangross/orchestkitRetrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.AI & ML1.3klamindbk-dense-ai/scientific-agent-skillsUse when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.AI & ML1.2kpolars-biok-dense-ai/scientific-agent-skillsHigh-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Overlap, nearest, merge, coverage, complement, subtract for BED/VCF/BAM/GFF intervals. Streaming, cloud-native, faster bioframe alternative.AI & ML1.2k
prompt-engineeringneolabhq/context-engineering-kitUse this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.AI & ML1.2kOther tasks
same rules, different job
