Bioinformatik

Agent Skills für Genomanalyse, Sequenzierungsdaten und biologische Forschung durchsuchen

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24.594 Ergebnisse

healthcare-emr-patterns

電子医療記録（EMR）パターン、相互運用性、およびHL7/FHIR統合。

Sprache des Skill-Inhalts: Englisch

操作并扩展NanoClaw v2，这是ECC基于claude -p构建的零依赖会话感知REPL。

Sprache des Skill-Inhalts: Chinesisch

NousResearch/hermes-agent

llama.cpp local GGUF inference + HF Hub model discovery.

Sprache des Skill-Inhalts: Englisch

NousResearch/hermes-agent

Structured LLM outputs validated with Pydantic.

Sprache des Skill-Inhalts: Englisch

NousResearch/hermes-agent

Gateway to 400+ genomics and computational biology skills.

Sprache des Skill-Inhalts: Englisch

n8n-conventions

Quick reference for n8n patterns. Full docs /AGENTS.md

Sprache des Skill-Inhalts: Englisch

thedotmack/claude-mem

Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.

Sprache des Skill-Inhalts: Englisch

Pathfinder traversal of the knowledge graph starting from a seed entity

Sprache des Skill-Inhalts: Englisch

Ingest and normalize market data into OHLCV vectors stored by symbol and date for later pattern detection. Use when you need to load price and volume history for a symbol before running pattern detection or comparison. Read-only analysis; it never places orders.

Sprache des Skill-Inhalts: Englisch

Detect and classify candlestick patterns from ingested OHLCV data

Sprache des Skill-Inhalts: Englisch

7-section repo readiness report from `metaharness genome {path}`. Returns repo_type / agent_topology / risk_score / mcp_surface / test_confidence / publish_readiness. Pure-read; degrades gracefully (ADR-150).

Sprache des Skill-Inhalts: Englisch

Run a GEPA learning cycle via `metaharness learn` (upstream ADR-235, metaharness@0.3.0) — optimizes a harness genome against a SWE-bench-style slice manifest. $0 dry-run by default; `--run` is the explicit spend opt-in. Requires a metaharness repo checkout (`--repo` or $METAHARNESS_REPO) — without one it reports `checkout-required` with clone instructions. Degrades gracefully when metaharness is absent.

Sprache des Skill-Inhalts: Englisch

harness-similarity

ADR-152 — weighted similarity between two harness fingerprints (genome + score JSON). Returns overall score in [0,1] plus per-component breakdown (cosine over 9 numerics, categorical agreement over 4 enums, jaccard over agent_topology). Unblocks ADR-151 §3.2 Recommender, §3.3 Drift Detection, §3.5 Plugin Compat. Pure-TS, no `@metaharness/*` dep — preserves ADR-150's four architectural constraints.

Sprache des Skill-Inhalts: Englisch

vector-hyperbolic

Place hierarchical data in the Poincare ball using depth for the radius and npx ruvector@0.3.3 embed text for direction (a unit-norm text embedding alone has no depth)

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores variants or scans windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and builds Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Handles annotated matrices in single-cell analysis, .h5ad and Zarr files, and integration with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.

Sprache des Skill-Inhalts: Englisch

cellxgene-census

K-Dense-AI/scientific-agent-skills

Queries the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Uses the Biohub esm Python SDK for ESM3 protein generation, ESMC embeddings, and ESMFold2 all-atom folding. Applies to local model inference and Biohub hosted clients, including former Forge workflows; distinguishes the separate legacy fair-esm distribution.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Analyzes, manipulates, compares, annotates, and visualizes phylogenetic or other hierarchical trees with ETE 4. Supports Newick/Nexus tree I/O, topology edits and pattern matching, Robinson-Foulds comparisons, gene-tree evolutionary events and reconciliation, NCBI/GTDB taxonomy, SmartView exploration, and publication rendering. Applies to existing trees after alignment and phylogenetic inference, rather than inferring trees from raw sequences.

Sprache des Skill-Inhalts: Englisch

folklore-variant-evidence

K-Dense-AI/scientific-agent-skills

Retrieves ClinGen gene-disease validity assertions for a public gene or disease, and reviews source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple indel through Folklore Clinical Variant Interpretation MCP. Used when a scientific agent must branch deterministically on resolved, ambiguous, not-found, invalid, unsupported, or unavailable variant outcomes; chain a resolved public variant into related literature or publication details; or preserve evidence provenance without accepting patient, phenotype, family, segregation, or private case data.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Supports audited local Geniml genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes.

Sprache des Skill-Inhalts: Englisch

genomic-coordinates

K-Dense-AI/scientific-agent-skills

Converts genomic intervals between coordinate conventions, normalises and compares variant representations, and detects assembly or contig-naming mismatches before they corrupt an analysis. Used whenever coordinates cross a format, tool, or assembly boundary - converting between BED, GFF/GTF, VCF, SAM/BAM, WIG, PSL, genePred, Picard interval_list, or region strings; reconciling 0-based half-open with 1-based inclusive; left-aligning or trimming indels; checking whether two variant records describe the same change; mapping genomic to transcript, CDS, or protein positions; auditing a BED/GTF/VCF for convention violations; or diagnosing GRCh37 vs hg19 vs GRCh38 vs T2T, chr-prefix, and liftover problems. Triggers include "off by one", "0-based", "1-based", "half-open", "coordinate system", "left-align", "normalize variant", "bcftools norm", "chr prefix", "wrong genome build", "liftover", "REF mismatch", and "HGVS".

Sprache des Skill-Inhalts: Englisch

genomic-intelligence

K-Dense-AI/scientific-agent-skills

Predicts regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene symbol, a genomic region, or a DNA/FASTA sequence and wants any of these predictions, mentions Genomic Intelligence, genomicintelligence.ai, api.genomicintelligence.ai, or mcp.genomicintelligence.ai.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Queries 20+ bioinformatics resources through CLI/Python. Supports quick lookups of gene info, BLAST/BLAT, viral sequence downloads, PDB/mmCIF structures, G2P residue annotations, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Supports 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.

Sprache des Skill-Inhalts: Englisch

neuropixels-analysis

K-Dense-AI/scientific-agent-skills

Analyzes Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting or claim tested support for arbitrary acquisition formats.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Queries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations.

Sprache des Skill-Inhalts: Englisch

pathogen-variant-surveillance

K-Dense-AI/scientific-agent-skills

Queries public GenSpectrum LAPIS data for pathogen genomic surveillance, current lineage nomenclature, weekly sequence proportions, reporting delays, and descriptive mutation frequencies. Use for variant surveillance, Pango lineage validation, dominant submitted lineages, Nextclade assignment provenance, SARS-CoV-2, influenza/H5N1 clades, RSV, mpox, measles, dengue, or LAPIS queries. Distinguishes sequence prevalence from infection prevalence, clades from genotypes, missing calls from reference matches, and sampling changes from biological growth advantage.

Sprache des Skill-Inhalts: Englisch

pathway-enrichment

K-Dense-AI/scientific-agent-skills

Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Builds and analyzes phylogenetic trees using MAFFT multiple sequence alignment, IQ-TREE maximum likelihood with ModelFinder and branch support, and FastTree approximate inference. Uses ETE3 for tree summaries and visualization. Applies to homologous nucleotide or protein sequences, microbial gene trees, protein families, and cautiously interpreted dated phylogenies.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Performs genomic interval overlap, nearest, merge, coverage, complement and subtraction on Polars DataFrames, and reads or writes BED, VCF, BCF, BAM, CRAM, GFF, GTF, FASTA and FASTQ data. Use for coordinate-aware genomic joins, read-depth analysis, lazy bioinformatics I/O, SQL queries or migration from bioframe.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Queries a pinned Precision Medicine Knowledge Graph (PrimeKG) CSV for typed gene, drug, disease, and phenotype nodes, direct associations, disease context, and one- or two-hop paths. Use for PrimeKG reproducibility, biological association lookup, and hypothesis generation with relation and data provenance preserved.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Performs bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.

Sprache des Skill-Inhalts: Englisch

qiime2-amplicon

K-Dense-AI/scientific-agent-skills

Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance. Checks paired FASTQ manifests, primer orientation diagnostics, predicted post-trimming overlap, sample IDs, runtime versions and read retention, and guides selection of compatible taxonomic classifiers.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Performs Scanpy single-cell RNA-seq QC, normalization, HVG selection, PCA/UMAP/t-SNE, clustering, exploratory marker ranking, pseudobulk preparation, visualization, and Seurat or SingleCellExperiment RDS conversion to h5ad. Applies to established exploratory scRNA-seq workflows with explicit count and expression provenance; complementary skills cover scvi-tools models and AnnData format details.

Sprache des Skill-Inhalts: Englisch

scientific-schematics

K-Dense-AI/scientific-agent-skills

Generates scientific diagram drafts using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.7 Flash for quality review. Refines when the review requests improvement, with at most two generations. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Performs RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Stores and retrieves genomic variant calls with TileDB-VCF. Use for indexed single-sample VCF/BCF ingestion, incremental cohorts, region and sample queries, streaming results, allele statistics, QC, and VCF/BCF export locally or through TileDB Cloud.

Sprache des Skill-Inhalts: Englisch

K-Dense-AI/scientific-agent-skills

Supports work with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the `waypoint` CLI from the `waypoint-bio` package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tables into waypoint format.

Sprache des Skill-Inhalts: Englisch

codewhale-hq/Codewhale

Sprache des Skill-Inhalts: Nicht bestimmbar

langfuse/langfuse

Seed reproducible local Langfuse data in ClickHouse and Postgres. Use for complex traces, long sessions, v3/v4 events, bulk list data, or frontend rendering and performance tests; never use ad hoc scripts or raw inserts.

Sprache des Skill-Inhalts: Englisch

HKUDS/Vibe-Trading

Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks "which alphas exist", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.

Sprache des Skill-Inhalts: Englisch