High-performance computing (HPC) systems are difficult to use due to complex job scheduling, resource selection, and limited feedback on job failures. This paper presents Inkly, a data-driven HPC assistant with job intelligence that augments user workflows with insights derived from historical Slurm job data. Inkly ingests job records via sacct, stores them in a SQLite database, and computes aggregate metrics such as partition success rates, CPU and memory usage patterns, and failure distributions. These metrics are added to the prompts to guide users toward more effective job configurations. The system enforces safety through prompt filtering, command guardrails, and containerized execution using Apptainer.

Recent pandemics like COVID-19 highlighted the importance of rapidly developing diagnostics to detect evolving pathogens. CRISPR-Cas technology has recently been used to develop diagnostic assays for sequence-specific recognition of DNA or RNA. These assays have similar sensitivity to the gold standard qPCR but can be deployed as easy to use and inexpensive test strips. However, the discovery of diagnostic regions of a genome flanked by conserved regions where primers can be designed requires extensive bioinformatic analyses of genome sequences. We developed the Python package krisp to aid in the discovery of primers and diagnostic sequences that differentiate groups of samples from each other, using either unaligned genome sequences or a variant call format (VCF) file as input. Krisp has been optimized to handle large datasets by using efficient algorithms that run in near linear time, use minimal RAM, and leverage parallel processing when available. The validity of krisp results has been demonstrated in the laboratory with the successful design of a CRISPR diagnostic assay to distinguish the sudden oak death pathogen Phytophthora ramorum from closely related Phytophthora species. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays.