Use when designing or implementing tool-using agents for research or software workflows and you need an explicit architecture, tool contract, prompt strategy, and evaluation plan.
Use when designing an evaluation plan for a CS, ML, or agent project and you need to choose datasets, baselines, metrics, ablations, compute budgets, and reporting rules.
Manage citations and BibTeX for computer science papers, theses, surveys, rebuttals, and project reports. Use when verifying DOI or arXiv metadata, cleaning `.bib` files, deduplicating references, formatting venue-ready citations, or checking that paper, code, and artifact references are consistent.
Use when reproducing a paper, repo, benchmark, or reported result and you need an explicit plan for environment setup, execution, discrepancy logging, and final reproduction status.
Use when turning an existing codebase, experiment set, or system into a paper plan and you need contribution framing, evidence mapping, figure planning, and missing-experiment detection.
Use when running iterative experiments and you need a disciplined way to log configs, seeds, environments, metrics, failures, and comparison summaries.
Explore and summarize computer science research data, experiment outputs, benchmark tables, logs, embeddings, predictions, and dataset files. Use when inspecting CSV, JSON, Parquet, NPY, NPZ, HDF5, TXT logs, or similar files to understand structure, quality, anomalies, and next analysis steps before modeling, benchmarking, or paper writing.
Detect available CPU, GPU, memory, and disk resources before compute-heavy CS work. Use when planning model training, large-scale evaluation, embedding generation, experiment sweeps, dataset processing, log analysis, or reproduction runs where hardware limits affect the workflow.