Project-wide code coverage and CRAP (Change Risk Anti-Patterns) score analysis for .NET projects. Calculates CRAP scores per method and surfaces risk hotspots — complex code with low coverage that is dangerous to modify. Use to diagnose why coverage is stuck or plateaued, identify what methods block improvement, or get project-wide coverage analysis with risk ranking. USE FOR: coverage stuck, coverage plateau, can't increase coverage, what's blocking coverage, coverage gap, CRAP scores, risk hotspots, where to add tests, coverage analysis, coverage report. DO NOT USE FOR: targeted single-method CRAP analysis (use crap-score); auditing test code for coverage-touching or other anti-patterns (use test-anti-patterns); writing tests; running tests (use run-tests). Requires or produces coverage (Cobertura) and CRAP metrics.
Role-based multi-agent orchestration framework for building "Crews" of collaborating LLM agents (each with a role, goal, backstory, and optional tools) that execute sequential or hierarchical task pipelines, plus event-driven "Flows" for precise, single-LLM-call orchestration. Use when composing specialized agents (e.g. a research agent + an analysis agent + a writer agent) into a declarative pipeline for complex multi-step tasks like automated literature review or multi-agent research workflows. Distinct from LangGraph's explicit state-machine graphs and smolagents' minimal tool-calling loop — CrewAI is standalone (no LangChain dependency) and expresses orchestration as roles and delegated tasks rather than a graph or a single ReAct loop.
Adapter, primer, and poly-A/T trimming for high-throughput sequencing reads (FASTQ/FASTA). Use for ATAC-seq (Nextera adapter removal), ChIP-seq/CUT&RUN, small RNA-seq (preserving reads as short as ~18 nt), and amplicon/primer trimming where exact or linked adapter sequences matter more than fastp's heuristic auto-detection. Covers 3'/5'/linked adapters, IUPAC wildcards, paired-end synchronization, quality/length filtering, and demultiplexing by barcode.
Data Version Control (DVC) for tracking large datasets/models with Git-like semantics, defining reproducible data/ML pipelines (dvc.yaml stages that only re-run when their inputs change), and lightweight experiment tracking without a server. Use when large files (VCF/BAM/FASTQ, reference genomes, model weights) can't go in Git, when you need Make/Snakemake-style selective re-execution driven by data, or when comparing many training runs locally before promoting one. Pairs with Git (code), cloud object storage (data), and Snakemake/Nextflow (compute graph).
Fast Python I/O for BigWig (continuous genome signal) and BigBed (interval annotation) files via libBigWig. Use for random-access signal queries at specific genomic coordinates (bw.values, bw.stats), computing per-region summary statistics (mean/max/coverage) over a BED file of regions, writing custom BigWig tracks from numpy arrays, and loading ChIP-seq/ATAC-seq/RNA-seq/methylation coverage tracks (e.g. produced by deeptools bamCoverage) into pandas/numpy for downstream analysis or ML feature extraction. Complements deeptools (which generates BigWig files) and chip-seq/atac-seq workflows.
Distributed Python compute with Ray — @ray.remote tasks/actors for cluster-scale parallelism, Ray Data for large-batch preprocessing, Ray Train for distributed model training (DDP/FSDP/DeepSpeed), Ray Tune for scalable hyperparameter search, and Ray Serve for model serving. Use when scaling a Python workload (docking screens, million-cell atlas preprocessing, hyperparameter sweeps, multi-GPU training) from a laptop to a multi-node cluster with minimal code changes. Ray Tune can use optuna as a search algorithm; Ray Train wraps pytorch-lightning-style training loops.
MinHash/FracMinHash sketching for alignment-free comparison of genomes and metagenomes. Use for fast all-vs-all genome similarity and ANI estimation across thousands of genomes without alignment, taxonomic classification of metagenomes against GTDB/NCBI reference databases (sourmash gather/tax), and sequencing-cohort QC (contamination or duplicate detection). Complements upstream assembly/QC pipelines (snakemake-workflow-engine, nextflow) and feeds downstream phylogenetics; distinct from alignment-based tools like BLAST or mash-style exact-num MinHash by supporting scaled (FracMinHash) sketches that compare well across very different dataset sizes.
Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.