Results for “network”

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Enforces identity-based network access with 802.1X, RADIUS authentication, dynamic VLAN assignment, and endpoint posture assessment using PacketFence.

This skill should be used when the user asks to "set up a web server", "configure HTTP or HTTPS", "perform SNMP enumeration", "configure SMB shares", "test network services", or ne...

Infer gene regulatory networks from gene expression data using scalable algorithms (GRNBoost2, GENIE3) with support for distributed computation.

BlockRun works with Claude Code and Google Antigravity.

Interact with GitHub issues - create, list, and view issues.

Create pull requests following Sentry's engineering practices.

Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.

Train and analyze Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features for mechanistic interpretability research.

Build game systems, implement Unity/Unreal Engine features, and optimize game performance with ECS architecture, physics, multiplayer networking, and shader programming.

Dig reference tool. Use when working with dig in network contexts.

Mtr reference tool. Use when working with mtr in network contexts.

Write efficient C code with proper memory management, pointer

Secure-by-default environment variable management for Claude Code sessions.

Opinionated, evolving constraints to guide agents when building interfaces

Fixes common PyTorch bugs including percentile calculations, LayerNorm for Conv1d, and buffer edge cases in reinforcement learning and neural network code.

This skill enables makepad-skills to self-improve continuously during development.

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Provides reference patterns for Bash on Linux and macOS, including command chaining, file operations, process management, text processing, environment variables, networking, scripting templates, and error handling.

Analyzes malware-generated network traffic from PCAP files to identify C2 protocols, data exfiltration, DNS tunneling, and beaconing patterns using Wireshark, Zeek, Suricata, and Python.

Unified Diary System: A context-preserving automated logger for multi-project development.

Vector-powered CLI for semantic file search with a Claude/Codex skill

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns using the Python netflow library.

Detect and analyze covert communication channels used by malware, including DNS tunneling, ICMP exfiltration, and protocol abuse for C2 and data exfiltration.

Analyze Zeek conn.log and NetFlow data to detect ransomware network indicators including C2 beaconing, TOR exit node connections, data exfiltration, and suspicious DNS patterns.

Implement PCI DSS 4.0.1 compliance controls across all 12 requirements, including scoping, network security, data protection, access controls, monitoring, and governance.

Parse and analyze Cobalt Strike Malleable C2 profiles using dissect.cobaltstrike and pyMalleableC2 to extract C2 indicators, detect evasion techniques, and generate network detection signatures.

Collect volatile forensic evidence from a compromised system following order of volatility, preserving memory, network connections, processes, and system state before they are lost.

LinkedIn automation via browser relay or cookies for messaging, profile viewing, and network actions.

Text-to-speech and speech-to-text using fal.ai audio models

LinkedIn automation via browser relay or cookies for messaging, profile viewing, and network actions.

Monitors a deployed URL for regressions after releases by checking HTTP status, console errors, network failures, performance metrics, content integrity, API health, static assets, and SSE streams.

Detect C2 beaconing patterns in network traffic using frequency analysis, jitter detection, and domain reputation to identify compromised endpoints communicating with adversary infrastructure.

Automates packet capture analysis using tshark and pyshark to extract protocol statistics, detect suspicious flows, identify IOCs, and analyze DNS anomalies from PCAP files.

Centralized 'Truth Engine' for cross-jurisdictional legal context (US, EU, CA) and contract scaffolding.

Builds and trains deep learning models with PyTorch, including tensors, autograd, and neural network modules.