Skill UI

This comprehensive runbook provides rapid, structured procedures for incident response on Vast.ai cloud GPU instances. It covers common failure scenarios like spot preemption, training job crashes, and GPU hardware issues. Users can follow the steps for triage, mitigation, recovery, and postmortem analysis to minimize downtime and ensure computational continuity.

Biopython provides Python APIs for sequence manipulation, database access, BLAST automation, structural bioinformatics, and phylogenetics, making it ideal for scripting custom pipelines and batch processing in computational molecular biology workflows.

This guide provides comprehensive guidelines for performing vertical scaling on Qdrant instances. Vertical scaling involves upgrading the computational resources (CPU, RAM, or disk) of existing nodes rather than adding new nodes. It is the recommended first step when single nodes experience resource exhaustion (e.g., RAM usage > 80%, CPU saturation). The guide covers sizing guidelines, best practices for cloud deployment, and clearly defines the thresholds when horizontal scaling becomes necessary.

This skill provides a rigorous framework for researchers to evaluate whether their cognitive science project is suitable for high-impact, theory-driven journals. It guides users to elevate their contribution from single-effect reports to multi-experiment programs, formal computational models, and substantial theoretical advances, ensuring the work meets the necessary standard of integrative scope.

A guide for writing theoretical computer science papers, detailing how to structure proofs and correctly incorporate computational evidence. It emphasizes matching arguments (e.g., upper/lower bounds) to claim shapes and ensures that computation (like SMT verification or exhaustive checks) is rigorous, reproducible, and serves only to certify a step, not replace the main theorem.

This guide details best practices for achieving reproducibility in highly theoretical computer science papers, such as those submitted to ICALP. Since artifact evaluation is absent, reproducibility hinges on the ability for any reader to independently verify every step of the proof. Key requirements include writing self-contained proofs, providing verifiable computational certificates, maintaining consistency between submissions, and optionally using formal verification tools like Coq or Lean.

This comprehensive guide outlines the necessary theoretical rigor for algorithms and models submitted to top-tier computational journals. It instructs authors on how to formally state mathematical formulations, rigorously prove model validity, establish convergence, and quantify complexity (e.g., approximation ratios, finite termination). It provides specialized paths for exact methods, metaheuristics, and ML-enhanced OR, ensuring that the theoretical advancements fully support and rationalize the experimental findings.

Provides comprehensive guidelines for authors submitting to the Journal of Economic Theory (JET). It details mandatory data sharing (Elsevier Option C), ensuring computational reproducibility for numerical examples, and proper declaration of generative AI usage. This playbook helps authors structure their submissions to meet high academic standards of verifiable evidence and transparency.

This guide helps researchers determine if their economic project—whether theory-heavy, empirical, or computational—meets the rigorous theoretical standards required for high-impact journals like the Journal of Economic Theory (JET). It outlines critical criteria such as originality, theoretical grounding, and the necessity of leading with a core theorem.

This skill acts as a comprehensive workflow router for authors preparing Operations Research (OR) manuscripts for top-tier journals. It guides the user through the entire modeling-to-submission lifecycle, mapping the necessary stages—from initial theory development and literature positioning to computational data analysis and final contribution framing. Its purpose is to ensure methodological rigor and adherence to the journal's specific, multi-stage norms.

This comprehensive guide outlines the requirements for submitting computational artifacts for post-acceptance evaluation at PPoPP. It details PPoPP's unique badge policy (Functional, Reusable, Results Reproduced), addresses the challenges of ensuring results reproduce across different hardware architectures, and provides a detailed packaging plan, including containerization, README structure, and provenance documentation for maximizing reproducibility.

A comprehensive guide on methodological rigor for submitting manuscripts to Social Forces. It covers designing causal inference (panel/DID/IV/RDD), demographic analysis, comparative-historical case selection, and network/computational methods. The focus is on strengthening the design by rigorously defending assumptions and ruling out alternative explanations, ensuring methodological soundness beyond mere association.