Skill UI

A comprehensive guide to hardening AWS Identity and Access Management (IAM) configurations. This skill enforces least-privilege access by utilizing IAM Access Analyzer, setting permission boundaries, scoping policies, and managing credential rotation. It is crucial for reducing the blast radius of compromised accounts and passing compliance audits (e.g., SOC 2, PCI-DSS).

This skill provides advanced detection methods for credential dumping, covering LSASS memory access, SAM hive extraction, and NTDS.dit theft (e.g., using Mimikatz). It utilizes Sysmon Event ID 10 and Windows Security logs to enable threat hunting, assisting SOC analysts in triaging EDR alerts and building robust SIEM correlation rules.

A comprehensive guide to deploying cloud-native deception across AWS, Azure, and GCP. This skill utilizes decoy resources—such as canary IAM keys, honey object storage buckets, and decoy secrets—whose sole purpose is to generate high-fidelity alerts the instant they are interacted with by an attacker. It detects cloud reconnaissance, credential theft, and lateral movement by wiring detection through native logging services (CloudTrail, Sentinel, Cloud Audit Logs), ensuring near-zero false positives for the SOC.

This tool automates the OAuth 2.0 scope minimization review process. It identifies over-permissioned third-party application integrations, excessive API scopes, and risky OAuth consent patterns across identity providers. It is crucial for security audits, compliance (GDPR, SOC 2), and enforcing the least-privilege principle for cross-boundary data access.

This utility initializes a node for participation in a federated network. It automatically generates a secure ed25519 keypair, creates the necessary federation configuration, and allows users to optionally set a compliance mode (such as HIPAA, GDPR, or SOC2). It is a fundamental step for integrating a node into a secure, shared infrastructure.

This guide assists in structuring the design of Environmental Science & Technology (ES&T) studies, including lab, mesocosm, field sampling, or modeling experiments. It ensures methodological rigor by planning for environmental relevance, necessary controls (e.g., abiotic loss, sorption), proper replication, mass/energy balance, and a comprehensive QA/QC plan from the outset, helping the study withstand expert peer review.

This comprehensive guide outlines the full project management lifecycle for submitting research papers to the ICASSP conference (IEEE Signal Processing Society). It details the annual timeline, critical milestones, backward planning heuristics, and best practices across various submission tracks to ensure timely and successful publication.

A comprehensive guide to interpreting the structural mechanics of large-scale, multi-deadline academic conferences (like NSDI). It details the difference between acceptance, one-shot revision, and rejection, explaining the strategic implications, associated bans, and the required scope for resubmission. Learn how to decode reviewers' feedback, identify critical baselines, and manage your research workflow across multiple cycles.

This skill provides a detailed guide to the requirements and positioning of Science Robotics (Sci Robot), an AAAS journal. It helps authors determine if their robotics research meets the high bar for broad scientific or societal significance. Key areas covered include demonstrated real-world capability, required quantification of physical performance, AAAS submission structure, and best practices for venue selection, making it essential for authors targeting top-tier journals.

This guide outlines the best practices for preparing system artifacts for academic evaluation, following the ACM Artifact Review and Badging scheme. It details how authors must ensure reproducibility, especially for tail latency and cost results, by providing scaled-down testbeds, comprehensive documentation, and clear provenance tracking to satisfy conference evaluators.

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.

This comprehensive guide outlines best practices for achieving high levels of scientific reproducibility in systems papers, especially for cloud measurement studies. It teaches authors how to map every claim in their paper to verifiable evidence, detailing the entire testbed setup, reproducing tail latency and costs accurately, and maintaining consistency between reported numbers and artifacts.